





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_      							=     logging.get_logger(__name__)

UpperCAmelCase_      							=     {"""vocab_file""": """spm_char.model"""}

UpperCAmelCase_      							=     {
    """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_      							=     {
    """microsoft/speecht5_asr""": 1024,
    """microsoft/speecht5_tts""": 1024,
    """microsoft/speecht5_vc""": 1024,
}
class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	VOCAB_FILES_NAMES
					lowerCAmelCase_							=	PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_							=	['''input_ids''', '''attention_mask''']




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_="<s>"				,						lowerCAmelCase_="</s>"				,						lowerCAmelCase_="<unk>"				,						lowerCAmelCase_="<pad>"				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							None:
										_snake_case									=				{} if sp_model_kwargs is None else sp_model_kwargs

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

										_snake_case									=				vocab_file

										_snake_case									=				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
										self.sp_model.Load(lowerCAmelCase_  )




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




					def 	lowerCAmelCase			(		self  )		->							str:
										_snake_case									=				{self.convert_ids_to_tokens(lowerCAmelCase_  ): i for i in range(self.vocab_size  )}
										vocab.update(self.added_tokens_encoder  )
										return vocab




					def __getstate__(		self  )		->							Optional[int]:
										_snake_case									=				self.__dict__.copy()
										_snake_case									=				None
										return state




					def __setstate__(		self				,						lowerCAmelCase_  )		->							List[Any]:
										_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				,						lowerCAmelCase_  )		->							List[str]:
										return self.sp_model.encode(lowerCAmelCase_				,						out_type=lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							List[Any]:
										return self.sp_model.piece_to_id(lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Any:
										_snake_case									=				self.sp_model.IdToPiece(lowerCAmelCase_  )
										return token




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							str:
										_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(lowerCAmelCase_  ) + token
																				_snake_case									=				[]
															else:
																				current_sub_tokens.append(lowerCAmelCase_  )
										out_string += self.sp_model.decode(lowerCAmelCase_  )
										return out_string.strip()




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

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






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None  )		->							Tuple[str]:
										if not os.path.isdir(lowerCAmelCase_  ):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
															return
										_snake_case									=				os.path.join(
										    lowerCAmelCase_				,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

										if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowerCAmelCase_  ) and os.path.isfile(self.vocab_file  ):
															copyfile(self.vocab_file				,						lowerCAmelCase_  )
										elif not os.path.isfile(self.vocab_file  ):
															with open(lowerCAmelCase_				,						'wb'  ) as fi:
																				_snake_case									=				self.sp_model.serialized_model_proto()
																				fi.write(lowerCAmelCase_  )

										return (out_vocab_file,)










from typing import TYPE_CHECKING

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


UpperCAmelCase_      							=     {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """SwinForImageClassification""",
					    """SwinForMaskedImageModeling""",
					    """SwinModel""",
					    """SwinPreTrainedModel""",
					    """SwinBackbone""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFSwinForImageClassification""",
					    """TFSwinForMaskedImageModeling""",
					    """TFSwinModel""",
					    """TFSwinPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swin import (
										    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwinBackbone,
										    SwinForImageClassification,
										    SwinForMaskedImageModeling,
										    SwinModel,
										    SwinPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_swin import (
										    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSwinForImageClassification,
										    TFSwinForMaskedImageModeling,
										    TFSwinModel,
										    TFSwinPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase_      							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



