






'''simple docstring'''





import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__lowerCamelCase						:    str     				=  logging.get_logger(__name__)

__lowerCamelCase						:    str     				=  {"""vocab_file""": """vocab.txt"""}

__lowerCamelCase						:    Optional[int]     				=  {
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

__lowerCamelCase						:    Dict     				=  {
    """facebook/esm2_t6_8M_UR50D""": 1024,
    """facebook/esm2_t12_35M_UR50D""": 1024,
}



def        __snake_case						(__UpperCAmelCase    ):





	"""simple docstring"""
	with open(__lowerCAmelCase       ,	'''r'''    ) as f:
		lowerCamelCase_					:  Optional[int]						=    f.read().splitlines()
		return [l.strip() for l in lines]




class   lowerCAmelCase__ ( __UpperCamelCase  ):
			A     				= VOCAB_FILES_NAMES
			A     				= PRETRAINED_VOCAB_FILES_MAP
			A     				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A     				= ['input_ids', 'attention_mask']

			def __init__(					self					:		int     ,		UpperCamelCase_					:		Any     ,		UpperCamelCase_					:		Optional[Any]="<unk>"     ,		UpperCamelCase_					:		List[str]="<cls>"     ,		UpperCamelCase_					:		str="<pad>"     ,		UpperCamelCase_					:		List[str]="<mask>"     ,		UpperCamelCase_					:		Any="<eos>"     ,		**UpperCamelCase_					:		Union[str, Any]     ,		) ->  Dict:







				"""simple docstring"""






				super().__init__(**UpperCamelCase_	)
				lowerCamelCase_					:  Any						=    load_vocab_file(UpperCamelCase_	)
				lowerCamelCase_					:  str						=    dict(enumerate(self.all_tokens	)	)
				lowerCamelCase_					:  int						=    {tok: ind for ind, tok in enumerate(self.all_tokens	)}
				lowerCamelCase_					:  str						=    unk_token
				lowerCamelCase_					:  Optional[int]						=    cls_token
				lowerCamelCase_					:  str						=    pad_token
				lowerCamelCase_					:  List[str]						=    mask_token
				lowerCamelCase_					:  Tuple						=    eos_token
				lowerCamelCase_					:  Tuple						=    self.all_tokens
				self._create_trie(self.unique_no_split_tokens	)

			def 						__UpperCamelCase				(					self					:		Optional[int]     ,		UpperCamelCase_					:		Dict	) ->  Tuple:







				"""simple docstring"""






				return self._id_to_token.get(UpperCamelCase_     ,		self.unk_token	)

			def 						__UpperCamelCase				(					self					:		Optional[Any]     ,		UpperCamelCase_					:		List[str]	) ->  Any:







				"""simple docstring"""






				return self._token_to_id.get(UpperCamelCase_     ,		self._token_to_id.get(self.unk_token	)	)

			def 						__UpperCamelCase				(					self					:		List[str]     ,		UpperCamelCase_					:		List[Any]     ,		**UpperCamelCase_					:		List[str]	) ->  Optional[int]:







				"""simple docstring"""






				return text.split()

			def 						__UpperCamelCase				(					self					:		Optional[int]     ,		UpperCamelCase_					:		Union[str, Any]=False	) ->  Optional[Any]:







				"""simple docstring"""






				return len(self._id_to_token	)

			def 						__UpperCamelCase				(					self					:		Any	) ->  Tuple:







				"""simple docstring"""






				return {token: i for i, token in enumerate(self.all_tokens	)}

			def 						__UpperCamelCase				(					self					:		Optional[Any]     ,		UpperCamelCase_					:		int	) ->  List[Any]:







				"""simple docstring"""






				return self._token_to_id.get(UpperCamelCase_     ,		self._token_to_id.get(self.unk_token	)	)

			def 						__UpperCamelCase				(					self					:		Optional[Any]     ,		UpperCamelCase_					:		Tuple	) ->  Dict:







				"""simple docstring"""






				return self._id_to_token.get(UpperCamelCase_     ,		self.unk_token	)

			def 						__UpperCamelCase				(					self					:		Union[str, Any]     ,		UpperCamelCase_					:		Dict     ,		UpperCamelCase_					:		int = None	) ->  Dict:







				"""simple docstring"""






				lowerCamelCase_					:  List[Any]						=    [self.cls_token_id]
				lowerCamelCase_					:  Dict						=    [self.eos_token_id]  # No sep token in ESM vocabulary
				if token_ids_a is None:
					if self.eos_token_id is None:
						return cls + token_ids_a
					else:
						return cls + token_ids_a + sep
				elif self.eos_token_id is None:
					raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!'''	)
				return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

			def 						__UpperCamelCase				(					self					:		Any     ,		UpperCamelCase_					:		List[str]     ,		UpperCamelCase_					:		Any = None     ,		UpperCamelCase_					:		str = False	) ->  Optional[Any]:







				"""simple docstring"""






				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 token in self.all_special_ids else 0 for token in token_ids_a]
				lowerCamelCase_					:  str						=    [1] + ([0] * len(UpperCamelCase_	)) + [1]
				if token_ids_a is not None:
					mask += [0] * len(UpperCamelCase_	) + [1]
				return mask

			def 						__UpperCamelCase				(					self					:		int     ,		UpperCamelCase_					:		Union[str, Any]     ,		UpperCamelCase_					:		Union[str, Any]	) ->  Tuple:







				"""simple docstring"""






				lowerCamelCase_					:  str						=    os.path.join(UpperCamelCase_     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''	)
				with open(UpperCamelCase_     ,		'''w'''	) as f:
					f.write('''\n'''.join(self.all_tokens	)	)
				return (vocab_file,)

			@property
			def 						__UpperCamelCase				(					self					:		Optional[int]	) ->  str:







				"""simple docstring"""






				return self.get_vocab_size(with_added_tokens=UpperCamelCase_	)





			def 						__UpperCamelCase				(					self					:		List[Any]     ,		UpperCamelCase_					:		str     ,		UpperCamelCase_					:		Union[str, Any] = False	) ->  str:







				"""simple docstring"""






				return super()._add_tokens(UpperCamelCase_     ,		special_tokens=UpperCamelCase_	)








import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Optional[int]     						=  ['image_processor', 'tokenizer']
      _A    :		List[Any]     						=  'LayoutLMv2ImageProcessor'
      _A    :		Dict     						=  ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')
      def __init__(      self		,						lowerCamelCase=None		,						lowerCamelCase=None		,						**lowerCamelCase			):
          if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."		,						lowerCamelCase		,						)
              snake_case__    =					kwargs.pop("feature_extractor"			)

          snake_case__    =					image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError("You need to specify an `image_processor`."			)
          if tokenizer is None:
              raise ValueError("You need to specify a `tokenizer`."			)

          super().__init__(lowerCamelCase		,						lowerCamelCase			)
      def __call__(      self		,						lowerCamelCase		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = True		,						lowerCamelCase = False		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = 0		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = False		,						lowerCamelCase = False		,						lowerCamelCase = False		,						lowerCamelCase = False		,						lowerCamelCase = True		,						lowerCamelCase = None		,						**lowerCamelCase		,						):
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  "You cannot provide bounding boxes "
                  "if you initialized the image processor with apply_ocr set to True."			)

          if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."			)

          if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."			)

          # first, apply the image processor
          snake_case__    =					self.image_processor(images=lowerCamelCase		,						return_tensors=lowerCamelCase			)

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(lowerCamelCase		,						lowerCamelCase			):
                  snake_case__    =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
              snake_case__    =					features["words"]

          snake_case__    =					self.tokenizer(
              text=text if text is not None else features["words"]		,						text_pair=text_pair if text_pair is not None else None		,						boxes=boxes if boxes is not None else features["boxes"]		,						word_labels=lowerCamelCase		,						add_special_tokens=lowerCamelCase		,						padding=lowerCamelCase		,						truncation=lowerCamelCase		,						max_length=lowerCamelCase		,						stride=lowerCamelCase		,						pad_to_multiple_of=lowerCamelCase		,						return_token_type_ids=lowerCamelCase		,						return_attention_mask=lowerCamelCase		,						return_overflowing_tokens=lowerCamelCase		,						return_special_tokens_mask=lowerCamelCase		,						return_offsets_mapping=lowerCamelCase		,						return_length=lowerCamelCase		,						verbose=lowerCamelCase		,						return_tensors=lowerCamelCase		,						**lowerCamelCase		,						)

          # add pixel values
          snake_case__    =					features.pop("pixel_values"			)
          if return_overflowing_tokens is True:
              snake_case__    =					self.get_overflowing_images(lowerCamelCase		,						encoded_inputs["overflow_to_sample_mapping"]			)
          snake_case__    =					images

          return encoded_inputs
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase			):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          snake_case__    =					[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]			)

          if len(lowerCamelCase			) != len(lowerCamelCase			):
              raise ValueError(
                  "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                  F""" {len(lowerCamelCase			)} and {len(lowerCamelCase			)}"""			)

          return images_with_overflow
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          return self.tokenizer.batch_decode(*lowerCamelCase		,						**lowerCamelCase			)
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          return self.tokenizer.decode(*lowerCamelCase		,						**lowerCamelCase			)
      @property
      def A_      (      self			):
          return ["input_ids", "bbox", "attention_mask", "image"]
      @property
      def A_      (      self			):
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,						lowerCamelCase		,						)
          return self.image_processor_class



      @property
      def A_      (      self			):
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,						lowerCamelCase		,						)
          return self.image_processor



