






"""simple docstring"""
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


_lowerCAmelCase         =							logging.get_logger(__name__)

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

_lowerCAmelCase         =							{
    "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",
    },
}

_lowerCAmelCase         =							{
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[Any]    =				VOCAB_FILES_NAMES
	_A       :				str    =				PRETRAINED_VOCAB_FILES_MAP
	_A       :				Optional[int]    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A       :				Any    =				["""input_ids""", """attention_mask"""]
	_A       :				Any    =				GPTaTokenizer





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

		A_		:						int	    =   kwargs.pop("""add_bos_token"""     ,     lowerCAmelCase_							)

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

		A_		:						List[str]	    =   add_prefix_space





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		A_		:						Any	    =   kwargs.get("""is_split_into_words"""     ,     lowerCAmelCase_							)
		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(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		A_		:						Optional[Any]	    =   kwargs.get("""is_split_into_words"""     ,     lowerCAmelCase_							)

		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(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						Optional[int]	    =   self._tokenizer.model.save(lowerCAmelCase_     ,     name=lowerCAmelCase_							)
		return tuple(lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   []
		for is_user, text in conversation.iter_texts():
			input_ids.extend(self.encode(lowerCAmelCase_     ,     add_special_tokens=lowerCAmelCase_							) + [self.eos_token_id]							)

		if len(lowerCAmelCase_							) > self.model_max_length:
			A_		:						List[str]	    =   input_ids[-self.model_max_length :]
		return input_ids









"""simple docstring"""
import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
_lowerCAmelCase         =							"."


if __name__ == "__main__":
							_lowerCAmelCase         =							os.path.join(REPO_PATH, "utils/documentation_tests.txt")
							_lowerCAmelCase         =							[]
							_lowerCAmelCase         =							[]
							with open(doctest_file_path) as fp:
														for line in fp:
																					_lowerCAmelCase         =							line.strip()
																					_lowerCAmelCase         =							os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														_lowerCAmelCase         =							"\n".join(non_existent_paths)
														raise ValueError(F'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
							if all_paths != sorted(all_paths):
														raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")

