




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


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{'vocab_file': 'sentencepiece.bpe.model'}

__UpperCAmelCase     							=						{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

__UpperCAmelCase     							=						{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

__UpperCAmelCase     							=						'▁'



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   List[str]							=  VOCAB_FILES_NAMES
		_snake_case   :   int							=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   str							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   List[str]							=  ['''input_ids''', '''attention_mask''']



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

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

							super().__init__(
							    bos_token=_UpperCamelCase    ,		eos_token=_UpperCamelCase    ,		unk_token=_UpperCamelCase    ,		sep_token=_UpperCamelCase    ,		cls_token=_UpperCamelCase    ,		pad_token=_UpperCamelCase    ,		mask_token=_UpperCamelCase    ,		sp_model_kwargs=self.sp_model_kwargs    ,		**_UpperCamelCase    ,		)

							UpperCAmelCase_   :				Dict						=						vocab_file
							UpperCAmelCase_   :				Tuple						=						spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(str(_UpperCamelCase       )       )

							UpperCAmelCase_   :				List[Any]						=						{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							UpperCAmelCase_   :				str						=						len(self.sp_model       ) - 1
							UpperCAmelCase_   :				Optional[int]						=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:

							if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCAmelCase_   :				int						=						[self.cls_token_id]
							UpperCAmelCase_   :				Tuple						=						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = False       )     ->							List[int]:
							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=_UpperCamelCase    ,		token_ids_a=_UpperCamelCase    ,		already_has_special_tokens=_UpperCamelCase       )

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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							UpperCAmelCase_   :				Tuple						=						[self.sep_token_id]
							UpperCAmelCase_   :				Any						=						[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  __UpperCAmelCase      (							self       )     ->							str:
							return len(self.sp_model       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Tuple						=						{self.convert_ids_to_tokens(_UpperCamelCase       ): i for i in range(self.vocab_size       )}
							vocab.update(self.added_tokens_encoder       )
							return vocab



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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							str:
							if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
							UpperCAmelCase_   :				Dict						=						self.sp_model.PieceToId(_UpperCamelCase       )

							return spm_id if spm_id else self.unk_token_id



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							str:
							if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				int						=						[]
							UpperCAmelCase_   :				Any						=						''
							UpperCAmelCase_   :				Dict						=						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(_UpperCamelCase       ) + token
																	UpperCAmelCase_   :				List[str]						=						True
																	UpperCAmelCase_   :				Any						=						[]
												else:
																	current_sub_tokens.append(_UpperCamelCase       )
																	UpperCAmelCase_   :				Optional[Any]						=						False
							out_string += self.sp_model.decode(_UpperCamelCase       )
							return out_string.strip()



		def __getstate__(							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				int						=						self.__dict__.copy()
							UpperCAmelCase_   :				int						=						None
							return state



		def __setstate__(							self    ,		_UpperCamelCase       )     ->							str:
							UpperCAmelCase_   :				List[str]						=						d

							# for backward compatibility
							if not hasattr(self    ,		'sp_model_kwargs'       ):
												UpperCAmelCase_   :				Union[str, Any]						=						{}

							UpperCAmelCase_   :				Any						=						spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(self.vocab_file       )



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

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(_UpperCamelCase       ) and os.path.isfile(self.vocab_file       ):
												copyfile(self.vocab_file    ,		_UpperCamelCase       )
							elif not os.path.isfile(self.vocab_file       ):
												with open(_UpperCamelCase    ,		'wb'       ) as fi:
																	UpperCAmelCase_   :				Optional[int]						=						self.sp_model.serialized_model_proto()
																	fi.write(_UpperCamelCase       )

							return (out_vocab_file,)














from collections import defaultdict
from math import ceil, sqrt




def 					lowercase__				(					__snake_case   :							int = 1_000_000					,	__snake_case   :							int = 10  ):


					'''simple docstring'''


					UpperCAmelCase_   :				defaultdict						=						defaultdict(__snake_case  )

					for outer_width in range(3					,	(t_limit // 4) + 2  ):
										if outer_width * outer_width > t_limit:
															UpperCAmelCase_   :				Union[str, Any]						=						max(
															    ceil(sqrt(outer_width * outer_width - t_limit  )  )					,	1  )
										else:
															UpperCAmelCase_   :				int						=						1

										hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

										for hole_width in range(__snake_case					,	outer_width - 1					,	2  ):
															count[outer_width * outer_width - hole_width * hole_width] += 1

					return sum(1 for n in count.values() if 1 <= n <= 10  )


if __name__ == "__main__":
		print(F'{solution() = }')






