




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase	:       int     =	logging.get_logger(__name__)


__lowerCAmelCase	:       Optional[int]     =	'▁'

__lowerCAmelCase	:       Union[str, Any]     =	{'vocab_file': 'spiece.model'}

__lowerCAmelCase	:       int     =	{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

__lowerCAmelCase	:       Any     =	{
    'google/reformer-crime-and-punishment': 52_4288,
}




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Dict											=      VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_ :							int											=      PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_ :							List[Any]											=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      ["""input_ids""", """attention_mask"""]





				def __init__(   self       :  List[Any]      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Dict="</s>"      ,   __lowerCamelCase       :  Dict="<unk>"      ,   __lowerCamelCase       :  Dict=[]      ,   __lowerCamelCase       :  Optional[Dict[str, Any]] = None      ,   **__lowerCamelCase       :  Dict      ,   )      ->  None:
							a					=    {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=__lowerCamelCase      ,   unk_token=__lowerCamelCase      ,   additional_special_tokens=__lowerCamelCase      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **__lowerCamelCase      ,   )

							a					=    vocab_file
							a					=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(__lowerCamelCase							)





				@property
				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  int:
							return self.sp_model.get_piece_size()





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Dict[str, int]:
							a					=    {self.convert_ids_to_tokens(__lowerCamelCase							): i for i in range(self.vocab_size							)}
							vocab.update(self.added_tokens_encoder							)
							return vocab





				def __getstate__(   self       :  Optional[Any]							)      ->  Optional[Any]:
							a					=    self.__dict__.copy()
							a					=    None
							return state





				def __setstate__(   self       :  str      ,   __lowerCamelCase       :  Tuple							)      ->  List[Any]:
							a					=    d

							# for backward compatibility
							if not hasattr(self      ,   "sp_model_kwargs"							):
										a					=    {}

							a					=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(self.vocab_file							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  str							)      ->  List[str]:
							return self.sp_model.encode(__lowerCamelCase      ,   out_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  Dict							)      ->  Any:
							return self.sp_model.piece_to_id(__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  Union[str, Any]							)      ->  str:
							if index < self.sp_model.get_piece_size():
										a					=    self.sp_model.IdToPiece(__lowerCamelCase							)
							return token





				def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  Optional[Any]							)      ->  List[Any]:
							a					=    []
							a					=    ""
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													out_string += self.sp_model.decode(__lowerCamelCase							) + token
													a					=    []
										else:
													current_sub_tokens.append(__lowerCamelCase							)
							out_string += self.sp_model.decode(__lowerCamelCase							)
							return out_string.strip()





				def 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[str] = None							)      ->  Tuple[str]:
							if not os.path.isdir(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a					=    os.path.join(
							    __lowerCamelCase      ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

							if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowerCamelCase							) and os.path.isfile(self.vocab_file							):
										copyfile(self.vocab_file      ,   __lowerCamelCase							)
							elif not os.path.isfile(self.vocab_file							):
										with open(__lowerCamelCase      ,   "wb"							) as fi:
													a					=    self.sp_model.serialized_model_proto()
													fi.write(__lowerCamelCase							)

							return (out_vocab_file,)












import argparse
import os
import re


__lowerCAmelCase	:       Union[str, Any]     =	'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCAmelCase	:       Dict     =	re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__lowerCAmelCase	:       Any     =	re.compile(r'\s*\(\s*"(\S[^"]+)"')




def     __magic_name__       (				A	:      int,						A	:      bool = False     ):






			'''simple docstring'''







			with open(A,						"r",						encoding="utf-8"     ) as f:
						a					=    f.read()

			a					=    content.split("\n"     )
			a					=    []
			a					=    0
			while line_idx < len(A     ):
						if _re_intro_mapping.search(lines[line_idx]     ) is not None:
									a					=    len(re.search(R"^(\s*)\S",						lines[line_idx]     ).groups()[0]     ) + 8
									# Start of a new mapping!
									while not lines[line_idx].startswith(" " * indent + "("     ):
												new_lines.append(lines[line_idx]     )
												line_idx += 1

									a					=    []
									while lines[line_idx].strip() != "]":
												# Blocks either fit in one line or not
												if lines[line_idx].strip() == "(":
															a					=    line_idx
															while not lines[line_idx].startswith(" " * indent + ")"     ):
																		line_idx += 1
															blocks.append("\n".join(lines[start_idx : line_idx + 1]     )     )
												else:
															blocks.append(lines[line_idx]     )
												line_idx += 1

									# Sort blocks by their identifiers
									a					=    sorted(A,						key=lambda A     : _re_identifier.search(A     ).groups()[0]     )
									new_lines += blocks
						else:
									new_lines.append(lines[line_idx]     )
									line_idx += 1

			if overwrite:
						with open(A,						"w",						encoding="utf-8"     ) as f:
									f.write("\n".join(A     )     )
			elif "\n".join(A     ) != content:
						return True




def     __magic_name__       (				A	:      bool = False     ):






			'''simple docstring'''







			a					=    [os.path.join(A,						A     ) for f in os.listdir(A     ) if f.endswith(".py"     )]
			a					=    [sort_auto_mapping(A,						overwrite=A     ) for fname in fnames]

			if not overwrite and any(A     ):
						a					=    [f for f, d in zip(A,						A     ) if d]
						raise ValueError(
						    F"""The following files have auto mappings that need sorting: {", ".join(A     )}. Run `make style` to fix"""
						    " this."     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Dict     =	argparse.ArgumentParser()
		parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
		__lowerCAmelCase	:       Optional[Any]     =	parser.parse_args()

		sort_all_auto_mappings(not args.check_only)






