import json
import struct
import torch
import threading
import warnings

###
# Code from ljleb/sd-mecha/sd_mecha/streaming.py

DTYPE_MAPPING = {
    'F64': (torch.float64, 8),
    'F32': (torch.float32, 4),
    'F16': (torch.float16, 2),
    'BF16': (torch.bfloat16, 2),
    'I8': (torch.int8, 1),
    'I64': (torch.int64, 8),
    'I32': (torch.int32, 4),
    'I16': (torch.int16, 2),
    "F8_E4M3": (torch.float8_e4m3fn, 1),
    "F8_E5M2": (torch.float8_e5m2, 1),
}

class InSafetensorsDict:
    def __init__(self, f, buffer_size):
        self.default_buffer_size = buffer_size
        self.file = f
        self.header_size, self.header = self._read_header()
        self.buffer = bytearray()
        self.buffer_start_offset = 8 + self.header_size
        self.lock = threading.Lock()

    def __del__(self):
        self.close()

    def __getitem__(self, key):
        if key not in self.header or key == "__metadata__":
            raise KeyError(key)
        return self._load_tensor(key)

    def __iter__(self):
        return iter(self.keys())

    def __len__(self):
        return len(self.header)

    def close(self):
        self.file.close()
        self.buffer = None
        self.header = None

    def keys(self):
        return (
            key
            for key in self.header.keys()
            if key != "__metadata__"
        )

    def values(self):
        for key in self.keys():
            yield self[key]

    def items(self):
        for key in self.keys():
            yield key, self[key]

    def _read_header(self):
        header_size_bytes = self.file.read(8)
        header_size = struct.unpack('<Q', header_size_bytes)[0]
        header_json = self.file.read(header_size).decode('utf-8').strip()
        header = json.loads(header_json)

        # sort by memory order to reduce seek time
        sorted_header = dict(sorted(header.items(), key=lambda item: item[1].get('data_offsets', [0])[0]))
        return header_size, sorted_header

    def _ensure_buffer(self, start_pos, length):
        if start_pos < self.buffer_start_offset or start_pos + length > self.buffer_start_offset + len(self.buffer):
            self.file.seek(start_pos)
            necessary_buffer_size = max(self.default_buffer_size, length)
            if len(self.buffer) < necessary_buffer_size:
                self.buffer = bytearray(necessary_buffer_size)
            else:
                self.buffer = self.buffer[:necessary_buffer_size]

            self.file.readinto(self.buffer)
            self.buffer_start_offset = start_pos

    def _load_tensor(self, tensor_name):
        tensor_info = self.header[tensor_name]
        offsets = tensor_info['data_offsets']
        dtype, dtype_bytes = DTYPE_MAPPING[tensor_info['dtype']]
        shape = tensor_info['shape']
        total_bytes = offsets[1] - offsets[0]
        absolute_start_pos = 8 + self.header_size + offsets[0]
        with warnings.catch_warnings():
            warnings.simplefilter('ignore')
            with self.lock:
                self._ensure_buffer(absolute_start_pos, total_bytes)
                buffer_offset = absolute_start_pos - self.buffer_start_offset
                return torch.frombuffer(self.buffer, count=total_bytes // dtype_bytes, offset=buffer_offset, dtype=dtype).reshape(shape)