

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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

A_	:			int  	=      {'vocab_file': 'sentencepiece.bpe.model'}

A_	:			List[Any]  	=      {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

A_	:			Dict  	=      {
    'camembert-base': 512,
}

A_	:			Optional[Any]  	=      '▁'



class    _a						(__lowercase    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	VOCAB_FILES_NAMES
					UpperCAmelCase__:       Any  							=	PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:       int  							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:       str  							=	["input_ids", "attention_mask"]






					def __init__(			self   ,	A__   ,	A__="<s>"   ,	A__="</s>"   ,	A__="</s>"   ,	A__="<s>"   ,	A__="<unk>"   ,	A__="<pad>"   ,	A__="<mask>"   ,	A__=["<s>NOTUSED", "</s>NOTUSED"]   ,	A__ = None   ,	**A__   ,	):
						# Mask token behave like a normal word, i.e. include the space before it
						A__							:   str					       =		AddedToken(_a   ,	lstrip=_a   ,	rstrip=_a      ) if isinstance(_a   ,	_a      ) else mask_token

						A__							:   Dict					       =		{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=_a   ,	eos_token=_a   ,	unk_token=_a   ,	sep_token=_a   ,	cls_token=_a   ,	pad_token=_a   ,	mask_token=_a   ,	additional_special_tokens=_a   ,	sp_model_kwargs=self.sp_model_kwargs   ,	**_a   ,	)
						A__							:   Dict					       =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
						self.sp_model.Load(str(_a      )      )
						A__							:   str					       =		vocab_file
						# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
						# sentencepiece vocabulary (this is the case for <s> and </s>
						A__							:   Tuple					       =		{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
						A__							:   Union[str, Any]					       =		len(self.fairseq_tokens_to_ids      )
						A__							:   str					       =		len(self.sp_model      ) + len(self.fairseq_tokens_to_ids      )
						A__							:   List[str]					       =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}






					def   __A      (			self   ,	A__   ,	A__ = None      ):

						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A__							:   Dict					       =		[self.cls_token_id]
						A__							:   Optional[Any]					       =		[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep






					def   __A      (			self   ,	A__   ,	A__ = None   ,	A__ = False      ):
						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=_a   ,	token_ids_a=_a   ,	already_has_special_tokens=_a      )

						if token_ids_a is None:
							return [1] + ([0] * len(_a      )) + [1]
						return [1] + ([0] * len(_a      )) + [1, 1] + ([0] * len(_a      )) + [1]






					def   __A      (			self   ,	A__   ,	A__ = None      ):
						A__							:   Any					       =		[self.sep_token_id]
						A__							:   str					       =		[self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep      ) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]






					@property
					def   __A      (			self      ):
						return len(self.fairseq_tokens_to_ids      ) + len(self.sp_model      )






					def   __A      (			self      ):
						A__							:   List[Any]					       =		{self.convert_ids_to_tokens(_a      ): i for i in range(self.vocab_size      )}
						vocab.update(self.added_tokens_encoder      )
						return vocab






					def   __A      (			self   ,	A__      ):
						return self.sp_model.encode(_a   ,	out_type=_a      )






					def   __A      (			self   ,	A__      ):
						if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
						elif self.sp_model.PieceToId(_a      ) == 0:
							# Convert sentence piece unk token to fairseq unk token index
							return self.unk_token_id
						return self.fairseq_offset + self.sp_model.PieceToId(_a      )






					def   __A      (			self   ,	A__      ):
						if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset      )






					def   __A      (			self   ,	A__      ):
						A__							:   Dict					       =		[]
						A__							:   int					       =		''''''
						A__							:   int					       =		False
						for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
								if not prev_is_special:
									out_string += " "
								out_string += self.sp_model.decode(_a      ) + token
								A__							:   Optional[int]					       =		True
								A__							:   Any					       =		[]
							else:
								current_sub_tokens.append(_a      )
								A__							:   str					       =		False
						out_string += self.sp_model.decode(_a      )
						return out_string.strip()






					def __getstate__(			self      ):
						A__							:   Optional[Any]					       =		self.__dict__.copy()
						A__							:   int					       =		None
						return state






					def __setstate__(			self   ,	A__      ):
						A__							:   List[str]					       =		d

						# for backward compatibility
						if not hasattr(self   ,	"""sp_model_kwargs"""      ):
							A__							:   List[str]					       =		{}

						A__							:   Optional[int]					       =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
						self.sp_model.Load(self.vocab_file      )






					def   __A      (			self   ,	A__   ,	A__ = None      ):
						if not os.path.isdir(_a      ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
							return
						A__							:   int					       =		os.path.join(
						    _a   ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

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

						return (out_vocab_file,)






import argparse
from collections import defaultdict

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[int]   ,				lowercase_:			Optional[Any]   ,				lowercase_:			Union[str, Any]   ,				lowercase_:			Any )					->				int:
	A__							:   Optional[Any]					       =		f"""{file}_{class_name}_{test_name}"""
	done_test[_id] += 1

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   Union[str, Any]					       =		f.readlines()

	A__							:   str					       =		f"""class {class_name}("""
	A__							:   Optional[Any]					       =		f"""{4 * ' '}def {test_name}("""
	A__							:   Union[str, Any]					       =		f"""{8 * ' '}{correct_line.split()[0]}"""
	A__							:   Optional[int]					       =		f"""{16 * ' '}{correct_line.split()[0]}"""
	A__							:   int					       =		False
	A__							:   str					       =		False
	A__							:   Tuple					       =		False
	A__							:   Optional[int]					       =		False
	A__							:   Optional[Any]					       =		0
	A__							:   Dict					       =		0

	A__							:   List[str]					       =		[]
	for line in lines:
		if line.startswith(lowercase_ ):
			A__							:   Dict					       =		True
		elif in_class and line.startswith(lowercase_ ):
			A__							:   Optional[Any]					       =		True
		elif in_class and in_func and (line.startswith(lowercase_ ) or line.startswith(lowercase_ )):
			A__							:   Tuple					       =		len(line.split(correct_line.split()[0] )[0] )
			count += 1

			if count == done_test[_id]:
				A__							:   Any					       =		True

		if in_class and in_func and in_line:
			if ")" not in line:
				continue
			else:
				A__							:   Dict					       =		True

		if in_class and in_func and in_line and insert_line:
			new_lines.append(f"""{spaces * ' '}{correct_line}""" )
			A__							:   List[str]					       =		False
		else:
			new_lines.append(lowercase_ )

	with open(lowercase_   ,				"""w""" ) as f:
		for line in new_lines:
			f.write(lowercase_ )

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[Any]=None )					->				Any:
	if fail is not None:
		with open(lowercase_   ,				"""r""" ) as f:
			A__							:   Dict					       =		{l.strip() for l in f.readlines()}
	else:
		A__							:   List[str]					       =		None

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   int					       =		f.readlines()

	A__							:   Union[str, Any]					       =		defaultdict(lowercase_ )
	for line in correct_lines:
		A__       ,       A__       ,       A__       ,       A__							:   Optional[int]					       =		line.split(""";""" )
		if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
			overwrite_file(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )


if __name__ == "__main__":
	A_	:			Optional[Any]  	=      argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	A_	:			Optional[Any]  	=      parser.parse_args()

	main(args.correct_filename, args.fail_filename)



