import os
from typing import Dict, List, Optional
from dataclasses import dataclass, field
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)

@dataclass
class ModelArguments:
    model_name: str = field(
        default="gpt2",
        metadata={"help": "The name of the pretrained model to use for text generation."}
    )
    max_tokens: int = field(
        default=50,
        metadata={"help": "The maximum number of tokens to generate in the response."}
    )

class MockOpenAI:
    """
    A mock implementation of OpenAI's API using Hugging Face's pipeline for text generation.

    :param api_key: Your Hugging Face API key, required for authentication.
    :param base_url: The base URL for the Hugging Face API, defaults to the production URL.
    :param model_name: The name of the pretrained model to use for text generation, defaults to 'gpt2'.
    :param max_tokens: The maximum number of tokens to generate in the response, defaults to 50.
    """
    def __init__(
        self,
        api_key: Optional[str] = None,
        base_url: Optional[str] = None,
        model_name: Optional[str] = "gpt2",
        max_tokens: int = 50,
    ):
        self.api_key = api_key or os.environ.get("HUGGING_FACE_API_KEY")
        self.base_url = base_url or "https://api-inference.huggingface.co/models"
        self.model_name = model_name
        self.max_tokens = max_tokens
        self.config = AutoConfig.from_pretrained(self.model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        self.model = AutoModelForSequenceClassification.from_pretrained(self.model_name, config=self.config)
        self.data_collator = DataCollatorWithPadding(self.tokenizer)
        self.trainer = Trainer(
            model=self.model,
            args=TrainingArguments(
                output_dir="./",
                num_train_epochs=1,
                learning_rate=1e-5,
                per_device_train_batch_size=16,
                per_device_eval_batch_size=16,
                evaluation_strategy="epoch",
            ),
        )

    class Chat:
        def __init__(self, mock_openai: 'MockOpenAI'):
            self.mock_openai = mock_openai

        class Completions:
            def __init__(self, mock_openai: 'MockOpenAI'):
                self.mock_openai = mock_openai

            def create(
                self,
                messages: List[Dict[str, str]],
                model: Optional[str] = None,
                max_tokens: int = 50,
                **kwargs,
            ):
                """
                Generate a text completion based on the given messages.

                :param messages: List of message objects, each containing 'role' and 'content'.
                :param model: The name of the pretrained model to use for text generation, defaults to 'gpt2'.
                :param max_tokens: The maximum number of tokens to generate in the response, defaults to 50.
                :param kwargs: Additional keyword arguments to pass to the pipeline function.
                :return: A dictionary containing the generated text.
                """
                if not self.mock_openai.config.is_decoder:
                    raise ValueError("This model is not a decoder.")

                model_name = model or self.mock_openai.model_name
                prompt = " ".join([msg["content"] for msg in messages])

                inputs = self.mock_openai.tokenizer(prompt, padding="max_length", truncation=True)
                outputs = self.mock_openai.trainer.predict(inputs.to_tensor(pad_to_multiple_of=self.mock_openai.config.max_length))
                result = self.mock_openai.tokenizer.decode(outputs[0], skip_special_tokens=True)

                if max_tokens is not None and len(result) > max_tokens:
                    result = result[:max_tokens]

                return result

    @property
    def chat(self):
        """
        Get the Chat class instance with the pretrained model for text generation.

        :return: The Chat class instance.
        """
        return self.Chat(self)

# Example usage
if __name__ == "__main__":
    parser = HfArgumentParser((ModelArguments,))
    model_args = parser.parse_args_into_dataclasses()[0]
    client = MockOpenAI(model_name=model_args.model_name, max_tokens=model_args.max_tokens)
    chat_completion = client.chat.Completions().create(
        messages=[
            {
                "role": "system",
                "content": "You are a helpful assistant.",
            },
            {
                "role": "user",
                "content": "What is deep learning?",
            }
        ]
    )

    print(chat_completion)