






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
				from ...tokenization_utils_base import TextInput
from ...utils import logging


A        =			logging.get_logger(__name__)

A        =			{'vocab_file': 'spiece.model'}

A        =			{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A        =			{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

A        =			'▁'


class 							__a							(  __A						):





		'''simple docstring'''



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


		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__="</s>" ,    UpperCamelCase__="<unk>" ,    UpperCamelCase__="<pad>" ,    UpperCamelCase__=100 ,    UpperCamelCase__=None ,    UpperCamelCase__ = None ,    UpperCamelCase__=True ,    **UpperCamelCase__ ,    ):
						# Add extra_ids to the special token list
						if extra_ids > 0 and additional_special_tokens is None:
										SCREAMING_SNAKE_CASE_   :	int													=   [F'''<extra_id_{i}>''' for i in range(UpperCamelCase__       )]
						elif extra_ids > 0 and additional_special_tokens is not None:
										# Check that we have the right number of extra_id special tokens
										SCREAMING_SNAKE_CASE_   :	int													=   len(set(filter(lambda UpperCamelCase__       : bool('extra_id' in str(UpperCamelCase__       )       ) ,    UpperCamelCase__       )       )       )
										if extra_tokens != extra_ids:
														raise ValueError(
														    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
														    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
														    ' tokens'       )
						if legacy:
										logger.warning_once(
										    F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
										    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   legacy
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    eos_token=UpperCamelCase__ ,    unk_token=UpperCamelCase__ ,    pad_token=UpperCamelCase__ ,    extra_ids=UpperCamelCase__ ,    additional_special_tokens=UpperCamelCase__ ,    sp_model_kwargs=self.sp_model_kwargs ,    legacy=UpperCamelCase__ ,    **UpperCamelCase__ ,    )

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   vocab_file
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   extra_ids

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(UpperCamelCase__       )


		@staticmethod
		def   __snake_case			(						UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
										SCREAMING_SNAKE_CASE_   :	Any													=   TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
										if init_max_model_length is not None and init_max_model_length != max_model_length:
														return init_max_model_length
										elif init_max_model_length is None:
														warnings.warn(
														    'This tokenizer was incorrectly instantiated with a model max length of'
														    F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
														    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
														    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
														    F''' {pretrained_model_name_or_path} automatically truncating your input to'''
														    F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
														    F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
														    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
														    ' instantiate this tokenizer with `model_max_length` set to your preferred value.' ,    UpperCamelCase__ ,    )

						return max_model_length


		@property
		def   __snake_case			(						self       ):
						return self.sp_model.get_piece_size() + self._extra_ids


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {self.convert_ids_to_tokens(UpperCamelCase__       ): i for i in range(self.vocab_size       )}
						vocab.update(self.added_tokens_encoder       )
						return vocab


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False       ):
						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=UpperCamelCase__ ,    token_ids_a=UpperCamelCase__ ,    already_has_special_tokens=UpperCamelCase__       )

						# normal case: some special tokens
						if token_ids_a is None:
										return ([0] * len(UpperCamelCase__       )) + [1]
						return ([0] * len(UpperCamelCase__       )) + [1] + ([0] * len(UpperCamelCase__       )) + [1]


		def   __snake_case			(						self       ):
						return list(
						    set(filter(lambda UpperCamelCase__       : bool(re.search(R'<extra_id_\d+>' ,    UpperCamelCase__       )       ) is not None ,    self.additional_special_tokens       )       )       )


		def   __snake_case			(						self       ):
						return [self._convert_token_to_id(UpperCamelCase__       ) for token in self.get_sentinel_tokens()]


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						if len(UpperCamelCase__       ) > 0 and token_ids[-1] == self.eos_token_id:
										warnings.warn(
										    F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
										    ' eos tokens being added.'       )
										return token_ids
						else:
										return token_ids + [self.eos_token_id]


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   [self.eos_token_id]

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


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self._add_eos_if_not_present(UpperCamelCase__       )
						if token_ids_a is None:
										return token_ids_a
						else:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   self._add_eos_if_not_present(UpperCamelCase__       )
										return token_ids_a + token_ids_a


		def __getstate__(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.__dict__.copy()
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   None
						return state


		def __setstate__(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   d

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

						SCREAMING_SNAKE_CASE_   :	Tuple													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(self.vocab_file       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    **UpperCamelCase__       ):
						# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
						# the beginning of the text
						if not self.legacy:
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   SPIECE_UNDERLINE + text.replace(UpperCamelCase__ ,    ' '       )
						return super().tokenize(UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    **UpperCamelCase__       ):
						if not self.legacy:
										SCREAMING_SNAKE_CASE_   :	int													=   text.startswith(UpperCamelCase__       )
										if is_first:
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   text[1:]

						SCREAMING_SNAKE_CASE_   :	str													=   self.sp_model.encode(UpperCamelCase__ ,    out_type=UpperCamelCase__       )

						if not self.legacy and not is_first and not text.startswith(' '       ) and tokens[0].startswith(UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Tuple													=   ([tokens[0][1:]] if len(tokens[0]       ) > 1 else []) + tokens[1:]
						return tokens


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						if token.startswith('<extra_id_'       ):
										SCREAMING_SNAKE_CASE_   :	str													=   re.match(R'<extra_id_(\d+)>' ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	List[str]													=   int(match.group(1       )       )
										return self.vocab_size - num - 1
						return self.sp_model.piece_to_id(UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						if index < self.sp_model.get_piece_size():
										SCREAMING_SNAKE_CASE_   :	Tuple													=   self.sp_model.IdToPiece(UpperCamelCase__       )
						else:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   F'''<extra_id_{self.vocab_size - 1 - index}>'''
						return token


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   []
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ''
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   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
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   True
														SCREAMING_SNAKE_CASE_   :	List[str]													=   []
										else:
														current_sub_tokens.append(UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   False
						out_string += self.sp_model.decode(UpperCamelCase__       )
						return out_string.strip()






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						if not os.path.isdir(UpperCamelCase__       ):
										logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
										return
						SCREAMING_SNAKE_CASE_   :	List[str]													=   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:
														SCREAMING_SNAKE_CASE_   :	Any													=   self.sp_model.serialized_model_proto()
														fi.write(UpperCamelCase__       )

						return (out_vocab_file,)







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        =			logging.get_logger(__name__)

A        =			{'vocab_file': 'spm_char.model'}

A        =			{
    '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        =			{
    'microsoft/speecht5_asr': 1024,
    'microsoft/speecht5_tts': 1024,
    'microsoft/speecht5_vc': 1024,
}


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				str				    =						VOCAB_FILES_NAMES
		UpperCAmelCase__      :				List[str]				    =						PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__      :				Optional[int]				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__      :				Dict				    =						["""input_ids""", """attention_mask"""]


		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__="<s>" ,    UpperCamelCase__="</s>" ,    UpperCamelCase__="<unk>" ,    UpperCamelCase__="<pad>" ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {} 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__ ,    )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   vocab_file

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(UpperCamelCase__       )


		@property
		def   __snake_case			(						self       ):
						return self.sp_model.get_piece_size()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {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       ):
						SCREAMING_SNAKE_CASE_   :	str													=   self.__dict__.copy()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   None
						return state


		def __setstate__(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   d

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

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(self.vocab_file       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.sp_model.encode(UpperCamelCase__ ,    out_type=UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.sp_model.piece_to_id(UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.sp_model.IdToPiece(UpperCamelCase__       )
						return token


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   []
						SCREAMING_SNAKE_CASE_   :	int													=   ''
						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
														SCREAMING_SNAKE_CASE_   :	Any													=   []
										else:
														current_sub_tokens.append(UpperCamelCase__       )
						out_string += self.sp_model.decode(UpperCamelCase__       )
						return out_string.strip()


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=None       ):
						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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False       ):
						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=UpperCamelCase__ ,    token_ids_a=UpperCamelCase__ ,    already_has_special_tokens=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [1]
						if token_ids_a is None:
										return ([0] * len(UpperCamelCase__       )) + suffix_ones
						return ([0] * len(UpperCamelCase__       )) + ([0] * len(UpperCamelCase__       )) + suffix_ones






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						if not os.path.isdir(UpperCamelCase__       ):
										logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
										return
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   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:
														SCREAMING_SNAKE_CASE_   :	Tuple													=   self.sp_model.serialized_model_proto()
														fi.write(UpperCamelCase__       )

						return (out_vocab_file,)