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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowerCAmelCase__					=				logging.get_logger(__name__)


if is_vision_available():
				import PIL
class        a_				(		SCREAMING_SNAKE_CASE		):






	'''simple docstring'''






	UpperCAmelCase_							=      ['pixel_values']




	def __init__(    self      :						Optional[int]    ,					lowercase__      :						bool = True    ,					lowercase__      :						Dict[str, int] = None    ,					lowercase__      :						PILImageResampling = PILImageResampling.BILINEAR    ,					lowercase__      :						bool = True    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = True    ,					lowercase__      :						bool = True    ,					lowercase__      :						Union[int, float] = 1 / 255    ,					lowercase__      :						bool = True    ,					lowercase__      :						Optional[Union[float, List[float]]] = None    ,					lowercase__      :						Optional[Union[float, List[float]]] = None    ,					**lowercase__      :						Union[str, Any]    ,					):







								'''simple docstring'''



								super().__init__(**lowercase__)

								lowerCAmelCase__  			=      size if size is not None else {'height': 2_560, 'width': 1_920}
								if isinstance(lowercase__    ,					(tuple, list)):
															# The previous feature extractor size parameter was in (width, height) format
															lowerCAmelCase__  			=      size[::-1]
								lowerCAmelCase__  			=      get_size_dict(lowercase__)

								lowerCAmelCase__  			=      do_resize
								lowerCAmelCase__  			=      size
								lowerCAmelCase__  			=      resample
								lowerCAmelCase__  			=      do_thumbnail
								lowerCAmelCase__  			=      do_align_long_axis
								lowerCAmelCase__  			=      do_pad
								lowerCAmelCase__  			=      do_rescale
								lowerCAmelCase__  			=      rescale_factor
								lowerCAmelCase__  			=      do_normalize
								lowerCAmelCase__  			=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								lowerCAmelCase__  			=      image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 	__snake_case		(    self      :						int    ,					lowercase__      :						np.ndarray    ,					lowercase__      :						Dict[str, int]    ,					lowercase__      :						Optional[Union[str, ChannelDimension]] = None):







								'''simple docstring'''



								lowerCAmelCase__			,      lowerCAmelCase__  			=      get_image_size(lowercase__)
								lowerCAmelCase__			,      lowerCAmelCase__  			=      size['height'], size['width']

								if (output_width < output_height and input_width > input_height) or (
								    output_width > output_height and input_width < input_height
								):
															lowerCAmelCase__  			=      np.rotaa(lowercase__    ,					3)

								if data_format is not None:
															lowerCAmelCase__  			=      to_channel_dimension_format(lowercase__    ,					lowercase__)

								return image




	def 	__snake_case		(    self      :						int    ,					lowercase__      :						np.ndarray    ,					lowercase__      :						Dict[str, int]    ,					lowercase__      :						bool = False    ,					lowercase__      :						Optional[Union[str, ChannelDimension]] = None    ,					):







								'''simple docstring'''



								lowerCAmelCase__			,      lowerCAmelCase__  			=      size['height'], size['width']
								lowerCAmelCase__			,      lowerCAmelCase__  			=      get_image_size(lowercase__)

								lowerCAmelCase__  			=      output_width - input_width
								lowerCAmelCase__  			=      output_height - input_height

								if random_padding:
															lowerCAmelCase__  			=      np.random.randint(low=0    ,					high=delta_height + 1)
															lowerCAmelCase__  			=      np.random.randint(low=0    ,					high=delta_width + 1)
								else:
															lowerCAmelCase__  			=      delta_height // 2
															lowerCAmelCase__  			=      delta_width // 2

								lowerCAmelCase__  			=      delta_height - pad_top
								lowerCAmelCase__  			=      delta_width - pad_left

								lowerCAmelCase__  			=      ((pad_top, pad_bottom), (pad_left, pad_right))
								return pad(lowercase__    ,					lowercase__    ,					data_format=lowercase__)




	def 	__snake_case		(    self      :						Dict    ,					*lowercase__      :						List[str]    ,					**lowercase__      :						str):







								'''simple docstring'''



								logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.')
								return self.pad_image(*lowercase__    ,					**lowercase__)




	def 	__snake_case		(    self      :						str    ,					lowercase__      :						np.ndarray    ,					lowercase__      :						Dict[str, int]    ,					lowercase__      :						PILImageResampling = PILImageResampling.BICUBIC    ,					lowercase__      :						Optional[Union[str, ChannelDimension]] = None    ,					**lowercase__      :						Dict    ,					):







								'''simple docstring'''



								lowerCAmelCase__			,      lowerCAmelCase__  			=      get_image_size(lowercase__)
								lowerCAmelCase__			,      lowerCAmelCase__  			=      size['height'], size['width']

								# We always resize to the smallest of either the input or output size.
								lowerCAmelCase__  			=      min(lowercase__    ,					lowercase__)
								lowerCAmelCase__  			=      min(lowercase__    ,					lowercase__)

								if height == input_height and width == input_width:
															return image

								if input_height > input_width:
															lowerCAmelCase__  			=      int(input_width * height / input_height)
								elif input_width > input_height:
															lowerCAmelCase__  			=      int(input_height * width / input_width)

								return resize(
								    lowercase__    ,					size=(height, width)    ,					resample=lowercase__    ,					reducing_gap=2.0    ,					data_format=lowercase__    ,					**lowercase__)




	def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						np.ndarray    ,					lowercase__      :						Dict[str, int]    ,					lowercase__      :						PILImageResampling = PILImageResampling.BICUBIC    ,					lowercase__      :						Optional[Union[str, ChannelDimension]] = None    ,					**lowercase__      :						Dict    ,					):







								'''simple docstring'''



								lowerCAmelCase__  			=      get_size_dict(lowercase__)
								lowerCAmelCase__  			=      min(size['height']    ,					size['width'])
								lowerCAmelCase__  			=      get_resize_output_image_size(lowercase__    ,					size=lowercase__    ,					default_to_square=lowercase__)
								lowerCAmelCase__  			=      resize(lowercase__    ,					size=lowercase__    ,					resample=lowercase__    ,					data_format=lowercase__    ,					**lowercase__)
								return resized_image




	def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						np.ndarray    ,					lowercase__      :						Union[int, float]    ,					lowercase__      :						Optional[Union[str, ChannelDimension]] = None    ,					**lowercase__      :						Optional[Any]    ,					):







								'''simple docstring'''



								return rescale(lowercase__    ,					scale=lowercase__    ,					data_format=lowercase__    ,					**lowercase__)




	def 	__snake_case		(    self      :						List[str]    ,					lowercase__      :						np.ndarray    ,					lowercase__      :						Union[float, List[float]]    ,					lowercase__      :						Union[float, List[float]]    ,					lowercase__      :						Optional[Union[str, ChannelDimension]] = None    ,					**lowercase__      :						Dict    ,					):







								'''simple docstring'''



								return normalize(lowercase__    ,					mean=lowercase__    ,					std=lowercase__    ,					data_format=lowercase__    ,					**lowercase__)




	def 	__snake_case		(    self      :						str    ,					lowercase__      :						ImageInput    ,					lowercase__      :						bool = None    ,					lowercase__      :						Dict[str, int] = None    ,					lowercase__      :						PILImageResampling = None    ,					lowercase__      :						bool = None    ,					lowercase__      :						bool = None    ,					lowercase__      :						bool = None    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = None    ,					lowercase__      :						float = None    ,					lowercase__      :						bool = None    ,					lowercase__      :						Optional[Union[float, List[float]]] = None    ,					lowercase__      :						Optional[Union[float, List[float]]] = None    ,					lowercase__      :						Optional[Union[str, TensorType]] = None    ,					lowercase__      :						Optional[ChannelDimension] = ChannelDimension.FIRST    ,					**lowercase__      :						Any    ,					):







								'''simple docstring'''



								lowerCAmelCase__  			=      do_resize if do_resize is not None else self.do_resize
								lowerCAmelCase__  			=      size if size is not None else self.size
								if isinstance(lowercase__    ,					(tuple, list)):
															# Previous feature extractor had size in (width, height) format
															lowerCAmelCase__  			=      size[::-1]
								lowerCAmelCase__  			=      get_size_dict(lowercase__)
								lowerCAmelCase__  			=      resample if resample is not None else self.resample
								lowerCAmelCase__  			=      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
								lowerCAmelCase__  			=      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
								lowerCAmelCase__  			=      do_pad if do_pad is not None else self.do_pad
								lowerCAmelCase__  			=      do_rescale if do_rescale is not None else self.do_rescale
								lowerCAmelCase__  			=      rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCAmelCase__  			=      do_normalize if do_normalize is not None else self.do_normalize
								lowerCAmelCase__  			=      image_mean if image_mean is not None else self.image_mean
								lowerCAmelCase__  			=      image_std if image_std is not None else self.image_std

								lowerCAmelCase__  			=      make_list_of_images(lowercase__)

								if not valid_images(lowercase__):
															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_rescale and rescale_factor is None:
															raise ValueError('Rescale factor must be specified if do_rescale is True.')

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

								if do_normalize and (image_mean is None or image_std is None):
															raise ValueError('Image mean and std must be specified if do_normalize is True.')

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

								if do_align_long_axis:
															lowerCAmelCase__  			=      [self.align_long_axis(lowercase__    ,					size=lowercase__) for image in images]

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

								if do_thumbnail:
															lowerCAmelCase__  			=      [self.thumbnail(image=lowercase__    ,					size=lowercase__) for image in images]

								if do_pad:
															lowerCAmelCase__  			=      [self.pad_image(image=lowercase__    ,					size=lowercase__    ,					random_padding=lowercase__) for image in images]

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

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

								lowerCAmelCase__  			=      [to_channel_dimension_format(lowercase__    ,					lowercase__) for image in images]

								lowerCAmelCase__  			=      {'pixel_values': images}
								return BatchFeature(data=lowercase__    ,					tensor_type=lowercase__)

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_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      ['image_processor', 'tokenizer']
 UpperCAmelCase_							=      'LayoutLMv2ImageProcessor'
 UpperCAmelCase_							=      ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')




 def __init__(    self      :						List[Any]    ,					lowercase__      :						List[Any]=None    ,					lowercase__      :						int=None    ,					**lowercase__      :						Optional[Any]):







        '''simple docstring'''



        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.'    ,					lowercase__    ,					)
               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__(lowercase__    ,					lowercase__)




 def __call__(    self      :						Any    ,					lowercase__      :						Union[str, Any]    ,					lowercase__      :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,					lowercase__      :						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,					lowercase__      :						Union[List[List[int]], List[List[List[int]]]] = None    ,					lowercase__      :						Optional[Union[List[int], List[List[int]]]] = None    ,					lowercase__      :						bool = True    ,					lowercase__      :						Union[bool, str, PaddingStrategy] = False    ,					lowercase__      :						Union[bool, str, TruncationStrategy] = False    ,					lowercase__      :						Optional[int] = None    ,					lowercase__      :						int = 0    ,					lowercase__      :						Optional[int] = None    ,					lowercase__      :						Optional[bool] = None    ,					lowercase__      :						Optional[bool] = None    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = True    ,					lowercase__      :						Optional[Union[str, TensorType]] = None    ,					**lowercase__      :						List[str]    ,					):







        '''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
        lowerCAmelCase__  			=      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__):
                      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=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
        lowerCAmelCase__  			=      features.pop('pixel_values')
        if return_overflowing_tokens is True:
               lowerCAmelCase__  			=      self.get_overflowing_images(lowercase__    ,					encoded_inputs['overflow_to_sample_mapping'])
        lowerCAmelCase__  			=      images

        return encoded_inputs




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      []
        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 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						Tuple    ,					**lowercase__      :						int):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						str    ,					**lowercase__      :						List[Any]):







        '''simple docstring'''



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




 @property
 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



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




 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''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 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



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