

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        _A						(     A__):
  SCREAMING_SNAKE_CASE			:					Dict				=     ['''image_processor''', '''tokenizer''']
  SCREAMING_SNAKE_CASE			:					Any				=     '''LayoutLMv2ImageProcessor'''
  SCREAMING_SNAKE_CASE			:					Tuple				=     ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')

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





        """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.'       ,    _a       ,    )
              SCREAMING_SNAKE_CASE_ :     Tuple							=   kwargs.pop('feature_extractor'    )

        SCREAMING_SNAKE_CASE_ :     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__(_a       ,    _a    )

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





        """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
        SCREAMING_SNAKE_CASE_ :     int							=   self.image_processor(images=_a       ,    return_tensors=_a    )

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

        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   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=_a       ,    add_special_tokens=_a       ,    padding=_a       ,    truncation=_a       ,    max_length=_a       ,    stride=_a       ,    pad_to_multiple_of=_a       ,    return_token_type_ids=_a       ,    return_attention_mask=_a       ,    return_overflowing_tokens=_a       ,    return_special_tokens_mask=_a       ,    return_offsets_mapping=_a       ,    return_length=_a       ,    verbose=_a       ,    return_tensors=_a       ,    **_a       ,    )

        # add pixel values
        SCREAMING_SNAKE_CASE_ :     Any							=   features.pop('pixel_values'    )
        if return_overflowing_tokens is True:
              SCREAMING_SNAKE_CASE_ :     Dict							=   self.get_overflowing_images(_a       ,    encoded_inputs['overflow_to_sample_mapping']    )
        SCREAMING_SNAKE_CASE_ :     int							=   images

        return encoded_inputs

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   []
        for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]    )

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

        return images_with_overflow

  def        UpperCAmelCase			( self       ,    *_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







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

  def        UpperCAmelCase			( self       ,    *_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







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

  @property
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







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

  @property
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







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



  @property
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







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









'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase :      List[str]					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}







class 							A__    (       A__     ,   A__  ):
  A__		 =				'convnextv2'





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=3							,					_a      :	Any=4							,					_a      :	int=4							,					_a      :	Union[str, Any]=None							,					_a      :	List[str]=None							,					_a      :	Optional[Any]="gelu"							,					_a      :	Any=0.02							,					_a      :	Any=1e-12							,					_a      :	Tuple=0.0							,					_a      :	int=224							,					_a      :	Any=None							,					_a      :	Optional[int]=None							,					**_a      :	List[str]							,					)	->    Optional[Any]:





       '''simple docstring'''






       super().__init__(**_a )

       _SCREAMING_SNAKE_CASE							    =num_channels
       _SCREAMING_SNAKE_CASE							    =patch_size
       _SCREAMING_SNAKE_CASE							    =num_stages
       _SCREAMING_SNAKE_CASE							    =[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
       _SCREAMING_SNAKE_CASE							    =[3, 3, 9, 3] if depths is None else depths
       _SCREAMING_SNAKE_CASE							    =hidden_act
       _SCREAMING_SNAKE_CASE							    =initializer_range
       _SCREAMING_SNAKE_CASE							    =layer_norm_eps
       _SCREAMING_SNAKE_CASE							    =drop_path_rate
       _SCREAMING_SNAKE_CASE							    =image_size
       _SCREAMING_SNAKE_CASE							    =['stem'] + [f"stage{idx}" for idx in range(1							,					len(self.depths ) + 1 )]
       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =get_aligned_output_features_output_indices(
           out_features=_a							,					out_indices=_a							,					stage_names=self.stage_names )
