import torch
from transformers import PreTrainedModel, PreTrainedTokenizerFast, PretrainedConfig, LogitsProcessorList
from transformers.generation_utils import GenerationMixin
from transformers.modeling_outputs import CausalLMOutput

class HelloWorldConfig(PretrainedConfig):
    model_type = "hello-world"

class HelloWorldModel(PreTrainedModel, GenerationMixin):
    config_class = HelloWorldConfig

    def __init__(self, config):
        super().__init__(config)

    def forward(self, input_ids=None, **kwargs):
        batch_size = input_ids.shape[0]
        sequence_length = input_ids.shape[1]
        
        # Generate a tensor with repeated "Hello, world!" token IDs
        hello_world_token_id = self.config.vocab_size - 1  # assuming last token is "Hello, world!"
        logits = torch.full((batch_size, sequence_length, self.config.vocab_size), float('-inf'))
        logits[:, :, hello_world_token_id] = 0  # setting logits for "Hello, world!" to 0 (highest value)
        
        return CausalLMOutput(logits=logits)

    def prepare_inputs_for_generation(self, input_ids, **kwargs):
        return {"input_ids": input_ids}

    def _update_model_kwargs_for_generation(self, outputs, model_kwargs, is_encoder_decoder=False):
        return model_kwargs

tokenizer_config = {
    "do_lower_case": False,
    "model_max_length": 512,
    "padding_side": "right",
    "special_tokens_map_file": None,
    "tokenizer_file": "tokenizer.json",
    "unk_token": "<unk>",
    "bos_token": "<s>",
    "eos_token": "</s>",
    "vocab_size": 1,  # Simplified vocabulary size
}

# Save tokenizer configuration
with open("tokenizer.json", "w") as f:
    import json
    json.dump(tokenizer_config, f)

tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")
tokenizer.add_tokens(["Hello, world!"])