






"""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 					a						( __snake_case		):
    SCREAMING_SNAKE_CASE						:					Optional[int]						=			["""image_processor""", """tokenizer"""]
    SCREAMING_SNAKE_CASE						:					int						=			"""LayoutLMv2ImageProcessor"""
    SCREAMING_SNAKE_CASE						:					Dict						=			("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")






    def __init__(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=None	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=None	,						**__SCREAMING_SNAKE_CASE					:							Tuple )					->		str:
      lowerCamelCase_   					=						None
      if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.'	,						__SCREAMING_SNAKE_CASE	,						)
        lowerCamelCase_   					=						kwargs.pop('feature_extractor' )

      lowerCamelCase_   					=						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__(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )






    def __call__(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							Any	,						__SCREAMING_SNAKE_CASE					:							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,						__SCREAMING_SNAKE_CASE					:							Union[List[List[int]], List[List[List[int]]]] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[Union[List[int], List[List[int]]]] = None	,						__SCREAMING_SNAKE_CASE					:							bool = True	,						__SCREAMING_SNAKE_CASE					:							Union[bool, str, PaddingStrategy] = False	,						__SCREAMING_SNAKE_CASE					:							Union[bool, str, TruncationStrategy] = False	,						__SCREAMING_SNAKE_CASE					:							Optional[int] = None	,						__SCREAMING_SNAKE_CASE					:							int = 0	,						__SCREAMING_SNAKE_CASE					:							Optional[int] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							bool = False	,						__SCREAMING_SNAKE_CASE					:							bool = False	,						__SCREAMING_SNAKE_CASE					:							bool = False	,						__SCREAMING_SNAKE_CASE					:							bool = False	,						__SCREAMING_SNAKE_CASE					:							bool = True	,						__SCREAMING_SNAKE_CASE					:							Optional[Union[str, TensorType]] = None	,						**__SCREAMING_SNAKE_CASE					:							List[Any]	,						)					->		BatchEncoding:
      # 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_   					=						self.image_processor(images=__SCREAMING_SNAKE_CASE	,						return_tensors=__SCREAMING_SNAKE_CASE )

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

      lowerCamelCase_   					=						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=__SCREAMING_SNAKE_CASE	,						add_special_tokens=__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						truncation=__SCREAMING_SNAKE_CASE	,						max_length=__SCREAMING_SNAKE_CASE	,						stride=__SCREAMING_SNAKE_CASE	,						pad_to_multiple_of=__SCREAMING_SNAKE_CASE	,						return_token_type_ids=__SCREAMING_SNAKE_CASE	,						return_attention_mask=__SCREAMING_SNAKE_CASE	,						return_overflowing_tokens=__SCREAMING_SNAKE_CASE	,						return_special_tokens_mask=__SCREAMING_SNAKE_CASE	,						return_offsets_mapping=__SCREAMING_SNAKE_CASE	,						return_length=__SCREAMING_SNAKE_CASE	,						verbose=__SCREAMING_SNAKE_CASE	,						return_tensors=__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE	,						)

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

      return encoded_inputs






    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							str	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		Union[str, Any]:
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      lowerCamelCase_   					=						[]
      for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx] )

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

      return images_with_overflow






    def        UpperCamelCase	(	self					:							List[Any]	,						*__SCREAMING_SNAKE_CASE					:							Dict	,						**__SCREAMING_SNAKE_CASE					:							Optional[int] )					->		int:
      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							List[str]	,						*__SCREAMING_SNAKE_CASE					:							Any	,						**__SCREAMING_SNAKE_CASE					:							List[Any] )					->		str:
      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )






    @property
    def        UpperCamelCase	(	self					:							str )					->		List[Any]:
      return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






    @property
    def        UpperCamelCase	(	self					:							Optional[Any] )					->		Dict:
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'	,						__SCREAMING_SNAKE_CASE	,						)
      return self.image_processor_class






    @property
    def        UpperCamelCase	(	self					:							str )					->		int:
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'	,						__SCREAMING_SNAKE_CASE	,						)
      return self.image_processor












"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE :    List[Any]    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :    str    =						{
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}






class 					a						( __snake_case		):
    SCREAMING_SNAKE_CASE						:					str						=			"""regnet"""
    SCREAMING_SNAKE_CASE						:					List[Any]						=			["""x""", """y"""]






    def __init__(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							Any=3	,						__SCREAMING_SNAKE_CASE					:							Any=32	,						__SCREAMING_SNAKE_CASE					:							List[str]=[128, 192, 512, 1088]	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=[2, 6, 12, 2]	,						__SCREAMING_SNAKE_CASE					:							str=64	,						__SCREAMING_SNAKE_CASE					:							List[Any]="y"	,						__SCREAMING_SNAKE_CASE					:							Optional[int]="relu"	,						**__SCREAMING_SNAKE_CASE					:							Union[str, Any]	,						)					->		Union[str, Any]:
      super().__init__(**__SCREAMING_SNAKE_CASE )
      if layer_type not in self.layer_types:
        raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types )}''' )
      lowerCamelCase_   					=						num_channels
      lowerCamelCase_   					=						embedding_size
      lowerCamelCase_   					=						hidden_sizes
      lowerCamelCase_   					=						depths
      lowerCamelCase_   					=						groups_width
      lowerCamelCase_   					=						layer_type
      lowerCamelCase_   					=						hidden_act
      # always downsample in the first stage
      lowerCamelCase_   					=						True




