






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


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

A__			:    Union[str, Any]         =				{'vocab_file': 'spm_char.model'}

A__			:    Dict         =				{
    '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',
    }
}

A__			:    Tuple         =				{
    'microsoft/speecht5_asr': 10_24,
    'microsoft/speecht5_tts': 10_24,
    'microsoft/speecht5_vc': 10_24,
}
class    _UpperCAmelCase      ( __A			):

				"""simple docstring"""




				lowercase__       							= VOCAB_FILES_NAMES
				lowercase__       							= PRETRAINED_VOCAB_FILES_MAP
				lowercase__       							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__       							= ["""input_ids""", """attention_mask"""]




				def __init__(  self		:					str,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					Any="<s>",						lowerCamelCase		:					List[str]="</s>",						lowerCamelCase		:					List[Any]="<unk>",						lowerCamelCase		:					Optional[int]="<pad>",						lowerCamelCase		:					Optional[Any] = None,						**lowerCamelCase		:					List[Any],						):

								'''simple docstring'''
								lowercase__     				=    {} if sp_model_kwargs is None else sp_model_kwargs

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

								lowercase__     				=    vocab_file

								lowercase__     				=    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(UpperCamelCase__						)




				@property
				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.sp_model.get_piece_size()




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    {self.convert_ids_to_tokens(UpperCamelCase__						): i for i in range(self.vocab_size						)}
								vocab.update(self.added_tokens_encoder						)
								return vocab




				def __getstate__(  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.__dict__.copy()
								lowercase__     				=    None
								return state




				def __setstate__(  self		:					Any,						lowerCamelCase		:					Any						):

								'''simple docstring'''
								lowercase__     				=    d

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

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




				def        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					Dict						):

								'''simple docstring'''
								return self.sp_model.encode(UpperCamelCase__,						out_type=UpperCamelCase__						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					Dict						):

								'''simple docstring'''
								return self.sp_model.piece_to_id(UpperCamelCase__						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.sp_model.IdToPiece(UpperCamelCase__						)
								return token




				def        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    []
								lowercase__     				=    ''
								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(UpperCamelCase__						) + token
																lowercase__     				=    []
												else:
																current_sub_tokens.append(UpperCamelCase__						)
								out_string += self.sp_model.decode(UpperCamelCase__						)
								return out_string.strip()




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					Dict,						lowerCamelCase		:					Union[str, Any]=None						):

								'''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        lowercase__     (  self		:					Any,						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					List[str] = None,						lowerCamelCase		:					Dict = False						):

								'''simple docstring'''
								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=UpperCamelCase__,						token_ids_a=UpperCamelCase__,						already_has_special_tokens=UpperCamelCase__						)

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




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					str = None						):

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

								return (out_vocab_file,)












from itertools import count






def 						a    (		lowerCamelCase_ = 50       ):






				'''simple docstring'''
				lowercase__     				=    [1] * min_block_length

				for n in count(lowerCamelCase_       ):
								fill_count_functions.append(1       )

								for block_length in range(lowerCamelCase_	,     n + 1       ):
												for block_start in range(n - block_length       ):
																fill_count_functions[n] += fill_count_functions[
																    n - block_start - block_length - 1
																]

												fill_count_functions[n] += 1

								if fill_count_functions[n] > 100_0000:
												break

				return n


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




