
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


UpperCamelCase_						:     Optional[int]	       =							logging.get_logger(__name__)

UpperCamelCase_						:     str	       =							{"""vocab_file""": """spm_char.model"""}

UpperCamelCase_						:     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""",
    }
}

UpperCamelCase_						:     Dict	       =							{
    """microsoft/speecht5_asr""": 1024,
    """microsoft/speecht5_tts""": 1024,
    """microsoft/speecht5_vc""": 1024,
}




class   __lowercase						(						__snake_case       ):
	_A       =   VOCAB_FILES_NAMES
	_A       =   PRETRAINED_VOCAB_FILES_MAP
	_A       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A       =   ["input_ids", "attention_mask"]






	def __init__(self				: List[Any]      ,	snake_case				: Any      ,	snake_case				: Dict="<s>"      ,	snake_case				: List[Any]="</s>"      ,	snake_case				: int="<unk>"      ,	snake_case				: List[Any]="<pad>"      ,	snake_case				: Optional[Dict[str, Any]] = None      ,	**snake_case				: Optional[int]      ,	) ->			None:
							_lowercase       :	Optional[Any]           =   {} if sp_model_kwargs is None else sp_model_kwargs

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

							_lowercase       :	Optional[Any]           =   vocab_file

							_lowercase       :	Any           =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(snake_case    )






	@property
	def       _a(self				: Any    ) ->			Optional[Any]:
							return self.sp_model.get_piece_size()






	def       _a(self				: Dict    ) ->			Any:
							_lowercase       :	Dict           =   {self.convert_ids_to_tokens(snake_case    ): i for i in range(self.vocab_size    )}
							vocab.update(self.added_tokens_encoder    )
							return vocab






	def __getstate__(self				: str    ) ->			Dict:
							_lowercase       :	int           =   self.__dict__.copy()
							_lowercase       :	Dict           =   None
							return state






	def __setstate__(self				: Optional[Any]      ,	snake_case				: Tuple    ) ->			str:
							_lowercase       :	Dict           =   d

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

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






	def       _a(self				: Tuple      ,	snake_case				: str    ) ->			List[str]:
							return self.sp_model.encode(snake_case      ,	out_type=snake_case    )






	def       _a(self				: Tuple      ,	snake_case				: List[str]    ) ->			Tuple:
							return self.sp_model.piece_to_id(snake_case    )






	def       _a(self				: Optional[int]      ,	snake_case				: Optional[int]    ) ->			Tuple:
							_lowercase       :	Optional[Any]           =   self.sp_model.IdToPiece(snake_case    )
							return token






	def       _a(self				: Union[str, Any]      ,	snake_case				: Dict    ) ->			Tuple:
							_lowercase       :	Union[str, Any]           =   []
							_lowercase       :	List[Any]           =   ""
							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(snake_case    ) + token
																			_lowercase       :	Any           =   []
													else:
																			current_sub_tokens.append(snake_case    )
							out_string += self.sp_model.decode(snake_case    )
							return out_string.strip()






	def       _a(self				: Dict      ,	snake_case				: Union[str, Any]      ,	snake_case				: int=None    ) ->			List[int]:
							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       _a(self				: str      ,	snake_case				: List[int]      ,	snake_case				: Optional[List[int]] = None      ,	snake_case				: bool = False    ) ->			List[int]:
							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=snake_case      ,	token_ids_a=snake_case      ,	already_has_special_tokens=snake_case    )

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






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

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

							return (out_vocab_file,)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_						:     int	       =							{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Any	       =							[
							    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AdaptiveEmbedding""",
							    """TransfoXLForSequenceClassification""",
							    """TransfoXLLMHeadModel""",
							    """TransfoXLModel""",
							    """TransfoXLPreTrainedModel""",
							    """load_tf_weights_in_transfo_xl""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Tuple	       =							[
							    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFAdaptiveEmbedding""",
							    """TFTransfoXLForSequenceClassification""",
							    """TFTransfoXLLMHeadModel""",
							    """TFTransfoXLMainLayer""",
							    """TFTransfoXLModel""",
							    """TFTransfoXLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
							from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_transfo_xl import (
														    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AdaptiveEmbedding,
														    TransfoXLForSequenceClassification,
														    TransfoXLLMHeadModel,
														    TransfoXLModel,
														    TransfoXLPreTrainedModel,
														    load_tf_weights_in_transfo_xl,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_transfo_xl import (
														    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAdaptiveEmbedding,
														    TFTransfoXLForSequenceClassification,
														    TFTransfoXLLMHeadModel,
														    TFTransfoXLMainLayer,
														    TFTransfoXLModel,
														    TFTransfoXLPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_						:     Optional[int]	       =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




