






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


SCREAMING_SNAKE_CASE__		:							int 				=				logging.get_logger(__name__)







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  ['pixel_values']




     def __init__(				self	,						_lowerCAmelCase = True	,						_lowerCAmelCase = None	,						_lowerCAmelCase = PILImageResampling.BICUBIC	,						_lowerCAmelCase = True	,						_lowerCAmelCase = True	,						_lowerCAmelCase = 1 / 255	,						_lowerCAmelCase = None	,						_lowerCAmelCase = True	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						**_lowerCAmelCase	,						):
          super().__init__(**_lowerCAmelCase		)
          UpperCAmelCase__      :  Optional[Any]				      =			size if size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase__      :  str				      =			get_size_dict(_lowerCAmelCase		)
          UpperCAmelCase__      :  Union[str, Any]				      =			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase__      :  Any				      =			get_size_dict(_lowerCAmelCase	,						default_to_square=_lowerCAmelCase	,						param_name="""crop_size"""		)

          UpperCAmelCase__      :  Optional[int]				      =			do_resize
          UpperCAmelCase__      :  List[str]				      =			do_rescale
          UpperCAmelCase__      :  Tuple				      =			do_normalize
          UpperCAmelCase__      :  Tuple				      =			do_center_crop
          UpperCAmelCase__      :  List[str]				      =			crop_size
          UpperCAmelCase__      :  List[Any]				      =			size
          UpperCAmelCase__      :  Union[str, Any]				      =			resample
          UpperCAmelCase__      :  Union[str, Any]				      =			rescale_factor
          UpperCAmelCase__      :  Union[str, Any]				      =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          UpperCAmelCase__      :  List[Any]				      =			image_std if image_std is not None else IMAGENET_DEFAULT_STD




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = PILImageResampling.BILINEAR	,						_lowerCAmelCase = None	,						**_lowerCAmelCase	,						):
          UpperCAmelCase__      :  Union[str, Any]				      =			get_size_dict(_lowerCAmelCase		)

          if "shortest_edge" in size:
               UpperCAmelCase__      :  Tuple				      =			get_resize_output_image_size(_lowerCAmelCase	,						size=size["""shortest_edge"""]	,						default_to_square=_lowerCAmelCase		)
               # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
          elif "height" in size and "width" in size:
               UpperCAmelCase__      :  List[Any]				      =			(size["""height"""], size["""width"""])
          else:
               raise ValueError(f"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"		)
          return resize(_lowerCAmelCase	,						size=_lowerCAmelCase	,						resample=_lowerCAmelCase	,						data_format=_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						**_lowerCAmelCase	,						):
          UpperCAmelCase__      :  str				      =			get_size_dict(_lowerCAmelCase		)
          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(_lowerCAmelCase	,						size=(size["""height"""], size["""width"""])	,						data_format=_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						**_lowerCAmelCase		):
          return rescale(_lowerCAmelCase	,						scale=_lowerCAmelCase	,						data_format=_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						**_lowerCAmelCase	,						):
          return normalize(_lowerCAmelCase	,						mean=_lowerCAmelCase	,						std=_lowerCAmelCase	,						data_format=_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = ChannelDimension.FIRST	,						**_lowerCAmelCase	,						):
          UpperCAmelCase__      :  Union[str, Any]				      =			do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase__      :  int				      =			do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase__      :  Optional[Any]				      =			do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase__      :  Tuple				      =			do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase__      :  List[Any]				      =			crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase__      :  List[str]				      =			get_size_dict(_lowerCAmelCase	,						param_name="""crop_size"""	,						default_to_square=_lowerCAmelCase		)
          UpperCAmelCase__      :  str				      =			resample if resample is not None else self.resample
          UpperCAmelCase__      :  int				      =			rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase__      :  int				      =			image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase__      :  List[Any]				      =			image_std if image_std is not None else self.image_std

          UpperCAmelCase__      :  Optional[Any]				      =			size if size is not None else self.size
          UpperCAmelCase__      :  List[str]				      =			get_size_dict(_lowerCAmelCase		)

          if not is_batched(_lowerCAmelCase		):
               UpperCAmelCase__      :  Optional[int]				      =			[images]

          if not valid_images(_lowerCAmelCase		):
               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__      :  Tuple				      =			[to_numpy_array(_lowerCAmelCase		) for image in images]

          if do_resize:
               UpperCAmelCase__      :  Any				      =			[self.resize(image=_lowerCAmelCase	,						size=_lowerCAmelCase	,						resample=_lowerCAmelCase		) for image in images]

          if do_center_crop:
               UpperCAmelCase__      :  Tuple				      =			[self.center_crop(image=_lowerCAmelCase	,						size=_lowerCAmelCase		) for image in images]

          if do_rescale:
               UpperCAmelCase__      :  Union[str, Any]				      =			[self.rescale(image=_lowerCAmelCase	,						scale=_lowerCAmelCase		) for image in images]

          if do_normalize:
               UpperCAmelCase__      :  Optional[int]				      =			[self.normalize(image=_lowerCAmelCase	,						mean=_lowerCAmelCase	,						std=_lowerCAmelCase		) for image in images]

          UpperCAmelCase__      :  Tuple				      =			[to_channel_dimension_format(_lowerCAmelCase	,						_lowerCAmelCase		) for image in images]

          UpperCAmelCase__      :  Any				      =			{"""pixel_values""": images}
          return BatchFeature(data=_lowerCAmelCase	,						tensor_type=_lowerCAmelCase		)









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__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""image_processor""", """tokenizer"""]
       lowercase_						=    """LayoutLMv2ImageProcessor"""
       lowercase_						=    ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

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







             '''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.''' ,   lowercase__ ,   )
                   __A    =kwargs.pop('''feature_extractor'''     )

             __A    =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__(lowercase__ ,   lowercase__     )

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







             '''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    =self.image_processor(images=lowercase__ ,   return_tensors=lowercase__     )

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

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

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

             return encoded_inputs

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



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

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

             return images_with_overflow

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



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

       @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.''' ,   lowercase__ ,   )
             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.''' ,   lowercase__ ,   )
             return self.image_processor


