




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


a													=			logging.get_logger(__name__)

a													=			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a													=			{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

a													=			{
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}




class       _A				(		__lowercase     ):
  __a					    =       VOCAB_FILES_NAMES
  __a					    =       PRETRAINED_VOCAB_FILES_MAP
  __a					    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __a					    =       ["""input_ids""", """attention_mask"""]
  __a					    =       GPTaTokenizer






  def __init__(    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE="<|endoftext|>"						,		_SCREAMING_SNAKE_CASE="<|endoftext|>"						,		_SCREAMING_SNAKE_CASE="<|endoftext|>"						,		_SCREAMING_SNAKE_CASE=False						,		**_SCREAMING_SNAKE_CASE						,		):
        super().__init__(
            _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		tokenizer_file=_SCREAMING_SNAKE_CASE						,		unk_token=_SCREAMING_SNAKE_CASE						,		bos_token=_SCREAMING_SNAKE_CASE						,		eos_token=_SCREAMING_SNAKE_CASE						,		add_prefix_space=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE						,		)

        _UpperCAmelCase      =			kwargs.pop("""add_bos_token"""						,		_SCREAMING_SNAKE_CASE			)

        _UpperCAmelCase      =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
        if pre_tok_state.get("""add_prefix_space"""						,		_SCREAMING_SNAKE_CASE			) != add_prefix_space:
              _UpperCAmelCase      =			getattr(_SCREAMING_SNAKE_CASE						,		pre_tok_state.pop("""type"""			)			)
              _UpperCAmelCase      =			add_prefix_space
              _UpperCAmelCase      =			pre_tok_class(**_SCREAMING_SNAKE_CASE			)

        _UpperCAmelCase      =			add_prefix_space






  def 				UpperCAmelCase (    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
        _UpperCAmelCase      =			kwargs.get("""is_split_into_words"""						,		_SCREAMING_SNAKE_CASE			)
        assert self.add_prefix_space or not is_split_into_words, (
            F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






  def 				UpperCAmelCase (    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
        _UpperCAmelCase      =			kwargs.get("""is_split_into_words"""						,		_SCREAMING_SNAKE_CASE			)

        assert self.add_prefix_space or not is_split_into_words, (
            F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






  def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
        _UpperCAmelCase      =			self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,		name=_SCREAMING_SNAKE_CASE			)
        return tuple(_SCREAMING_SNAKE_CASE			)






  def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
        _UpperCAmelCase      =			[]
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE						,		add_special_tokens=_SCREAMING_SNAKE_CASE			) + [self.eos_token_id]			)

        if len(_SCREAMING_SNAKE_CASE			) > self.model_max_length:
              _UpperCAmelCase      =			input_ids[-self.model_max_length :]
        return input_ids





import math




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * power_factor




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * math.sqrt(1 - power_factor**2       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()