




'''simple docstring'''





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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowercase__								=						logging.get_logger(__name__)


if is_vision_available():
       import PIL






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''pixel_values''']






      def __init__(self			,	a__ = True			,	a__ = None			,	a__ = PILImageResampling.BICUBIC			,	a__ = True			,	a__ = None			,	a__ = True			,	a__ = 1 / 2_5_5			,	a__ = True			,	a__ = None			,	a__ = None			,	a__ = True			,	**a__			,	)							-> None:
         super().__init__(**a__       )
         SCREAMING_SNAKE_CASE__      :					str							    =			size if size is not None else {'''shortest_edge''': 2_2_4}
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			get_size_dict(a__			,	default_to_square=a__       )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         SCREAMING_SNAKE_CASE__      :					int							    =			get_size_dict(a__       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			do_resize
         SCREAMING_SNAKE_CASE__      :					int							    =			size
         SCREAMING_SNAKE_CASE__      :					Any							    =			resample
         SCREAMING_SNAKE_CASE__      :					str							    =			do_center_crop
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			crop_size
         SCREAMING_SNAKE_CASE__      :					int							    =			do_rescale
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			rescale_factor
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			do_normalize
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			image_std if image_std is not None else OPENAI_CLIP_STD
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			do_convert_rgb






      def  A__(self			,	a__			,	a__			,	a__ = PILImageResampling.BICUBIC			,	a__ = None			,	**a__			,	)							-> np.ndarray:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			get_size_dict(a__			,	default_to_square=a__       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			get_resize_output_image_size(
             a__			,	size=(size['''height'''], size['''width'''])			,	default_to_square=a__       )
         return resize(a__			,	size=a__			,	resample=a__			,	data_format=a__			,	**a__       )






      def  A__(self			,	a__			,	a__			,	a__ = None			,	**a__			,	)							-> np.ndarray:
         SCREAMING_SNAKE_CASE__      :					Any							    =			get_size_dict(a__       )
         return center_crop(a__			,	size=(size['''height'''], size['''width'''])			,	data_format=a__			,	**a__       )






      def  A__(self			,	a__			,	a__			,	a__ = None			,	**a__			,	)							-> Optional[Any]:
         return rescale(a__			,	scale=a__			,	data_format=a__			,	**a__       )






      def  A__(self			,	a__			,	a__			,	a__			,	a__ = None			,	**a__			,	)							-> np.ndarray:
         return normalize(a__			,	mean=a__			,	std=a__			,	data_format=a__			,	**a__       )






      def  A__(self			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = ChannelDimension.FIRST			,	**a__			,	)							-> PIL.Image.Image:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			do_resize if do_resize is not None else self.do_resize
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			size if size is not None else self.size
         SCREAMING_SNAKE_CASE__      :					str							    =			get_size_dict(a__			,	default_to_square=a__       )
         SCREAMING_SNAKE_CASE__      :					str							    =			resample if resample is not None else self.resample
         SCREAMING_SNAKE_CASE__      :					str							    =			do_center_crop if do_center_crop is not None else self.do_center_crop
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			crop_size if crop_size is not None else self.crop_size
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			get_size_dict(a__       )
         SCREAMING_SNAKE_CASE__      :					str							    =			do_rescale if do_rescale is not None else self.do_rescale
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			rescale_factor if rescale_factor is not None else self.rescale_factor
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			do_normalize if do_normalize is not None else self.do_normalize
         SCREAMING_SNAKE_CASE__      :					Dict							    =			image_mean if image_mean is not None else self.image_mean
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			image_std if image_std is not None else self.image_std
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			make_list_of_images(a__       )

         if not valid_images(a__       ):
            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.'''       )

         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.'''       )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			[convert_to_rgb(a__       ) for image in images]

         # All transformations expect numpy arrays.
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			[to_numpy_array(a__       ) for image in images]

         if do_resize:
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[self.resize(image=a__			,	size=a__			,	resample=a__       ) for image in images]

         if do_center_crop:
            SCREAMING_SNAKE_CASE__      :					int							    =			[self.center_crop(image=a__			,	size=a__       ) for image in images]

         if do_rescale:
            SCREAMING_SNAKE_CASE__      :					int							    =			[self.rescale(image=a__			,	scale=a__       ) for image in images]

         if do_normalize:
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			[self.normalize(image=a__			,	mean=a__			,	std=a__       ) for image in images]

         SCREAMING_SNAKE_CASE__      :					str							    =			[to_channel_dimension_format(a__			,	a__       ) for image in images]

         SCREAMING_SNAKE_CASE__      :					Dict							    =			{'''pixel_values''': images}
         return BatchFeature(data=a__			,	tensor_type=a__       )







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






      def __init__(self			,	a__=None			,	a__=None			,	**a__       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			None
         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__      :					List[Any]							    =			kwargs.pop('''feature_extractor'''       )

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			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			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = True			,	a__ = False			,	a__ = False			,	a__ = None			,	a__ = 0			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = True			,	a__ = None			,	**a__			,	)							-> 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
         SCREAMING_SNAKE_CASE__      :					str							    =			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__      :					Tuple							    =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
            SCREAMING_SNAKE_CASE__      :					Any							    =			features['''words''']

         SCREAMING_SNAKE_CASE__      :					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__      :					Tuple							    =			features.pop('''pixel_values'''       )
         if return_overflowing_tokens is True:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.get_overflowing_images(a__			,	encoded_inputs['''overflow_to_sample_mapping''']       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			images

         return encoded_inputs






      def  A__(self			,	a__			,	a__       )							-> str:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         SCREAMING_SNAKE_CASE__      :					int							    =			[]
         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  A__(self			,	*a__			,	**a__       )							-> List[Any]:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Tuple:
         return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






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

