




'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       int						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       Any						=			{"vocab_file": "spm_char.model"}

_SCREAMING_SNAKE_CASE							:       str						=			{
    "vocab_file": {
        "microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
        "microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
        "microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
    }
}

_SCREAMING_SNAKE_CASE							:       Any						=			{
    "microsoft/speecht5_asr": 1024,
    "microsoft/speecht5_tts": 1024,
    "microsoft/speecht5_vc": 1024,
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Any							=   VOCAB_FILES_NAMES
			lowerCAmelCase_					:				Any							=   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_					:				int							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_					:				List[str]							=   ["input_ids", "attention_mask"]




			def __init__( self    ,							a__    ,							a__="<s>"    ,							a__="</s>"    ,							a__="<unk>"    ,							a__="<pad>"    ,							a__ = None    ,							**a__    ,							)    ->    None:

										'''simple docstring'''




										snake_case_      =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=a__    ,							eos_token=a__    ,							unk_token=a__    ,							pad_token=a__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**a__    ,							)

										snake_case_      =				vocab_file

										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(a__							)




			@property
			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										return self.sp_model.get_piece_size()




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				{self.convert_ids_to_tokens(a__							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab




			def __getstate__( self							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				self.__dict__.copy()
										snake_case_      =				None
										return state




			def __setstate__( self    ,							a__							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				d

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

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




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[str]:

										'''simple docstring'''




										return self.sp_model.encode(a__    ,							out_type=a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Dict:

										'''simple docstring'''




										return self.sp_model.piece_to_id(a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    int:

										'''simple docstring'''




										snake_case_      =				self.sp_model.IdToPiece(a__							)
										return token




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    str:

										'''simple docstring'''




										snake_case_      =				[]
										snake_case_      =				""
										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(a__							) + token
																								snake_case_      =				[]
																	else:
																								current_sub_tokens.append(a__							)
										out_string += self.sp_model.decode(a__							)
										return out_string.strip()




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__=None							)    ->    List[int]:

										'''simple docstring'''




										if token_ids_a is None:
																	return token_ids_a + [self.eos_token_id]
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return token_ids_a + token_ids_a + [self.eos_token_id]




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None    ,							a__ = False							)    ->    List[int]:

										'''simple docstring'''




										if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=a__    ,							token_ids_a=a__    ,							already_has_special_tokens=a__							)

										snake_case_      =				[1]
										if token_ids_a is None:
																	return ([0] * len(a__							)) + suffix_ones
										return ([0] * len(a__							)) + ([0] * len(a__							)) + suffix_ones




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    Tuple[str]:

										'''simple docstring'''




										if not os.path.isdir(a__							):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																	return
										snake_case_      =				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:
																								snake_case_      =				self.sp_model.serialized_model_proto()
																								fi.write(a__							)

										return (out_vocab_file,)










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	int = 1_0_0_0_0_0_0					):

							'''simple docstring'''






							snake_case_      =				set(range(3							,    snake_case							,    2					)					)
							primes.add(2					)
							for p in range(3							,    snake_case							,    2					):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p							,    snake_case							,    snake_case					)					)					)

							snake_case_      =				[float(snake_case					) for n in range(limit + 1					)]

							for p in primes:
														for n in range(snake_case							,    limit + 1							,    snake_case					):
																					phi[n] *= 1 - 1 / p

							return int(sum(phi[2:]					)					)


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




