

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 _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =VOCAB_FILES_NAMES
			lowerCamelCase__           =PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__           =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__           =['input_ids', 'attention_mask']
			lowerCamelCase__           =GPTaTokenizer
			def __init__(			self							: str      ,      a							: Tuple=None      ,      a							: List[Any]=None      ,      a							: List[Any]=None      ,      a							: Any="<|endoftext|>"      ,      a							: str="<|endoftext|>"      ,      a							: Any="<|endoftext|>"      ,      a							: Union[str, Any]=False      ,      **a							: int      ,      )							->       Union[str, Any]:

				"""simple docstring"""

				super().__init__(
				    a      ,      a      ,      tokenizer_file=a      ,      unk_token=a      ,      bos_token=a      ,      eos_token=a      ,      add_prefix_space=a      ,      **a      ,      )

				SCREAMING_SNAKE_CASE   :      Tuple				     =       kwargs.pop("add_bos_token"      ,      a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
				if pre_tok_state.get("add_prefix_space"      ,      a	) != add_prefix_space:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       getattr(a      ,      pre_tok_state.pop("type"	)	)
					SCREAMING_SNAKE_CASE   :      List[str]				     =       add_prefix_space
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       pre_tok_class(**a	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       add_prefix_space
			def 							__UpperCamelCase (			self							: Optional[int]      ,      *a							: Tuple      ,      **a							: List[Any]	)							->       BatchEncoding:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       kwargs.get("is_split_into_words"      ,      a	)
				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(*a      ,      **a	)
			def 							__UpperCamelCase (			self							: List[str]      ,      *a							: Any      ,      **a							: Dict	)							->       BatchEncoding:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       kwargs.get("is_split_into_words"      ,      a	)

				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(*a      ,      **a	)
			def 							__UpperCamelCase (			self							: str      ,      a							: str      ,      a							: Optional[str] = None	)							->       Tuple[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self._tokenizer.model.save(a      ,      name=a	)
				return tuple(a	)






			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: "Conversation"	)							->       List[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
				for is_user, text in conversation.iter_texts():
					input_ids.extend(self.encode(a      ,      add_special_tokens=a	) + [self.eos_token_id]	)

				if len(a	) > self.model_max_length:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       input_ids[-self.model_max_length :]
				return input_ids


from __future__ import annotations

def  lowerCamelCase__ (					_a							,			_a):
	if b == 0:
		return (1, 0)
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      Tuple				     =       extended_euclid(_a							,			a % b)
	SCREAMING_SNAKE_CASE   :      Dict				     =       a // b
	return (y, x - k * y)

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      str				     =       extended_euclid(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      str				     =       na * na
	SCREAMING_SNAKE_CASE   :      int				     =       ra * x * na + ra * y * na
	return (n % m + m) % m

def  lowerCamelCase__ (					_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      List[str]				     =       extended_euclid(_a							,			_a)
	if b < 0:
		SCREAMING_SNAKE_CASE   :      int				     =       (b % n + n) % n
	return b

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       invert_modulo(_a							,			_a), invert_modulo(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       na * na
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ra * x * na + ra * y * na
	return (n % m + m) % m


if __name__ == "__main__":
			from doctest import testmod

			testmod(name='chinese_remainder_theorem', verbose=True)
			testmod(name='chinese_remainder_theorem2', verbose=True)
			testmod(name='invert_modulo', verbose=True)
			testmod(name='extended_euclid', verbose=True)