


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			:						Optional[Any]        =  logging.get_logger(__name__)

A			:						Tuple        =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A			:						List[str]        =  {
    '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			:						Tuple        =  {
    'gpt2': 1_0_2_4,
    'gpt2-medium': 1_0_2_4,
    'gpt2-large': 1_0_2_4,
    'gpt2-xl': 1_0_2_4,
    'distilgpt2': 1_0_2_4,
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					PRETRAINED_VOCAB_FILES_MAP
					A__     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					['''input_ids''', '''attention_mask''']
					A__     =					GPTaTokenizer

					def __init__(self       :		Union[str, Any] ,   _UpperCAmelCase       :		List[Any]=None ,   _UpperCAmelCase       :		List[Any]=None ,   _UpperCAmelCase       :		List[Any]=None ,   _UpperCAmelCase       :		str="<|endoftext|>" ,   _UpperCAmelCase       :		Dict="<|endoftext|>" ,   _UpperCAmelCase       :		int="<|endoftext|>" ,   _UpperCAmelCase       :		str=False ,   **_UpperCAmelCase       :		str ,   )		->  List[Any]:




							"""simple docstring"""


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

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

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

							lowercase__	     =						add_prefix_space

					def     lowerCamelCase__	(self       :		Tuple ,   *_UpperCAmelCase       :		List[Any] ,   **_UpperCAmelCase       :		Optional[Any]						)		->  BatchEncoding:




							"""simple docstring"""


							lowercase__	     =						kwargs.get("""is_split_into_words""" ,   _UpperCAmelCase						)
							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(*_UpperCAmelCase ,   **_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[str] ,   *_UpperCAmelCase       :		Optional[Any] ,   **_UpperCAmelCase       :		List[Any]						)		->  BatchEncoding:




							"""simple docstring"""


							lowercase__	     =						kwargs.get("""is_split_into_words""" ,   _UpperCAmelCase						)

							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(*_UpperCAmelCase ,   **_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = None						)		->  Tuple[str]:




							"""simple docstring"""


							lowercase__	     =						self._tokenizer.model.save(_UpperCAmelCase ,   name=_UpperCAmelCase						)
							return tuple(_UpperCAmelCase						)


					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		"Conversation"						)		->  List[int]:




							"""simple docstring"""


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

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





from __future__ import annotations






def 					UpperCamelCase   (      __magic_name__	:				list[int]	)  ->    list[int]:  # This function is recursive
		"""simple docstring"""




		lowercase__	     =						len(__magic_name__	)
		# If the array contains only one element, we return it (it's the stop condition of
		# recursion)
		if array_length <= 1:
				return array
				# Else
		lowercase__	     =						array[0]
		lowercase__	     =						False
		lowercase__	     =						1
		lowercase__	     =						[]
		while not is_found and i < array_length:
				if array[i] < pivot:
						lowercase__	     =						True
						lowercase__	     =						[element for element in array[i:] if element >= array[i]]
						lowercase__	     =						longest_subsequence(__magic_name__	)
						if len(__magic_name__	) > len(__magic_name__	):
								lowercase__	     =						temp_array
				else:
						i += 1

		lowercase__	     =						[element for element in array[1:] if element >= pivot]
		lowercase__	     =						[pivot, *longest_subsequence(__magic_name__	)]
		if len(__magic_name__	) > len(__magic_name__	):
				return temp_array
		else:
				return longest_subseq


if __name__ == "__main__":
	import doctest

	doctest.testmod()

