# coding=utf-8
# Copyright 2022 The OpenAI Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Whisper model."""

import math
import os.path
import random
from typing import Optional, Tuple, Union

import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, EncoderDecoderCache, StaticCache
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from dataclasses import dataclass
from transformers.modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    Seq2SeqLMOutput,
    Seq2SeqModelOutput,
    SequenceClassifierOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)
from .configuration_whisper import WhisperVQConfig
from .generation_whisper import WhisperGenerationMixin

if is_flash_attn_2_available():
    from transformers.modeling_flash_attention_utils import _flash_attention_forward

logger = logging.get_logger(__name__)

_HIDDEN_STATES_START_POSITION = 1

_CONFIG_FOR_DOC = "WhisperConfig"
_CHECKPOINT_FOR_DOC = "openai/whisper-tiny"


@dataclass
class QuantizedBaseModelOutput(BaseModelOutput):
    quantized_token_ids: Optional[torch.LongTensor] = None


def vector_quantize(inputs, codebook):
    embedding_size = codebook.size(1)
    inputs_flatten = inputs.reshape(-1, embedding_size)
    codebook_sqr = torch.sum(codebook ** 2, dim=1)
    inputs_sqr = torch.sum(inputs_flatten ** 2, dim=1, keepdim=True)
    # Compute the distances to the codebook
    distances = torch.addmm(codebook_sqr + inputs_sqr,
                            inputs_flatten, codebook.t(), alpha=-2.0, beta=1.0)

    _, indices_flatten = torch.min(distances, dim=1)
    codes_flatten = torch.index_select(codebook, dim=0,
                                       index=indices_flatten)
    codes = codes_flatten.view_as(inputs)
    return codes, indices_flatten, distances


def mse_loss_with_mask(input, target, mask):
    loss = torch.nn.functional.mse_loss(input, target, reduction='none')
    loss = loss.mean(dim=-1)
    loss = loss * mask
    return loss.sum() / mask.sum()


class CausalConv1d(nn.Conv1d):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        bias=True,
        **kwargs
    ):
        super(CausalConv1d, self).__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=0,
            dilation=dilation,
            groups=groups,
            bias=bias,
            **kwargs
        )

        self.left_padding = dilation * (kernel_size - 1)

    def forward(self, inp):
        x = torch.nn.functional.pad(inp.unsqueeze(2), (self.left_padding, 0, 0, 0)).squeeze(2)

        return super(CausalConv1d, self).forward(x)


# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        device: torch.device,
        min_dtype: float,
        cache_position: torch.Tensor,
        batch_size: int,
):
    """
    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
    `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.

    Args:
        attention_mask (`torch.Tensor`):
            A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
        sequence_length (`int`):
            The sequence length being processed.
        target_length (`int`):
            The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
        dtype (`torch.dtype`):
            The dtype to use for the 4D attention mask.
        device (`torch.device`):
            The device to plcae the 4D attention mask on.
        min_dtype (`float`):
            The minimum value representable with the dtype `dtype`.
        cache_position (`torch.Tensor`):
            Indices depicting the position of the input sequence tokens in the sequence.
        batch_size (`torch.Tensor`):
            Batch size.
    """
    if attention_mask is not None and attention_mask.dim() == 4:
        # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
        causal_mask = attention_mask
    else:
        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
        if sequence_length != 1:
            causal_mask = torch.triu(causal_mask, diagonal=1)
        causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
        if attention_mask is not None:
            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
            mask_length = attention_mask.shape[-1]
            padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
            padding_mask = padding_mask == 0
            causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                padding_mask, min_dtype
            )

    return causal_mask


def sinusoids(length: int, channels: int, max_timescale: float = 10000) -> torch.Tensor:
    """Returns sinusoids for positional embedding"""
    if channels % 2 != 0:
        raise ValueError(
            f"Number of channels has to be divisible by 2 for sinusoidal positional embeddings, got {channels} channels."
        )
    log_timescale_increment = math.log(max_timescale) / (channels // 2 - 1)
    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
    scaled_time = torch.arange(length).view(-1, 1) * inv_timescales.view(1, -1)
    return torch.cat([scaled_time.sin(), scaled_time.cos()], dim=1)


# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
    """
    Shift input ids one token to the right.
    """
    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
    shifted_input_ids[:, 0] = decoder_start_token_id

    if pad_token_id is None:
        raise ValueError("self.model.config.pad_token_id has to be defined.")
    # replace possible -100 values in labels by `pad_token_id`
    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

    return shifted_input_ids


# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices
def _compute_mask_indices(
        shape: Tuple[int, int],
        mask_prob: float,
        mask_length: int,
        attention_mask: Optional[torch.LongTensor] = None,
        min_masks: int = 0,
) -> np.ndarray:
    """
    Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for
    ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on
    CPU as part of the preprocessing during training.

    Args:
        shape: The shape for which to compute masks. This should be of a tuple of size 2 where
               the first element is the batch size and the second element is the length of the axis to span.
        mask_prob:  The percentage of the whole axis (between 0 and 1) which will be masked. The number of
                    independently generated mask spans of length `mask_length` is computed by
                    `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the
                    actual percentage will be smaller.
        mask_length: size of the mask
        min_masks: minimum number of masked spans
        attention_mask: A (right-padded) attention mask which independently shortens the feature axis of
                        each batch dimension.
    """
    batch_size, sequence_length = shape

    if mask_length < 1:
        raise ValueError("`mask_length` has to be bigger than 0.")

    if mask_length > sequence_length:
        raise ValueError(
            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}"
            f" and `sequence_length`: {sequence_length}`"
        )

    # epsilon is used for probabilistic rounding
    epsilon = np.random.rand(1).item()

    def compute_num_masked_span(input_length):
        """Given input length, compute how many spans should be masked"""
        num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
        num_masked_span = max(num_masked_span, min_masks)

        # make sure num masked span <= sequence_length
        if num_masked_span * mask_length > sequence_length:
            num_masked_span = sequence_length // mask_length

        # make sure num_masked span is also <= input_length - (mask_length - 1)
        if input_length - (mask_length - 1) < num_masked_span:
            num_masked_span = max(input_length - (mask_length - 1), 0)

        return num_masked_span

    # compute number of masked spans in batch
    input_lengths = (
        attention_mask.sum(-1).detach().tolist()
        if attention_mask is not None
        else [sequence_length for _ in range(batch_size)]
    )

    # SpecAugment mask to fill
    spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
    spec_aug_mask_idxs = []

    max_num_masked_span = compute_num_masked_span(sequence_length)

    if max_num_masked_span == 0:
        return spec_aug_mask

    for input_length in input_lengths:
        # compute num of masked spans for this input
        num_masked_span = compute_num_masked_span(input_length)

        # get random indices to mask
        spec_aug_mask_idx = np.random.choice(
            np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False
        )

        # pick first sampled index that will serve as a dummy index to pad vector
        # to ensure same dimension for all batches due to probabilistic rounding
        # Picking first sample just pads those vectors twice.
        if len(spec_aug_mask_idx) == 0:
            # this case can only happen if `input_length` is strictly smaller then
            # `sequence_length` in which case the last token has to be a padding
            # token which we can use as a dummy mask id
            dummy_mask_idx = sequence_length - 1
        else:
            dummy_mask_idx = spec_aug_mask_idx[0]

        spec_aug_mask_idx = np.concatenate(
            [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
        )
        spec_aug_mask_idxs.append(spec_aug_mask_idx)

    spec_aug_mask_idxs = np.array(spec_aug_mask_idxs)

    # expand masked indices to masked spans
    spec_aug_mask_idxs = np.broadcast_to(
        spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length)
    )
    spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length)

    # add offset to the starting indexes so that indexes now create a span
    offsets = np.arange(mask_length)[None, None, :]
    offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape(
        batch_size, max_num_masked_span * mask_length
    )
    spec_aug_mask_idxs = spec_aug_mask_idxs + offsets

    # ensure that we cannot have indices larger than sequence_length
    if spec_aug_mask_idxs.max() > sequence_length - 1:
        spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1

    # scatter indices to mask
    np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)

    return spec_aug_mask


class WhisperPositionalEmbedding(nn.Embedding):
    def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
        super().__init__(num_positions, embedding_dim)

    def forward(self, input_ids, past_key_values_length=0, position_ids=None):
        if position_ids is None:
            return self.weight[past_key_values_length: past_key_values_length + input_ids.shape[1]]
        else:
            return self.weight[position_ids]


class WhisperAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
            self,
            embed_dim: int,
            num_heads: int,
            dropout: float = 0.0,
            is_decoder: bool = False,
            bias: bool = True,
            is_causal: bool = False,
            layer_idx: Optional[int] = None,
            config: Optional[WhisperVQConfig] = None,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        self.config = config

        if (self.head_dim * num_heads) != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                f" and `num_heads`: {num_heads})."
            )
        self.scaling = self.head_dim ** -0.5
        self.is_decoder = is_decoder
        self.is_causal = is_causal

        if layer_idx is None and is_decoder:
            logger.warning_once(
                f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
                "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )
        self.layer_idx = layer_idx

        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

    # Copied from transformers.models.bart.modeling_bart.BartAttention._shape with BART->whisper
    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
            self,
            hidden_states: torch.Tensor,
            key_value_states: Optional[torch.Tensor] = None,
            past_key_value: Optional[EncoderDecoderCache] = None,
            attention_mask: Optional[torch.Tensor] = None,
            layer_head_mask: Optional[torch.Tensor] = None,
            output_attentions: bool = False,
            cache_position: Optional[torch.LongTensor] = None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""

        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        bsz, tgt_len, _ = hidden_states.size()

        # get query proj
        query_states = self._shape(self.q_proj(hidden_states) * self.scaling, tgt_len, bsz)

        if past_key_value is not None:
            is_updated = past_key_value.is_updated.get(self.layer_idx)
            if is_cross_attention:
                # after the first generated id, we can subsequently re-use all key/value_states from cache
                past_key_value.is_updated[self.layer_idx] = True
                past_key_value = past_key_value.cross_attention_cache
            else:
                past_key_value = past_key_value.self_attention_cache

        # use key_value_states if cross attention
        current_states = key_value_states if key_value_states is not None else hidden_states
        if is_cross_attention and past_key_value and is_updated:
            # reuse k,v, cross_attentions
            key_states = past_key_value.key_cache[self.layer_idx]
            value_states = past_key_value.value_cache[self.layer_idx]
        else:
            key_states = self._shape(self.k_proj(current_states), -1, bsz)
            value_states = self._shape(self.v_proj(current_states), -1, bsz)
            if past_key_value is not None:
                # save all key/value_states to cache to be re-used for fast auto-regressive generation
                cache_position = cache_position if not is_cross_attention else None
                key_states, value_states = past_key_value.update(
                    key_states, value_states, self.layer_idx, {"cache_position": cache_position}
                )

        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))

        if attention_mask is not None:  # no matter the length, we just slice it
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
            attn_weights = attn_weights + causal_mask

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        if layer_head_mask is not None:
            if layer_head_mask.size() != (self.num_heads,):
                raise ValueError(
                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
                    f" {layer_head_mask.size()}"
                )
            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights

        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
        attn_output = torch.matmul(attn_probs, value_states)

        if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2)
        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
        # partitioned across GPUs when using tensor-parallelism.
        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights, past_key_value


class WhisperFlashAttention2(WhisperAttention):
    """
    Whisper flash attention module. This module inherits from `WhisperAttention` as the weights of the module stays
    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
    flash attention and deal with padding tokens in case the input contains any of them.
    """

    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

    def forward(
            self,
            hidden_states: torch.Tensor,
            key_value_states: Optional[torch.Tensor] = None,
            past_key_value: Optional[EncoderDecoderCache] = None,
            attention_mask: Optional[torch.Tensor] = None,
            layer_head_mask: Optional[torch.Tensor] = None,
            output_attentions: bool = False,
            cache_position: Optional[torch.LongTensor] = None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if isinstance(past_key_value, StaticCache):
            raise ValueError(
                "The `static` cache implementation is not compatible with `attn_implementation='flash_attention_2'`. "
                "Use `attn_implementation='sdpa'` in the meantime, and open an issue at https://github.com/huggingface/transformers"
            )
        # WhisperFlashAttention2 attention does not support output_attentions
        if output_attentions:
            raise ValueError("WhisperFlashAttention2 attention does not support output_attentions")

        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        bsz, tgt_len, _ = hidden_states.size()

        # get query proj
        query_states = torch.reshape(self.q_proj(hidden_states), (bsz, tgt_len, self.num_heads, self.head_dim))

        if past_key_value is not None:
            is_updated = past_key_value.is_updated.get(self.layer_idx)
            if is_cross_attention:
                # after the first generated id, we can subsequently re-use all key/value_states from cache
                past_key_value.is_updated[self.layer_idx] = True
                past_key_value = past_key_value.cross_attention_cache
            else:
                past_key_value = past_key_value.self_attention_cache

        # use key_value_states if cross attention
        current_states = key_value_states if key_value_states is not None else hidden_states
        if is_cross_attention and past_key_value and is_updated:
            # reuse k,v, cross_attentions
            key_states = past_key_value.key_cache[self.layer_idx]
            value_states = past_key_value.value_cache[self.layer_idx]
        else:
            key_states = self._shape(self.k_proj(current_states), -1, bsz)
            value_states = self._shape(self.v_proj(current_states), -1, bsz)
            if past_key_value is not None:
                # save all key/value_states to cache to be re-used for fast auto-regressive generation
                cache_position = cache_position if not is_cross_attention else None
                key_states, value_states = past_key_value.update(
                    key_states, value_states, self.layer_idx, {"cache_position": cache_position}
                )

        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]
        #  We would need to refactor the KV cache to be able to avoid many of these transpose/reshape/view.
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        causal_mask = attention_mask
        if attention_mask is not None:  # no matter the length, we just slice it
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]

        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
        # therefore the input hidden states gets silently casted in float32. Hence, we need
        # cast them back in the correct dtype just to be sure everything works as expected.
        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
        # in fp32. (LlamaRMSNorm handles it correctly)

        input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            if torch.is_autocast_enabled():
                target_dtype = torch.get_autocast_gpu_dtype()
            # Handle the case where the model is quantized
            elif hasattr(self.config, "_pre_quantization_dtype"):
                target_dtype = self.config._pre_quantization_dtype
            else:
                target_dtype = self.q_proj.weight.dtype

            logger.warning_once(
                f"The input hidden states seems to be silently casted in float32, this might be related to"
                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
                f" {target_dtype}."
            )

            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)

        attn_output = _flash_attention_forward(
            query_states,
            key_states,
            value_states,
            causal_mask,
            tgt_len,
            dropout=self.dropout,
            is_causal=self.is_causal,
            use_top_left_mask=self._flash_attn_uses_top_left_mask,
        )

        attn_output = attn_output.reshape(bsz, tgt_len, -1)
        attn_output = self.out_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class WhisperSdpaAttention(WhisperAttention):
    def forward(
            self,
            hidden_states: torch.Tensor,
            key_value_states: Optional[torch.Tensor] = None,
            past_key_value: Optional[EncoderDecoderCache] = None,
            attention_mask: Optional[torch.Tensor] = None,
            layer_head_mask: Optional[torch.Tensor] = None,
            output_attentions: bool = False,
            cache_position: Optional[torch.LongTensor] = None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""
        if output_attentions or layer_head_mask is not None:
            # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once this is implemented.
            logger.warning_once(
                "WhisperModel is using WhisperSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention"
                ' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
            )
            return super().forward(
                hidden_states,
                key_value_states=key_value_states,
                past_key_value=past_key_value,
                attention_mask=attention_mask,
                layer_head_mask=layer_head_mask,
                output_attentions=output_attentions,
                cache_position=cache_position,
            )

        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        bsz, tgt_len, _ = hidden_states.size()

        # get query proj
        query_states = self._shape(self.q_proj(hidden_states), tgt_len, bsz)

        if past_key_value is not None:
            is_updated = past_key_value.is_updated.get(self.layer_idx)
            if is_cross_attention:
                # after the first generated id, we can subsequently re-use all key/value_states from cache
                past_key_value.is_updated[self.layer_idx] = True
                past_key_value = past_key_value.cross_attention_cache
            else:
                past_key_value = past_key_value.self_attention_cache

        # use key_value_states if cross attention
        current_states = key_value_states if key_value_states is not None else hidden_states
        if is_cross_attention and past_key_value and is_updated:
            # reuse k,v, cross_attentions
            key_states = past_key_value.key_cache[self.layer_idx]
            value_states = past_key_value.value_cache[self.layer_idx]
        else:
            key_states = self._shape(self.k_proj(current_states), -1, bsz)
            value_states = self._shape(self.v_proj(current_states), -1, bsz)
            if past_key_value is not None:
                # save all key/value_states to cache to be re-used for fast auto-regressive generation
                cache_position = cache_position if not is_cross_attention else None
                key_states, value_states = past_key_value.update(
                    key_states, value_states, self.layer_idx, {"cache_position": cache_position}
                )

        causal_mask = attention_mask
        if attention_mask is not None:  # no matter the length, we just slice it
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]

        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
        # The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case tgt_len == 1.
        is_causal = True if self.is_causal and causal_mask is None and tgt_len > 1 else False

        # NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask,
        # but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
        attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_states,
            key_states,
            value_states,
            attn_mask=causal_mask,
            dropout_p=self.dropout if self.training else 0.0,
            is_causal=is_causal,
        )

        if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2)

        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
        # partitioned across GPUs when using tensor-parallelism.
        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, None, past_key_value


WHISPER_ATTENTION_CLASSES = {
    "eager": WhisperAttention,
    # "flash_attention_2": WhisperFlashAttention2,
    "sdpa": WhisperSdpaAttention,
}


# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->Whisper, MBART->WHISPER
class WhisperVQEncoderLayer(nn.Module):
    def __init__(self, config: WhisperVQConfig, is_causal=False):
        super().__init__()
        self.embed_dim = config.d_model

        self.self_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation](
            embed_dim=self.embed_dim,
            num_heads=config.encoder_attention_heads,
            dropout=config.attention_dropout,
            config=config,
            is_causal=is_causal
        )
        self.is_causal = is_causal
        if self.is_causal:
            assert isinstance(self.self_attn, WhisperSdpaAttention), "Causal attention is only supported for SDPA"
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(
            self,
            hidden_states: torch.Tensor,
            attention_mask: torch.Tensor,
            layer_head_mask: torch.Tensor,
            output_attentions: bool = False,
    ) -> torch.Tensor:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(encoder_attention_heads,)`.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, attn_weights, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask if not self.is_causal else None,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        if hidden_states.dtype == torch.float16 and (
                torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
        ):
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class WhisperDecoderLayer(nn.Module):
    def __init__(self, config: WhisperVQConfig, layer_idx: int = None):
        super().__init__()
        self.embed_dim = config.d_model

        self.self_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation](
            embed_dim=self.embed_dim,
            num_heads=config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
            is_causal=True,
            layer_idx=layer_idx,
            config=config,
        )
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout

        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.encoder_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation](
            self.embed_dim,
            config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
            layer_idx=layer_idx,
            config=config,
        )
        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(
            self,
            hidden_states: torch.Tensor,
            attention_mask: Optional[torch.Tensor] = None,
            encoder_hidden_states: Optional[torch.Tensor] = None,
            encoder_attention_mask: Optional[torch.Tensor] = None,
            layer_head_mask: Optional[torch.Tensor] = None,
            cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
            past_key_value: Optional[EncoderDecoderCache] = None,
            output_attentions: Optional[bool] = False,
            use_cache: Optional[bool] = True,
            cache_position: Optional[torch.LongTensor] = None,
    ) -> torch.Tensor:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(encoder_attention_heads,)`.
            cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
                size `(decoder_attention_heads,)`.
            past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            past_key_value=past_key_value,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
            cache_position=cache_position,
        )
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        # Cross-Attention Block
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)
            hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
                hidden_states=hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                layer_head_mask=cross_attn_layer_head_mask,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
            )
            hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
            hidden_states = residual + hidden_states

            # add cross-attn to positions 1 of present_key_value tuple
            present_key_value = (present_key_value, cross_attn_present_key_value)

        # Fully Connected
        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


class WhisperPreTrainedModel(PreTrainedModel):
    config_class = WhisperVQConfig
    base_model_prefix = "model"
    main_input_name = "input_features"
    supports_gradient_checkpointing = True
    _no_split_modules = ["WhisperEncoderLayer", "WhisperDecoderLayer"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True
    _supports_static_cache = True

    def _init_weights(self, module):
        std = self.config.init_std
        if isinstance(module, (nn.Linear, nn.Conv1d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, WhisperVQEncoder):
            with torch.no_grad():
                embed_positions = module.embed_positions.weight
                embed_positions.copy_(sinusoids(*embed_positions.shape))

    def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
        """
        Computes the output length of the convolutional layers
        """
        input_lengths = (input_lengths - 1) // 2 + 1

        return input_lengths


WHISPER_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`WhisperConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

WHISPER_INPUTS_DOCSTRING = r"""
    Args:
        input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`):
            Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
            loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
            tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
        attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing *SpecAugment* data augmentation on padding token indices. Mask values selected in
            `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.

            Indices can be obtained using [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are decoder input IDs?](../glossary#decoder-input-ids)

            Whisper uses the `decoder_start_token_id` as the starting token for `decoder_input_ids` generation. If
            `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).
        decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.

            If you want to change padding behavior, you should read
            [`modeling_whisper._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the BART
            paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`EncoderDecoderCache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
            Pre-computed hidden-states that can be used to speed up auto-regressive (sequential) decoding. There are
            four sets of pre-computed hidden-states: key and values states in the self-attention blocks (2) and
            in the cross-attention blocks (2). The `past_key_values` are returned when `use_cache=True` is passed or
            when `config.use_cache=True`

            Two formats are allowed:
            - An [`~cache_utils.EncoderDecoderCache`] instance;
            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
            input (see `past_key_values`). This is useful if you want more control over how to convert
            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
            Indices depicting the position of the input sequence tokens in the sequence. It is used to update the cache
            in the correct position and to infer the complete sequence length.
"""

WHISPER_ENCODER_INPUTS_DOCSTRING = r"""
    Args:
        input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`):
            Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
            loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
            tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
            hidden-states at the output of the last layer of the encoder.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


class WhisperVQEncoder(WhisperPreTrainedModel):
    """
    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
    [`WhisperEncoderLayer`].

    Args:
        config: WhisperConfig
    """

    def __init__(self, config: WhisperVQConfig):
        super().__init__(config)
        self.config = config
        self.dropout = config.dropout
        self.layerdrop = config.encoder_layerdrop

        embed_dim = config.d_model
        self.num_mel_bins = config.num_mel_bins
        self.padding_idx = config.pad_token_id
        self.max_source_positions = config.max_source_positions
        self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
        if config.encoder_causal_convolution:
            conv_class = CausalConv1d
        else:
            conv_class = nn.Conv1d
        self.conv1 = conv_class(self.num_mel_bins, embed_dim, kernel_size=3, padding=1)
        self.conv2 = conv_class(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1)

        self.embed_positions = nn.Embedding(self.max_source_positions, embed_dim)
        self.embed_positions.requires_grad_(False)
        if config.quantize_encoder_only:
            self.layers = nn.ModuleList([WhisperVQEncoderLayer(config,
                                                               is_causal=config.encoder_causal_attention or config.quantize_causal_encoder)
                                         for _ in range(config.quantize_position)])
        else:
            self.layers = nn.ModuleList([WhisperVQEncoderLayer(config, is_causal=config.encoder_causal_attention or (
                        config.quantize_causal_encoder and layer_id < config.quantize_position)) for layer_id in
                                         range(config.encoder_layers)])
            self.layer_norm = nn.LayerNorm(config.d_model)

        self.gradient_checkpointing = False
        # Parameters related to pooling layer
        self.pooling_layer = None
        # Parameters related to quantization layer
        self.codebook = None
        self.embed_positions2 = None
        self.quantize_loss = None
        self.num_active_codes = None
        self.quantize_ema_count = 0
        # Save hiddens
        self.save_hidden_dir = None
        self.save_hidden_position = None
        # Initialize weights and apply final processing
        self.init_pooling_layer(config)
        self.init_quantize_layer(config)
        self.post_init()

    def init_pooling_layer(self, config: WhisperVQConfig):
        if config.pooling_kernel_size is not None:
            if config.pooling_type == "max":
                self.pooling_layer = nn.MaxPool1d(kernel_size=config.pooling_kernel_size)
            elif config.pooling_type == "avg":
                self.pooling_layer = nn.AvgPool1d(kernel_size=config.pooling_kernel_size)
            else:
                raise NotImplementedError(f"Pooling type {config.pooling_type} not implemented")

    def init_quantize_layer(self, config: WhisperVQConfig, quantize_load_codebook=None):
        if config.quantize_vocab_size is not None:
            if config.pooling_position is not None:
                assert config.quantize_position >= config.pooling_position
            self.codebook = nn.Embedding(config.quantize_vocab_size, self.config.d_model)
            if quantize_load_codebook is not None:
                init_codes = np.load(quantize_load_codebook)
                self.codebook.weight.data.copy_(torch.from_numpy(init_codes))
            max_source_positions = self.max_source_positions
            if config.pooling_kernel_size is not None:
                max_source_positions = math.ceil(max_source_positions / self.config.pooling_kernel_size)
            self.embed_positions2 = nn.Embedding(max_source_positions, self.config.d_model)
            self.embed_positions2.weight.data.copy_(self.embed_positions.weight.data[:max_source_positions])
            if config.quantize_ema_decay is not None:
                self.codebook.weight.requires_grad = False
                self.register_buffer("ema_count", torch.ones(config.quantize_vocab_size, dtype=torch.float))
                self.register_buffer("ema_weight", self.codebook.weight.data.clone().float())

    def _freeze_parameters(self):
        for param in self.parameters():
            param.requires_grad = False
        self._requires_grad = False

    def get_input_embeddings(self) -> nn.Module:
        return self.conv1

    def set_input_embeddings(self, value: nn.Module):
        self.conv1 = value

    def get_block_causal_attention_mask(self, attention_mask, block_size=50):
        dtype = self.dtype
        batch_size, seq_length = attention_mask.shape
        causal_mask = torch.torch.tril(
            torch.ones(1, seq_length, seq_length, dtype=torch.bool, device=attention_mask.device))
        block_square_mask = []
        for start in range(0, seq_length, block_size):
            end = min(start + block_size, seq_length)
            length = end - start
            block_square_mask.append(causal_mask.new_ones((length, length)))
        block_square_mask = torch.block_diag(*block_square_mask)
        block_causal_mask = causal_mask | block_square_mask
        block_causal_mask = block_causal_mask & attention_mask[:, None, :]
        block_causal_mask = block_causal_mask.to(dtype=dtype)  # fp16 compatibility
        block_causal_mask = (1.0 - block_causal_mask) * torch.finfo(dtype).min
        block_causal_mask = block_causal_mask.unsqueeze(1)
        return block_causal_mask

    def forward(
            self,
            input_features,
            attention_mask=None,
            head_mask=None,
            output_attentions=None,
            output_hidden_states=None,
            return_dict=None,
            quantized_token_ids=None
    ):
        r"""
        Args:
            input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
                Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
                obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a
                `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
                `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
                and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
            attention_mask (`torch.Tensor`)`, *optional*):
                Whisper does not support masking of the `input_features`, this argument is preserved for compatibility,
                but it is not used. By default the silence in the input log mel spectrogram are ignored.
            head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """

        # expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0]
        # if input_features.shape[-1] != expected_seq_length:
        #     raise ValueError(
        #         f"Whisper expects the mel input features to be of length {expected_seq_length}, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
        #     )

        batch_size, feature_size, seq_length = input_features.shape
        seq_length = seq_length // (self.conv1.stride[0] * self.conv2.stride[0])

        attention_mask = attention_mask[:, :: self.conv1.stride[0] * self.conv2.stride[0]]
        if self.config.quantize_causal_block_size is not None:
            extended_attention_mask = self.get_block_causal_attention_mask(attention_mask,
                                                                           block_size=self.config.quantize_causal_block_size)
        else:
            extended_attention_mask = self.get_extended_attention_mask(attention_mask, (batch_size, seq_length))
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        inputs_embeds = nn.functional.gelu(self.conv1(input_features))
        inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))

        inputs_embeds = inputs_embeds.permute(0, 2, 1)
        embed_pos = self.embed_positions.weight

        hidden_states = inputs_embeds + embed_pos[:seq_length]
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        assert attention_mask.shape[-1] == hidden_states.shape[1]
        # check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            assert head_mask.size()[0] == (
                len(self.layers)
            ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            to_drop = False
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop:  # skip the layer
                    to_drop = True

            if to_drop:
                layer_outputs = (None, None)
            else:
                if self.gradient_checkpointing and self.training:
                    layer_outputs = self._gradient_checkpointing_func(
                        encoder_layer.__call__,
                        hidden_states,
                        extended_attention_mask,
                        (head_mask[idx] if head_mask is not None else None),
                        output_attentions,
                    )
                else:
                    layer_outputs = encoder_layer(
                        hidden_states,
                        extended_attention_mask,
                        layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                        output_attentions=output_attentions,
                    )

                hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)
            if idx + 1 == self.config.pooling_position and self.config.pooling_kernel_size is not None:
                hidden_states = hidden_states.permute(0, 2, 1)
                if hidden_states.shape[-1] % self.config.pooling_kernel_size != 0:
                    hidden_states = torch.nn.functional.pad(hidden_states, (
                    0, self.config.pooling_kernel_size - hidden_states.shape[-1] % self.config.pooling_kernel_size))
                hidden_states = self.pooling_layer(hidden_states).permute(0, 2, 1)
                attention_mask = attention_mask[:, ::self.config.pooling_kernel_size]
                if self.config.quantize_causal_block_size is not None:
                    extended_attention_mask = self.get_block_causal_attention_mask(attention_mask, block_size=self.config.quantize_causal_block_size // self.config.pooling_kernel_size)
                else:
                    extended_attention_mask = self.get_extended_attention_mask(attention_mask, (
                    batch_size, seq_length // self.config.pooling_kernel_size))

            if idx + 1 == self.config.quantize_position and self.config.quantize_vocab_size is not None:
                if quantized_token_ids is not None:
                    hidden_states = self.codebook(quantized_token_ids)
                else:
                    hidden_quantized, indices_flat, distances = vector_quantize(hidden_states, self.codebook.weight)
                    quantized_token_ids = indices_flat.reshape(batch_size, hidden_quantized.shape[1])
                    if self.training:
                        encodings = torch.nn.functional.one_hot(indices_flat, self.config.quantize_vocab_size).float()
                        encodings = encodings * attention_mask.reshape(-1, 1)
                        n = torch.sum(encodings, dim=0)
                        torch.distributed.all_reduce(n, op=torch.distributed.ReduceOp.SUM)
                        self.num_active_codes = n.nonzero().shape[0]
                        if self.config.quantize_ema_decay:
                            hidden_flat = hidden_states.detach().float().reshape(-1, hidden_states.shape[-1])
                            with torch.autocast(device_type='cuda', dtype=torch.float32):
                                dw = torch.matmul(encodings.t(), hidden_flat)
                            torch.distributed.all_reduce(dw, op=torch.distributed.ReduceOp.SUM)
                            self.ema_count = self.ema_count * self.config.quantize_ema_decay + (
                                    1 - self.config.quantize_ema_decay) * n
                            total_count = torch.sum(self.ema_count)
                            self.ema_count = (self.ema_count + 1e-5) / (
                                    total_count + self.config.quantize_vocab_size * 1e-5) * total_count
                            self.ema_weight = self.ema_weight * self.config.quantize_ema_decay + (
                                    1 - self.config.quantize_ema_decay) * dw
                            self.codebook.weight.data = self.ema_weight / self.ema_count.unsqueeze(1)
                            self.quantize_loss = self.config.quantize_loss_scale * self.config.quantize_commit_coefficient * mse_loss_with_mask(
                                hidden_states, hidden_quantized.detach(), attention_mask)
                            self.quantize_ema_count += 1
                            if self.config.quantize_restart_interval is not None and self.quantize_ema_count % self.config.quantize_restart_interval == 0:
                                rank, world_size = torch.distributed.get_rank(), torch.distributed.get_world_size()
                                segment_vocab_size = self.config.quantize_vocab_size // world_size
                                start_idx = segment_vocab_size * rank
                                ema_count_segment = self.ema_count[start_idx: start_idx + segment_vocab_size]
                                threshold = 1 * (
                                            self.config.quantize_ema_decay ** self.config.quantize_restart_interval)
                                update_indices = (ema_count_segment < threshold).nonzero()[:, 0] + start_idx
                                num_update = update_indices.shape[0]
                                mask_flat = attention_mask.reshape(-1) > 0
                                hidden_selected = hidden_flat[mask_flat]
                                hidden_update = hidden_selected[random.sample(range(len(hidden_selected)), num_update)]
                                num_update = torch.as_tensor([num_update], dtype=torch.long,
                                                             device=hidden_states.device)
                                num_update_list = [torch.as_tensor([0], dtype=torch.long, device=hidden_states.device)
                                                   for _
                                                   in range(world_size)]
                                torch.distributed.all_gather(num_update_list, num_update)
                                update_indices_list = [
                                    torch.zeros(num.item(), dtype=torch.long, device=hidden_states.device) for num in
                                    num_update_list]
                                torch.distributed.all_gather(update_indices_list, update_indices)
                                update_indices = torch.cat(update_indices_list)
                                hidden_update_list = [
                                    torch.zeros(num.item(), hidden_flat.shape[-1], dtype=hidden_update.dtype,
                                                device=hidden_states.device) for num in num_update_list]
                                torch.distributed.all_gather(hidden_update_list, hidden_update)
                                hidden_update = torch.cat(hidden_update_list)
                                self.codebook.weight.data[update_indices] = hidden_update
                                self.ema_count[update_indices] = 1
                                self.ema_weight[update_indices] = hidden_update
                                if torch.distributed.get_rank() == 0:
                                    print(f"restart {len(update_indices)} tokens")
                        else:
                            loss = self.config.quantize_loss_scale * (
                                    self.config.quantize_commit_coefficient * mse_loss_with_mask(hidden_states,
                                                                                                 hidden_quantized.detach(),
                                                                                                 attention_mask) + mse_loss_with_mask(
                                hidden_quantized, hidden_states.detach(), attention_mask))
                            self.quantize_loss = loss
                        hidden_states = hidden_states + (hidden_quantized - hidden_states).detach()
                    else:
                        hidden_states = hidden_quantized
                hidden_states = hidden_states + self.embed_positions2.weight[:hidden_states.shape[1]]

            if idx + 1 == self.save_hidden_position:
                import numpy as np
                import uuid
                to_save = []
                for batch_idx, hidden_state in enumerate(hidden_states):
                    for seq_idx, hidden in enumerate(hidden_state):
                        if attention_mask[batch_idx, seq_idx]:
                            to_save.append(hidden.detach().cpu().numpy())
                np.save(os.path.join(self.save_hidden_dir, f"{str(uuid.uuid4())}.npy"), to_save)
        if not self.config.quantize_encoder_only:
            hidden_states = self.layer_norm(hidden_states)
        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
        return QuantizedBaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions,
            quantized_token_ids=quantized_token_ids,
        )


class WhisperVQDecoder(WhisperPreTrainedModel):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`WhisperDecoderLayer`]

    Args:
        config: WhisperConfig
    """

    main_input_name = "input_ids"

    def __init__(self, config: WhisperVQConfig):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.decoder_layerdrop
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_target_positions
        self.max_source_positions = config.max_source_positions
        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0

        self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
        self.embed_positions = WhisperPositionalEmbedding(self.max_target_positions, config.d_model)

        self.layers = nn.ModuleList(
            [WhisperDecoderLayer(config, layer_idx) for layer_idx in range(config.decoder_layers)]
        )
        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
        self._use_sdpa = config._attn_implementation == "sdpa"

        self.layer_norm = nn.LayerNorm(config.d_model)

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    def forward(
            self,
            input_ids=None,
            attention_mask=None,
            encoder_hidden_states=None,
            encoder_attention_mask=None,
            head_mask=None,
            cross_attn_head_mask=None,
            past_key_values=None,
            inputs_embeds=None,
            position_ids=None,
            use_cache=None,
            output_attentions=None,
            output_hidden_states=None,
            return_dict=None,
            cache_position=None,
    ):
        r"""
        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

                Indices can be obtained using [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and
                [`PreTrainedTokenizer.__call__`] for details.

                [What are input IDs?](../glossary#input-ids)
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                [What are attention masks?](../glossary#attention-mask)
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                of the decoder.]
            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
            head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

            cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention
                on hidden heads. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

            past_key_values (`EncoderDecoderCache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
                Pre-computed hidden-states that can be used to speed up auto-regressive (sequential) decoding. There are
                four sets of pre-computed hidden-states: key and values states in the self-attention blocks (2) and
                in the cross-attention blocks (2). The `past_key_values` are returned when `use_cache=True` is passed or
                when `config.use_cache=True`

                Two formats are allowed:
                - An [`~cache_utils.EncoderDecoderCache`] instance;
                - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
                `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
            inputs_embeds (`torch.FloatTensor` of
                shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
                `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
                control over how to convert `input_ids` indices into associated vectors than the model's internal
                embedding lookup matrix.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
                Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
                cache in the correct position and to infer the complete sequence length.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
        assert encoder_attention_mask.shape[-1] == encoder_hidden_states.shape[1]
        encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)

        return_legacy_cache = False
        return_self_attention_cache = False
        if use_cache or past_key_values is not None:
            if isinstance(past_key_values, Cache) and not isinstance(past_key_values, EncoderDecoderCache):
                return_self_attention_cache = True
                past_key_values = EncoderDecoderCache(past_key_values, DynamicCache())
            elif not isinstance(past_key_values, EncoderDecoderCache):
                return_legacy_cache = True
                logger.warning_once(
                    "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.43.0. "
                    "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
                    "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
                )
                past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)

        past_key_values_length = 0
        if cache_position is not None:
            past_key_values_length = cache_position[0]
        elif past_key_values is not None:
            past_key_values_length = past_key_values.get_seq_length()

        if cache_position is None:
            cache_position = torch.arange(
                past_key_values_length, past_key_values_length + input_shape[1], device=inputs_embeds.device
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        # embed positions
        if input_ids is not None:
            positions = self.embed_positions(
                input_ids, past_key_values_length=past_key_values_length, position_ids=position_ids
            )
        else:
            positions = self.embed_positions(
                inputs_embeds, past_key_values_length=past_key_values_length, position_ids=position_ids
            )

        hidden_states = inputs_embeds + positions.to(inputs_embeds.device)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        causal_mask = self._update_causal_mask(
            attention_mask,
            inputs_embeds,
            cache_position,
            past_key_values.self_attention_cache if past_key_values is not None else None,
            output_attentions,
        )

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache = True` is incompatible with gradient checkpointing. Setting `use_cache = False`..."
                )
                use_cache = False
        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None

        # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
            if attn_mask is not None:
                assert attn_mask.size()[0] == (len(self.layers)), (
                    f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
                    f" {head_mask.size()[0]}."
                )
        for idx, decoder_layer in enumerate(self.layers):
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop:
                    continue

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    causal_mask,
                    encoder_hidden_states,
                    encoder_extended_attention_mask,  # encoder attention mask
                    head_mask[idx] if head_mask is not None else None,
                    cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
                    None,  # past_key_value
                    output_attentions,
                    use_cache,
                    cache_position,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_extended_attention_mask,
                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                    cross_attn_layer_head_mask=(
                        cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
                    ),
                    past_key_value=past_key_values if use_cache else None,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
                )
            hidden_states = layer_outputs[0]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        hidden_states = self.layer_norm(hidden_states)
        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = past_key_values if use_cache else None
        if return_self_attention_cache:
            next_cache = past_key_values.self_attention_cache
        if return_legacy_cache:
            next_cache = past_key_values.to_legacy_cache()
        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
                if v is not None
            )
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            cross_attentions=all_cross_attentions,
        )

    # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
    def _update_causal_mask(
            self,
            attention_mask: torch.Tensor,
            input_tensor: torch.Tensor,
            cache_position: torch.Tensor,
            past_key_values: Cache,
            output_attentions: bool,
    ):
        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114

        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and 0.0 in attention_mask:
                return attention_mask
            return None

        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
        # to infer the attention mask.
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_static_cache = isinstance(past_key_values, StaticCache)

        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                    attention_mask,
                    inputs_embeds=input_tensor,
                    past_key_values_length=past_seen_tokens,
                    is_training=self.training,
            ):
                return None

        dtype, device = input_tensor.dtype, input_tensor.device
        min_dtype = torch.finfo(dtype).min
        sequence_length = input_tensor.shape[1]
        if using_static_cache:
            target_length = past_key_values.get_max_length()
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, torch.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
        causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
            attention_mask,
            sequence_length=sequence_length,
            target_length=target_length,
            dtype=dtype,
            device=device,
            min_dtype=min_dtype,
            cache_position=cache_position,
            batch_size=input_tensor.shape[0],
        )

        if (
                self.config._attn_implementation == "sdpa"
                and attention_mask is not None
                and attention_mask.device.type == "cuda"
                and not output_attentions
        ):
            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
            # Details: https://github.com/pytorch/pytorch/issues/110213
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask


@add_start_docstrings(
    "The bare Whisper Model outputting raw hidden-states without any specific head on top.",
    WHISPER_START_DOCSTRING,
)
class WhisperVQModel(WhisperPreTrainedModel):
    def __init__(self, config: WhisperVQConfig):
        super().__init__(config)

        self.encoder = WhisperVQEncoder(config)
        self.decoder = WhisperVQDecoder(config)
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.decoder.embed_tokens

    def set_input_embeddings(self, value):
        self.decoder.embed_tokens = value

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def freeze_encoder(self):
        """
        Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will
        not be updated during training.
        """
        self.encoder._freeze_parameters()

    def _mask_input_features(
            self,
            input_features: torch.FloatTensor,
            attention_mask: Optional[torch.LongTensor] = None,
    ):
        """
        Masks extracted features along time axis and/or along feature axis according to
        [SpecAugment](https://arxiv.org/abs/1904.08779).
        """

        # `config.apply_spec_augment` can set masking to False
        if not getattr(self.config, "apply_spec_augment", True):
            return input_features

        # generate indices & apply SpecAugment along time axis
        batch_size, hidden_size, sequence_length = input_features.size()

        if self.config.mask_time_prob > 0 and self.training:
            # generate indices & apply SpecAugment along time axis
            mask_time_indices = _compute_mask_indices(
                (batch_size, sequence_length),
                mask_prob=self.config.mask_time_prob,
                mask_length=self.config.mask_time_length,
                attention_mask=attention_mask,
                min_masks=self.config.mask_time_min_masks,
            )
            mask_time_indices = torch.tensor(mask_time_indices, device=input_features.device, dtype=torch.bool)
            mask_time_indices = mask_time_indices[:, None].expand(-1, hidden_size, -1)
            input_features[mask_time_indices] = 0

        if self.config.mask_feature_prob > 0 and self.training:
            # generate indices & apply SpecAugment along feature axis
            mask_feature_indices = _compute_mask_indices(
                (batch_size, hidden_size),
                mask_prob=self.config.mask_feature_prob,
                mask_length=self.config.mask_feature_length,
                min_masks=self.config.mask_feature_min_masks,
            )
            mask_feature_indices = torch.tensor(mask_feature_indices, device=input_features.device, dtype=torch.bool)
            input_features[mask_feature_indices] = 0

        return input_features

    @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
            self,
            input_features: Optional[torch.FloatTensor] = None,
            attention_mask: Optional[torch.LongTensor] = None,
            decoder_input_ids: Optional[torch.LongTensor] = None,
            decoder_attention_mask: Optional[torch.LongTensor] = None,
            head_mask: Optional[torch.Tensor] = None,
            decoder_head_mask: Optional[torch.Tensor] = None,
            cross_attn_head_mask: Optional[torch.Tensor] = None,
            encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
            past_key_values: Optional[Union[EncoderDecoderCache, Tuple[torch.FloatTensor]]] = None,
            decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None,
            decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            cache_position: Optional[torch.LongTensor] = None,
            quantized_token_ids: Optional[torch.LongTensor] = None
    ) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]:
        r"""
        Returns:

        Example:
         ```python
         >>> import torch
         >>> from transformers import AutoFeatureExtractor, WhisperModel
         >>> from datasets import load_dataset

         >>> model = WhisperVQModel.from_pretrained("openai/whisper-base")
         >>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
         >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
         >>> input_features = inputs.input_features
         >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
         >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
         >>> list(last_hidden_state.shape)
         [1, 2, 512]
         ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if encoder_outputs is None:
            input_features = self._mask_input_features(input_features, attention_mask=attention_mask)

            encoder_outputs = self.encoder(
                input_features,
                attention_mask=attention_mask,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                quantized_token_ids=quantized_token_ids
            )
        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
            encoder_outputs = BaseModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
            )

        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
        attention_mask = attention_mask[:, ::self.encoder.conv1.stride[0] * self.encoder.conv2.stride[0]]
        if self.encoder.config.pooling_kernel_size is not None:
            attention_mask = attention_mask[:, ::self.encoder.config.pooling_kernel_size]
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_attention_mask=attention_mask,
            encoder_hidden_states=encoder_outputs[0],
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=decoder_inputs_embeds,
            position_ids=decoder_position_ids,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
        )

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return Seq2SeqModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )


@add_start_docstrings(
    "The Whisper Model with a language modeling head. Can be used for automatic speech recognition.",
    WHISPER_START_DOCSTRING,
)
class WhisperVQForConditionalGeneration(WhisperGenerationMixin, WhisperPreTrainedModel):
    base_model_prefix = "model"
    _tied_weights_keys = ["proj_out.weight"]

    def __init__(self, config: WhisperVQConfig):
        super().__init__(config)
        self.model = WhisperVQModel(config)
        self.proj_out = nn.Linear(config.d_model, config.vocab_size, bias=False)
        self.quantize_loss = None
        # Initialize weights and apply final processing
        self.post_init()

    def get_encoder(self):
        return self.model.get_encoder()

    def get_decoder(self):
        return self.model.get_decoder()

    def get_output_embeddings(self):
        return self.proj_out

    def set_output_embeddings(self, new_embeddings):
        self.proj_out = new_embeddings

    def get_input_embeddings(self) -> nn.Module:
        return self.model.get_input_embeddings()

    def freeze_encoder(self):
        """
        Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will
        not be updated during training.
        """
        self.model.encoder._freeze_parameters()

    @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
            self,
            input_features: Optional[torch.FloatTensor] = None,
            attention_mask: Optional[torch.LongTensor] = None,
            decoder_input_ids: Optional[torch.LongTensor] = None,
            decoder_attention_mask: Optional[torch.LongTensor] = None,
            head_mask: Optional[torch.Tensor] = None,
            decoder_head_mask: Optional[torch.Tensor] = None,
            cross_attn_head_mask: Optional[torch.Tensor] = None,
            encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
            past_key_values: Optional[Union[EncoderDecoderCache, Tuple[torch.FloatTensor]]] = None,
            decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None,
            decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None,
            labels: Optional[torch.LongTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            cache_position: Optional[torch.LongTensor] = None,
            quantized_token_ids: Optional[torch.LongTensor] = None
    ) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]`
            or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is
            only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Returns:

        Example:

        ```python
        >>> import torch
        >>> from transformers import AutoProcessor, WhisperForConditionalGeneration
        >>> from datasets import load_dataset

        >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
        >>> model = WhisperVQForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")

        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")

        >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
        >>> input_features = inputs.input_features

        >>> generated_ids = model.generate(inputs=input_features)

        >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        >>> transcription
        ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if labels is not None:
            if decoder_input_ids is None and decoder_inputs_embeds is None:
                decoder_input_ids = shift_tokens_right(
                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
                )

        outputs = self.model(
            input_features,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            encoder_outputs=encoder_outputs,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            decoder_inputs_embeds=decoder_inputs_embeds,
            decoder_position_ids=decoder_position_ids,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
            quantized_token_ids=quantized_token_ids
        )
        lm_logits = self.proj_out(outputs[0])

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            # move labels to correct device to enable PP
            labels = labels.to(lm_logits.device)
            loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.reshape(-1))
            if self.training and self.model.encoder.quantize_loss is not None:
                loss = loss + self.model.encoder.quantize_loss

        if not return_dict:
            output = (lm_logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return Seq2SeqLMOutput(
            loss=loss,
            logits=lm_logits,
            past_key_values=outputs.past_key_values,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
        )

    def prepare_inputs_for_generation(
            self,
            decoder_input_ids,
            past_key_values=None,
            use_cache=None,
            encoder_outputs=None,
            attention_mask=None,
            decoder_attention_mask=None,
            cache_position=None,
            quantized_token_ids=None,
            **kwargs,
    ):
        decoder_position_ids = None
        if decoder_attention_mask is not None:
            decoder_position_ids = (decoder_attention_mask.cumsum(-1) - 1).clamp(min=0)

        past_length = 0
        if past_key_values is not None:
            if isinstance(past_key_values, EncoderDecoderCache):
                past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
            else:
                past_length = past_key_values[0][0].shape[2]

            # Some generation methods already pass only the last input ID
            if decoder_input_ids.shape[1] > past_length:
                remove_prefix_length = past_length
            else:
                # Default to old behavior: keep only final ID
                remove_prefix_length = decoder_input_ids.shape[1] - 1

            decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]

            if decoder_position_ids is not None:
                decoder_position_ids = decoder_position_ids[:, remove_prefix_length:]
                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
                decoder_position_ids = decoder_position_ids.clone(memory_format=torch.contiguous_format)

        if cache_position is None:
            cache_position = torch.arange(
                past_length, past_length + decoder_input_ids.shape[1], device=decoder_input_ids.device
            )
        elif use_cache:
            cache_position = cache_position[-decoder_input_ids.shape[1]:]

        # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
        # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
        decoder_input_ids = decoder_input_ids.contiguous()

        if (
                isinstance(past_key_values, EncoderDecoderCache)
                and (
                isinstance(past_key_values.self_attention_cache, StaticCache)
                or isinstance(past_key_values.cross_attention_cache, StaticCache)
        )
                and decoder_attention_mask is not None
                and decoder_attention_mask.ndim == 2
        ):
            batch_size, sequence_length = decoder_input_ids.shape
            device = decoder_input_ids.device

            dtype = self.proj_out.weight.dtype
            min_dtype = torch.finfo(dtype).min

            decoder_attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
                decoder_attention_mask,
                sequence_length=sequence_length,
                target_length=past_key_values.self_attention_cache.get_max_length(),
                dtype=dtype,
                device=device,
                min_dtype=min_dtype,
                cache_position=cache_position,
                batch_size=batch_size,
            )

        return {
            "encoder_outputs": encoder_outputs,
            "attention_mask": attention_mask,
            "past_key_values": past_key_values,
            "decoder_input_ids": decoder_input_ids,
            "use_cache": use_cache,
            "decoder_attention_mask": decoder_attention_mask,
            "decoder_position_ids": decoder_position_ids,
            "cache_position": cache_position,
            "quantized_token_ids": quantized_token_ids
        }

    def _retrieve_init_tokens(self, input_features, batch_size, generation_config, config, num_segment_frames, kwargs):
        if self.config.skip_language_detection:
            return torch.as_tensor([[generation_config.decoder_start_token_id] for _ in range(batch_size)],
                                   dtype=torch.long, device=self.device).expand(batch_size, -1)
        else:
            return super()._retrieve_init_tokens(input_features, batch_size, generation_config, config,
                                                 num_segment_frames, kwargs)


class WhisperDecoderWrapper(WhisperPreTrainedModel):
    """
    This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
    used in combination with the [`EncoderDecoderModel`] framework.
    """

    def __init__(self, config):
        super().__init__(config)
        config.is_encoder_decoder = False
        self.decoder = WhisperVQDecoder(config)

    def get_input_embeddings(self):
        return self.decoder.embed_tokens

    def set_input_embeddings(self, value):
        self.decoder.embed_tokens = value

    def forward(self, *args, **kwargs):
        return self.decoder(*args, **kwargs)


@add_start_docstrings(
    """
    Whisper decoder with a language modeling head on top (linear layer with weights tied to the input embeddings).
    """,
    WHISPER_START_DOCSTRING,
)
class WhisperForCausalLM(WhisperPreTrainedModel):
    _tied_weights_keys = ["proj_out.weight"]
    main_input_name = "input_ids"

    def __init__(self, config):
        super().__init__(config)
        config.is_encoder_decoder = False
        self.model = WhisperDecoderWrapper(config)

        self.proj_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_output_embeddings(self):
        return self.proj_out

    def set_output_embeddings(self, new_embeddings):
        self.proj_out = new_embeddings

    def get_input_embeddings(self) -> nn.Module:
        return self.model.get_input_embeddings()

    def set_input_embeddings(self, value):
        self.model.set_input_embeddings(value)

    def set_decoder(self, decoder):
        self.model.decoder = decoder

    def get_decoder(self):
        return self.model.decoder

    @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
    def forward(
            self,
            input_ids: torch.LongTensor = None,
            attention_mask: Optional[torch.Tensor] = None,
            encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
            head_mask: Optional[torch.Tensor] = None,
            cross_attn_head_mask: Optional[torch.Tensor] = None,
            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            labels: Optional[torch.LongTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
        r"""
        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
                [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids)
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.
                [What are attention masks?](../glossary#attention-mask)
            encoder_outputs  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                if the model is configured as a decoder.
            head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.
            cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.
            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
                tensors are only required when the model is used as a decoder in a Sequence to Sequence model. Contains
                pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
                blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If
                `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
                don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
                `decoder_input_ids` of shape `(batch_size, sequence_length)`.
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
                than the model's internal embedding lookup matrix.
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
                Indices depicting the position of the input sequence tokens in the sequence. It is used to update the cache
                in the correct position and to infer the complete sequence length.

        Returns:

        Example:

        ```python
        >>> from transformers import WhisperForCausalLM, WhisperForConditionalGeneration, WhisperProcessor
        >>> import torch
        >>> from datasets import load_dataset

        >>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
        >>> model = WhisperVQForConditionalGeneration.from_pretrained("openai/whisper-large-v2")

        >>> assistant_model = WhisperForCausalLM.from_pretrained("distil-whisper/distil-large-v2")

        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        >>> sample = ds[0]["audio"]
        >>> input_features = processor(
        ...     sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt"
        ... ).input_features

        >>> predicted_ids = model.generate(input_features, assistant_model=assistant_model)

        >>> # decode token ids to text
        >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
        >>> transcription
        ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # If the user passed a tuple or `BaseModelOutput` for encoder_outputs, we extract only the hidden states
        if isinstance(encoder_outputs, (BaseModelOutput, tuple, list)):
            encoder_outputs = encoder_outputs[0]

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model.decoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            encoder_hidden_states=encoder_outputs,
            head_mask=head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
        )

        logits = self.proj_out(outputs[0])

        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

    def prepare_inputs_for_generation(
            self,
            input_ids,
            past_key_values=None,
            use_cache=None,
            encoder_outputs=None,
            attention_mask=None,
            cache_position=None,
            **kwargs,
    ):
        past_length = 0
        if past_key_values is not None:
            if isinstance(past_key_values, (Cache, EncoderDecoderCache)):
                past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
            else:
                past_length = past_key_values[0][0].shape[2]

            # Some generation methods already pass only the last input ID
            if input_ids.shape[1] > past_length:
                remove_prefix_length = past_length
            else:
                # Default to old behavior: keep only final ID
                remove_prefix_length = input_ids.shape[1] - 1

            input_ids = input_ids[:, remove_prefix_length:]

        if cache_position is None:
            cache_position = torch.arange(past_length, past_length + input_ids.shape[1], device=input_ids.device)
        elif use_cache:
            cache_position = cache_position[-input_ids.shape[1]:]

        return {
            "encoder_outputs": encoder_outputs,
            "past_key_values": past_key_values,
            "input_ids": input_ids,
            "use_cache": use_cache,
            "attention_mask": attention_mask,
            "cache_position": cache_position,
        }

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past


@add_start_docstrings(
    """
    Whisper Encoder Model with a sequence classification head on top (a linear layer over the pooled output) for tasks
    like SUPERB Keyword Spotting.
    """,
    WHISPER_ENCODER_INPUTS_DOCSTRING,
)
class WhisperForAudioClassification(WhisperPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.encoder = WhisperVQEncoder(config)
        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
        if config.use_weighted_layer_sum:
            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
        self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size)
        self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    def freeze_encoder(self):
        """
        Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will
        not be updated during training. Only the projection layers and classification head will be updated.
        """
        self.encoder._freeze_parameters()

    def get_input_embeddings(self) -> nn.Module:
        return self.encoder.get_input_embeddings()

    def set_input_embeddings(self, value: nn.Module):
        self.encoder.set_input_embeddings(value)

    @add_start_docstrings_to_model_forward(WHISPER_ENCODER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
            self,
            input_features: Optional[torch.LongTensor] = None,
            head_mask: Optional[torch.Tensor] = None,
            encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
            labels: Optional[torch.LongTensor] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Example:

        ```python
        >>> import torch
        >>> from transformers import AutoFeatureExtractor, WhisperForAudioClassification
        >>> from datasets import load_dataset

        >>> feature_extractor = AutoFeatureExtractor.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id")
        >>> model = WhisperForAudioClassification.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id")

        >>> ds = load_dataset("google/fleurs", "all", split="validation", streaming=True)
        >>> sample = next(iter(ds))

        >>> inputs = feature_extractor(
        ...     sample["audio"]["array"], sampling_rate=sample["audio"]["sampling_rate"], return_tensors="pt"
        ... )
        >>> input_features = inputs.input_features

        >>> with torch.no_grad():
        ...     logits = model(input_features).logits

        >>> predicted_class_ids = torch.argmax(logits).item()
        >>> predicted_label = model.config.id2label[predicted_class_ids]
        >>> predicted_label
        'Afrikaans'
        ```"""

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        if self.config.use_weighted_layer_sum:
            output_hidden_states = True
        elif output_hidden_states is None:
            output_hidden_states = self.config.output_hidden_states

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_features,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )

        if self.config.use_weighted_layer_sum:
            hidden_states = encoder_outputs[_HIDDEN_STATES_START_POSITION]
            hidden_states = torch.stack(hidden_states, dim=1)
            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
        else:
            hidden_states = encoder_outputs[0]

        hidden_states = self.projector(hidden_states)
        pooled_output = hidden_states.mean(dim=1)

        logits = self.classifier(pooled_output)

        loss = None

        if labels is not None:
            loss_fct = CrossEntropyLoss()
            # move labels to correct device to enable PP
            labels = labels.to(logits.device)
            loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))

        if not return_dict:
            output = (logits,) + encoder_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )