
"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{'''vocab_file''': '''sentencepiece.model'''}

_lowercase							   =				{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

_lowercase							   =				{
    '''google/rembert''': 2_56,
}







class 				__lowercase				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[int]      =    VOCAB_FILES_NAMES
						_lowerCamelCase:    List[Any]      =    PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:    Optional[int]      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


						def __init__( self     :      Dict  ,A_     :      List[Any]  ,A_     :      Any=False  ,A_     :      List[str]=True  ,A_     :      Tuple=True  ,A_     :      Optional[Any]="[CLS]"  ,A_     :      Any="[SEP]"  ,A_     :      Any="[UNK]"  ,A_     :      str="[SEP]"  ,A_     :      int="[PAD]"  ,A_     :      Optional[Any]="[CLS]"  ,A_     :      Dict="[MASK]"  ,**A_     :      Any  ,)     ->   Any:
							super().__init__(
							    do_lower_case=A_  ,remove_space=A_  ,keep_accents=A_  ,bos_token=A_  ,eos_token=A_  ,unk_token=A_  ,sep_token=A_  ,pad_token=A_  ,cls_token=A_  ,mask_token=A_  ,**A_  ,)

							A						   =   do_lower_case
							A						   =   remove_space
							A						   =   keep_accents
							A						   =   vocab_file

							A						   =   spm.SentencePieceProcessor()
							self.sp_model.Load(A_      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Optional[Any]:
							return len(self.sp_model      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Union[str, Any]:
							A						   =   {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     :      Dict      )     ->   Dict:
							A						   =   self.__dict__.copy()
							A						   =   None
							return state


						def __setstate__( self     :      Optional[Any]  ,A_     :      str      )     ->   Dict:
							A						   =   d
							A						   =   spm.SentencePieceProcessor()
							self.sp_model.Load(self.vocab_file      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      Optional[Any]  ,A_     :      Optional[Any]=False      )     ->   Optional[int]:
							A						   =   self.sp_model.EncodeAsPieces(A_      )
							return pieces


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      List[Any]      )     ->   Dict:
							return self.sp_model.PieceToId(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Any      )     ->   Optional[Any]:
							return self.sp_model.IdToPiece(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      Optional[Any]      )     ->   Optional[Any]:
							A						   =   self.sp_model.decode_pieces(A_      )
							return out_string


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      List[int]  ,A_     :      Optional[List[int]] = None      )     ->   List[int]:
							A						   =   [self.sep_token_id]
							A						   =   [self.cls_token_id]
							if token_ids_a is None:
								return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      List[int]  ,A_     :      Optional[List[int]] = None  ,A_     :      bool = False      )     ->   List[int]:

							if already_has_special_tokens:
								if token_ids_a is not None:
									raise ValueError(
									    'You should not supply a second sequence if the provided sequence of '
									    'ids is already formatted with special tokens for the model.'      )
								return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

							if token_ids_a is not None:
								return [1] + ([0] * len(A_      )) + [1] + ([0] * len(A_      )) + [1]
							return [1] + ([0] * len(A_      )) + [1]


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      List[int]  ,A_     :      Optional[List[int]] = None      )     ->   List[int]:
							A						   =   [self.sep_token_id]
							A						   =   [self.cls_token_id]

							if token_ids_a is None:
								return len(cls + token_ids_a + sep      ) * [0]
							return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      str  ,A_     :      Optional[str] = None      )     ->   Tuple[str]:
							if not os.path.isdir(A_      ):
								logger.error('Vocabulary path ({}) should be a directory'.format(A_      )      )
								return
							A						   =   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_      ):
								copyfile(self.vocab_file  ,A_      )

							return (out_vocab_file,)

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''CLIPTokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''CLIPFeatureExtractor''']
		_lowercase							   =				['''CLIPImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPModel''',
		    '''CLIPPreTrainedModel''',
		    '''CLIPTextModel''',
		    '''CLIPTextModelWithProjection''',
		    '''CLIPVisionModel''',
		    '''CLIPVisionModelWithProjection''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCLIPModel''',
		    '''TFCLIPPreTrainedModel''',
		    '''TFCLIPTextModel''',
		    '''TFCLIPVisionModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''FlaxCLIPModel''',
		    '''FlaxCLIPPreTrainedModel''',
		    '''FlaxCLIPTextModel''',
		    '''FlaxCLIPTextPreTrainedModel''',
		    '''FlaxCLIPVisionModel''',
		    '''FlaxCLIPVisionPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

		_lowercase							   =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)