



"""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 				UpperCamelCase__       (     __SCREAMING_SNAKE_CASE  ):
      _SCREAMING_SNAKE_CASE      =					["image_processor", "tokenizer"]
      _SCREAMING_SNAKE_CASE      =					"LayoutLMv2ImageProcessor"
      _SCREAMING_SNAKE_CASE      =					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")



      def __init__(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				str=None   ,      SCREAMING_SNAKE_CASE_    :				List[str]=None   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'   ,      _snake_case   ,      )
          lowerCAmelCase_       :    List[str]								=			kwargs.pop('feature_extractor'	)

        lowerCAmelCase_       :    Tuple								=			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__(_snake_case   ,      _snake_case	)



      def __call__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any] = None   ,      SCREAMING_SNAKE_CASE_    :				List[Any] = None   ,      SCREAMING_SNAKE_CASE_    :				Dict = None   ,      SCREAMING_SNAKE_CASE_    :				str = None   ,      SCREAMING_SNAKE_CASE_    :				str = True   ,      SCREAMING_SNAKE_CASE_    :				List[str] = False   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any] = None   ,      SCREAMING_SNAKE_CASE_    :				List[Any] = 0   ,      SCREAMING_SNAKE_CASE_    :				Tuple = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any] = None   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any] = None   ,      SCREAMING_SNAKE_CASE_    :				str = False   ,      SCREAMING_SNAKE_CASE_    :				str = False   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any] = False   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any] = False   ,      SCREAMING_SNAKE_CASE_    :				List[str] = True   ,      SCREAMING_SNAKE_CASE_    :				List[str] = None   ,      **SCREAMING_SNAKE_CASE_    :				List[str]   ,      ):
        # 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
        lowerCAmelCase_       :    str								=			self.image_processor(images=_snake_case   ,      return_tensors=_snake_case	)

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

        lowerCAmelCase_       :    Union[str, 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=_snake_case   ,      add_special_tokens=_snake_case   ,      padding=_snake_case   ,      truncation=_snake_case   ,      max_length=_snake_case   ,      stride=_snake_case   ,      pad_to_multiple_of=_snake_case   ,      return_token_type_ids=_snake_case   ,      return_attention_mask=_snake_case   ,      return_overflowing_tokens=_snake_case   ,      return_special_tokens_mask=_snake_case   ,      return_offsets_mapping=_snake_case   ,      return_length=_snake_case   ,      verbose=_snake_case   ,      return_tensors=_snake_case   ,      **_snake_case   ,      )

        # add pixel values
        lowerCAmelCase_       :    Tuple								=			features.pop('pixel_values'	)
        if return_overflowing_tokens is True:
          lowerCAmelCase_       :    Optional[Any]								=			self.get_overflowing_images(_snake_case   ,      encoded_inputs['overflow_to_sample_mapping']	)
        lowerCAmelCase_       :    Optional[Any]								=			images

        return encoded_inputs



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Any	):
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        lowerCAmelCase_       :    Any								=			[]
        for sample_idx in overflow_to_sample_mapping:
          images_with_overflow.append(images[sample_idx]	)

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

        return images_with_overflow



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]   ,      *SCREAMING_SNAKE_CASE_    :				Any   ,      **SCREAMING_SNAKE_CASE_    :				List[Any]	):
        return self.tokenizer.batch_decode(*_snake_case   ,      **_snake_case	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      *SCREAMING_SNAKE_CASE_    :				str   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        return self.tokenizer.decode(*_snake_case   ,      **_snake_case	)



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        return ["input_ids", "bbox", "attention_mask", "image"]



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,      _snake_case   ,      )
        return self.image_processor_class





      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,      _snake_case   ,      )
        return self.image_processor










"""simple docstring"""







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase__		:    List[Any]				   =     logging.getLogger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""masked_bert"""



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_0_5_2_2   ,      SCREAMING_SNAKE_CASE_    :				int=7_6_8   ,      SCREAMING_SNAKE_CASE_    :				Tuple=1_2   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1_2   ,      SCREAMING_SNAKE_CASE_    :				Tuple=3_0_7_2   ,      SCREAMING_SNAKE_CASE_    :				List[str]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Tuple=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2   ,      SCREAMING_SNAKE_CASE_    :				Dict=0.02   ,      SCREAMING_SNAKE_CASE_    :				Any=1E-12   ,      SCREAMING_SNAKE_CASE_    :				List[str]=0   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="topK"   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="constant"   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.0   ,      **SCREAMING_SNAKE_CASE_    :				Any   ,      ):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			vocab_size
        lowerCAmelCase_       :    str								=			hidden_size
        lowerCAmelCase_       :    Optional[int]								=			num_hidden_layers
        lowerCAmelCase_       :    Dict								=			num_attention_heads
        lowerCAmelCase_       :    List[str]								=			hidden_act
        lowerCAmelCase_       :    List[Any]								=			intermediate_size
        lowerCAmelCase_       :    Any								=			hidden_dropout_prob
        lowerCAmelCase_       :    str								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Any								=			max_position_embeddings
        lowerCAmelCase_       :    Dict								=			type_vocab_size
        lowerCAmelCase_       :    Tuple								=			initializer_range
        lowerCAmelCase_       :    List[Any]								=			layer_norm_eps
        lowerCAmelCase_       :    str								=			pruning_method
        lowerCAmelCase_       :    Optional[Any]								=			mask_init
        lowerCAmelCase_       :    int								=			mask_scale





