




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase     					=		logging.get_logger(__name__)



class      a	(  _A     ):

     '''simple docstring'''





     lowerCAmelCase							:			Dict  						=    ['pixel_values']
     def __init__(      self  :     Union[str, Any]   ,       __snake_case  :     bool = True   ,       __snake_case  :     Optional[Dict[str, int]] = None   ,       __snake_case  :     PILImageResampling = PILImageResampling.BICUBIC   ,       __snake_case  :     bool = True   ,       __snake_case  :     bool = True   ,       __snake_case  :     Union[int, float] = 1 / 2_55   ,       __snake_case  :     Dict[str, int] = None   ,       __snake_case  :     bool = True   ,       __snake_case  :     Optional[Union[float, List[float]]] = None   ,       __snake_case  :     Optional[Union[float, List[float]]] = None   ,       **__snake_case  :     Optional[Any]   ,       ):
         super().__init__(**__SCREAMING_SNAKE_CASE							)
         UpperCAmelCase_									=	size if size is not None else {'''height''': 2_24, '''width''': 2_24}
         UpperCAmelCase_									=	get_size_dict(__SCREAMING_SNAKE_CASE							)
         UpperCAmelCase_									=	crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
         UpperCAmelCase_									=	get_size_dict(__SCREAMING_SNAKE_CASE   ,       default_to_square=__SCREAMING_SNAKE_CASE   ,       param_name='''crop_size'''							)

         UpperCAmelCase_									=	do_resize
         UpperCAmelCase_									=	do_rescale
         UpperCAmelCase_									=	do_normalize
         UpperCAmelCase_									=	do_center_crop
         UpperCAmelCase_									=	crop_size
         UpperCAmelCase_									=	size
         UpperCAmelCase_									=	resample
         UpperCAmelCase_									=	rescale_factor
         UpperCAmelCase_									=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase_									=	image_std if image_std is not None else IMAGENET_DEFAULT_STD
     def     lowerCamelCase_  (      self  :     Any   ,       __snake_case  :     np.ndarray   ,       __snake_case  :     Dict[str, int]   ,       __snake_case  :     PILImageResampling = PILImageResampling.BILINEAR   ,       __snake_case  :     Optional[Union[str, ChannelDimension]] = None   ,       **__snake_case  :     str   ,       ):
         UpperCAmelCase_									=	get_size_dict(__SCREAMING_SNAKE_CASE							)

         if "shortest_edge" in size:
             UpperCAmelCase_									=	get_resize_output_image_size(__SCREAMING_SNAKE_CASE   ,       size=size['''shortest_edge''']   ,       default_to_square=__SCREAMING_SNAKE_CASE							)
             # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
             UpperCAmelCase_									=	(size['''height'''], size['''width'''])
         else:
             raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'							)
         return resize(__SCREAMING_SNAKE_CASE   ,       size=__SCREAMING_SNAKE_CASE   ,       resample=__SCREAMING_SNAKE_CASE   ,       data_format=__SCREAMING_SNAKE_CASE   ,       **__SCREAMING_SNAKE_CASE							)
     def     lowerCamelCase_  (      self  :     Tuple   ,       __snake_case  :     np.ndarray   ,       __snake_case  :     Dict[str, int]   ,       __snake_case  :     Optional[Union[str, ChannelDimension]] = None   ,       **__snake_case  :     Tuple   ,       ):
         UpperCAmelCase_									=	get_size_dict(__SCREAMING_SNAKE_CASE							)
         if "height" not in size or "width" not in size:
             raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'							)
         return center_crop(__SCREAMING_SNAKE_CASE   ,       size=(size['''height'''], size['''width'''])   ,       data_format=__SCREAMING_SNAKE_CASE   ,       **__SCREAMING_SNAKE_CASE							)
     def     lowerCamelCase_  (      self  :     int   ,       __snake_case  :     np.ndarray   ,       __snake_case  :     float   ,       __snake_case  :     Optional[Union[str, ChannelDimension]] = None   ,       **__snake_case  :     str							):
         return rescale(__SCREAMING_SNAKE_CASE   ,       scale=__SCREAMING_SNAKE_CASE   ,       data_format=__SCREAMING_SNAKE_CASE   ,       **__SCREAMING_SNAKE_CASE							)
     def     lowerCamelCase_  (      self  :     Union[str, Any]   ,       __snake_case  :     np.ndarray   ,       __snake_case  :     Union[float, List[float]]   ,       __snake_case  :     Union[float, List[float]]   ,       __snake_case  :     Optional[Union[str, ChannelDimension]] = None   ,       **__snake_case  :     Union[str, Any]   ,       ):
         return normalize(__SCREAMING_SNAKE_CASE   ,       mean=__SCREAMING_SNAKE_CASE   ,       std=__SCREAMING_SNAKE_CASE   ,       data_format=__SCREAMING_SNAKE_CASE   ,       **__SCREAMING_SNAKE_CASE							)




     def     lowerCamelCase_  (      self  :     str   ,       __snake_case  :     ImageInput   ,       __snake_case  :     Optional[bool] = None   ,       __snake_case  :     Dict[str, int] = None   ,       __snake_case  :     PILImageResampling = None   ,       __snake_case  :     bool = None   ,       __snake_case  :     int = None   ,       __snake_case  :     Optional[bool] = None   ,       __snake_case  :     Optional[float] = None   ,       __snake_case  :     Optional[bool] = None   ,       __snake_case  :     Optional[Union[float, List[float]]] = None   ,       __snake_case  :     Optional[Union[float, List[float]]] = None   ,       __snake_case  :     Optional[Union[str, TensorType]] = None   ,       __snake_case  :     Union[str, ChannelDimension] = ChannelDimension.FIRST   ,       **__snake_case  :     Tuple   ,       ):
         UpperCAmelCase_									=	do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_									=	do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_									=	do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_									=	do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_									=	crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_									=	get_size_dict(__SCREAMING_SNAKE_CASE   ,       param_name='''crop_size'''   ,       default_to_square=__SCREAMING_SNAKE_CASE							)
         UpperCAmelCase_									=	resample if resample is not None else self.resample
         UpperCAmelCase_									=	rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_									=	image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_									=	image_std if image_std is not None else self.image_std

         UpperCAmelCase_									=	size if size is not None else self.size
         UpperCAmelCase_									=	get_size_dict(__SCREAMING_SNAKE_CASE							)

         if not is_batched(__SCREAMING_SNAKE_CASE							):
             UpperCAmelCase_									=	[images]

         if not valid_images(__SCREAMING_SNAKE_CASE							):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

         if do_resize and size is None:
             raise ValueError('''Size must be specified if do_resize is True.'''							)

         if do_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.'''							)

         if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

         # All transformations expect numpy arrays.
         UpperCAmelCase_									=	[to_numpy_array(__SCREAMING_SNAKE_CASE							) for image in images]

         if do_resize:
             UpperCAmelCase_									=	[self.resize(image=__SCREAMING_SNAKE_CASE   ,       size=__SCREAMING_SNAKE_CASE   ,       resample=__SCREAMING_SNAKE_CASE							) for image in images]

         if do_center_crop:
             UpperCAmelCase_									=	[self.center_crop(image=__SCREAMING_SNAKE_CASE   ,       size=__SCREAMING_SNAKE_CASE							) for image in images]

         if do_rescale:
             UpperCAmelCase_									=	[self.rescale(image=__SCREAMING_SNAKE_CASE   ,       scale=__SCREAMING_SNAKE_CASE							) for image in images]

         if do_normalize:
             UpperCAmelCase_									=	[self.normalize(image=__SCREAMING_SNAKE_CASE   ,       mean=__SCREAMING_SNAKE_CASE   ,       std=__SCREAMING_SNAKE_CASE							) for image in images]

         UpperCAmelCase_									=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE							) for image in images]

         UpperCAmelCase_									=	{'''pixel_values''': images}
         return BatchFeature(data=__SCREAMING_SNAKE_CASE   ,       tensor_type=__SCREAMING_SNAKE_CASE							)








'''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 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "LayoutLMv2ImageProcessor"
 lowerCAmelCase__       = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
 def __init__(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					List[str]:


      """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."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_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__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__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] = None   ,				__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   :			Any   ,				)    ->					BatchEncoding:


      """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      = 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     ):
                __SCREAMING_SNAKE_CASE      = [text]  # add batch dimension (as the image processor always adds a batch dimension)
           __SCREAMING_SNAKE_CASE      = features["""words"""]

      __SCREAMING_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=__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
      __SCREAMING_SNAKE_CASE      = features.pop("""pixel_values"""     )
      if return_overflowing_tokens is True:
           __SCREAMING_SNAKE_CASE      = self.get_overflowing_images(__SCREAMING_SNAKE_CASE   ,				encoded_inputs["""overflow_to_sample_mapping"""]     )
      __SCREAMING_SNAKE_CASE      = images

      return encoded_inputs
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = []
      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   :			Optional[Any]   ,				*__SCREAMING_SNAKE_CASE   :			str   ,				**__SCREAMING_SNAKE_CASE   :			List[str]     )    ->					List[str]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				*__SCREAMING_SNAKE_CASE   :			Dict   ,				**__SCREAMING_SNAKE_CASE   :			Any     )    ->					Union[str, Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




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


      """simple docstring"""




      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   :			Optional[int]     )    ->					Any:


      """simple docstring"""




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






