





'''simple docstring'''




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       (a__				):
     lowerCAmelCase						     =			['''image_processor''', '''tokenizer''']
     lowerCAmelCase						     =			'''LayoutLMv2ImageProcessor'''
     lowerCAmelCase						     =			('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')


     def __init__(		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase):

          '''simple docstring'''






          if "feature_extractor" in kwargs:
               warnings.warn(
                   'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                   ' instead.'		,      _UpperCAmelCase		,      )
               __A		:					str						=			kwargs.pop('feature_extractor')

          __A		:					Dict						=			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__(_UpperCAmelCase		,      _UpperCAmelCase)


     def __call__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = True		,      _UpperCAmelCase = False		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 0		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = False		,      _UpperCAmelCase = False		,      _UpperCAmelCase = False		,      _UpperCAmelCase = False		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          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
          __A		:					Dict						=			self.image_processor(images=_UpperCAmelCase		,      return_tensors=_UpperCAmelCase)

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					Optional[int]						=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
               __A		:					Dict						=			features['words']

          __A		:					Any						=			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=_UpperCAmelCase		,      add_special_tokens=_UpperCAmelCase		,      padding=_UpperCAmelCase		,      truncation=_UpperCAmelCase		,      max_length=_UpperCAmelCase		,      stride=_UpperCAmelCase		,      pad_to_multiple_of=_UpperCAmelCase		,      return_token_type_ids=_UpperCAmelCase		,      return_attention_mask=_UpperCAmelCase		,      return_overflowing_tokens=_UpperCAmelCase		,      return_special_tokens_mask=_UpperCAmelCase		,      return_offsets_mapping=_UpperCAmelCase		,      return_length=_UpperCAmelCase		,      verbose=_UpperCAmelCase		,      return_tensors=_UpperCAmelCase		,      **_UpperCAmelCase		,      )

          # add pixel values
          __A		:					Dict						=			features.pop('pixel_values')
          if return_overflowing_tokens is True:
               __A		:					str						=			self.get_overflowing_images(_UpperCAmelCase		,      encoded_inputs['overflow_to_sample_mapping'])
          __A		:					str						=			images

          return encoded_inputs


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Tuple						=			[]
          for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx])

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

          return images_with_overflow


     def 				SCREAMING_SNAKE_CASE    (		self		,      *_UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.batch_decode(*_UpperCAmelCase		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      *_UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.decode(*_UpperCAmelCase		,      **_UpperCAmelCase)


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return ["input_ids", "bbox", "attention_mask", "image"]


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,      _UpperCAmelCase		,      )
          return self.image_processor_class


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,      _UpperCAmelCase		,      )
          return self.image_processor






'''simple docstring'''




from ...configuration_utils import PretrainedConfig


lowercase__       : Any         =       {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''tapas'''


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=12		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1024		,      _UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10]		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=0		,      _UpperCAmelCase=10.0		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase="ratio"		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase		,      **_UpperCAmelCase)

          # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
          __A		:					Dict						=			vocab_size
          __A		:					Tuple						=			hidden_size
          __A		:					Any						=			num_hidden_layers
          __A		:					int						=			num_attention_heads
          __A		:					Tuple						=			hidden_act
          __A		:					Tuple						=			intermediate_size
          __A		:					List[Any]						=			hidden_dropout_prob
          __A		:					int						=			attention_probs_dropout_prob
          __A		:					List[str]						=			max_position_embeddings
          __A		:					Optional[int]						=			type_vocab_sizes
          __A		:					str						=			initializer_range
          __A		:					List[str]						=			layer_norm_eps

          # Fine-tuning task hyperparameters
          __A		:					List[str]						=			positive_label_weight
          __A		:					List[Any]						=			num_aggregation_labels
          __A		:					Optional[Any]						=			aggregation_loss_weight
          __A		:					Tuple						=			use_answer_as_supervision
          __A		:					List[str]						=			answer_loss_importance
          __A		:					Any						=			use_normalized_answer_loss
          __A		:					Any						=			huber_loss_delta
          __A		:					Union[str, Any]						=			temperature
          __A		:					Tuple						=			aggregation_temperature
          __A		:					Optional[Any]						=			use_gumbel_for_cells
          __A		:					List[str]						=			use_gumbel_for_aggregation
          __A		:					Tuple						=			average_approximation_function
          __A		:					List[str]						=			cell_selection_preference
          __A		:					Dict						=			answer_loss_cutoff
          __A		:					Union[str, Any]						=			max_num_rows
          __A		:					Optional[Any]						=			max_num_columns
          __A		:					int						=			average_logits_per_cell
          __A		:					Optional[Any]						=			select_one_column
          __A		:					int						=			allow_empty_column_selection
          __A		:					List[Any]						=			init_cell_selection_weights_to_zero
          __A		:					int						=			reset_position_index_per_cell
          __A		:					Union[str, Any]						=			disable_per_token_loss

          # Aggregation hyperparameters
          __A		:					Optional[Any]						=			aggregation_labels
          __A		:					List[str]						=			no_aggregation_label_index

          if isinstance(self.aggregation_labels		,      _UpperCAmelCase):
               __A		:					Optional[Any]						=			{int(_UpperCAmelCase): v for k, v in aggregation_labels.items()}