





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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


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




class        SCREAMING_SNAKE_CASE__					(		A__ ):
 __SCREAMING_SNAKE_CASE							=      ['''pixel_values''']



 def __init__(				self,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = 0.9,__lowerCamelCase = PILImageResampling.BICUBIC,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = 1 / 255,__lowerCamelCase = True,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = None,**__lowerCamelCase,):
        super().__init__(**__snake_case					)
        A__		   =		size if size is not None else {'''shortest_edge''': 224}
        A__		   =		get_size_dict(__snake_case,default_to_square=__snake_case					)
        A__		   =		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        A__		   =		get_size_dict(__snake_case,param_name='''crop_size'''					)

        A__		   =		do_resize
        A__		   =		size
        A__		   =		crop_pct
        A__		   =		resample
        A__		   =		do_center_crop
        A__		   =		crop_size
        A__		   =		do_rescale
        A__		   =		rescale_factor
        A__		   =		do_normalize
        A__		   =		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        A__		   =		image_std if image_std is not None else IMAGENET_DEFAULT_STD



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = PILImageResampling.BICUBIC,__lowerCamelCase = None,**__lowerCamelCase,):
        A__		   =		get_size_dict(__snake_case,default_to_square=__snake_case					)
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
               raise ValueError(f"size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"					)
        if crop_pct is not None:
               if "shortest_edge" in size:
                      A__		   =		int(size['''shortest_edge'''] / crop_pct					)
               elif "height" in size and "width" in size:
                      if size["height"] == size["width"]:
                             A__		   =		int(size['''height'''] / crop_pct					)
                      else:
                             A__		   =		(int(size['''height'''] / crop_pct					), int(size['''width'''] / crop_pct					))
               else:
                      raise ValueError('''Invalid size for resize: {}'''.format(__snake_case					)					)

               A__		   =		get_resize_output_image_size(__snake_case,size=__snake_case,default_to_square=__snake_case					)
        else:
               if "shortest_edge" in size:
                      A__		   =		get_resize_output_image_size(__snake_case,size=size['''shortest_edge'''],default_to_square=__snake_case					)
               elif "height" in size and "width" in size:
                      A__		   =		(size['''height'''], size['''width'''])
               else:
                      raise ValueError('''Invalid size for resize: {}'''.format(__snake_case					)					)

        return resize(__snake_case,size=__snake_case,resample=__snake_case,data_format=__snake_case,**__snake_case					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,**__lowerCamelCase,):
        A__		   =		get_size_dict(__snake_case					)
        if "height" not in size or "width" not in size:
               raise ValueError(f"size must contain \'height\' and \'width\' as keys. Got {size.keys()}"					)
        return center_crop(__snake_case,size=(size['''height'''], size['''width''']),data_format=__snake_case,**__snake_case					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,**__lowerCamelCase,):
        return rescale(__snake_case,scale=__snake_case,data_format=__snake_case,**__snake_case					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,**__lowerCamelCase,):
        return normalize(__snake_case,mean=__snake_case,std=__snake_case,data_format=__snake_case,**__snake_case					)




 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 = None,__lowerCamelCase = ChannelDimension.FIRST,**__lowerCamelCase,):
        A__		   =		do_resize if do_resize is not None else self.do_resize
        A__		   =		crop_pct if crop_pct is not None else self.crop_pct
        A__		   =		resample if resample is not None else self.resample
        A__		   =		do_center_crop if do_center_crop is not None else self.do_center_crop
        A__		   =		do_rescale if do_rescale is not None else self.do_rescale
        A__		   =		rescale_factor if rescale_factor is not None else self.rescale_factor
        A__		   =		do_normalize if do_normalize is not None else self.do_normalize
        A__		   =		image_mean if image_mean is not None else self.image_mean
        A__		   =		image_std if image_std is not None else self.image_std

        A__		   =		size if size is not None else self.size
        A__		   =		get_size_dict(__snake_case,default_to_square=__snake_case					)
        A__		   =		crop_size if crop_size is not None else self.crop_size
        A__		   =		get_size_dict(__snake_case,param_name='''crop_size'''					)

        A__		   =		make_list_of_images(__snake_case					)

        if not valid_images(__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 or resample is None:
               raise ValueError('''Size and resample must be specified if do_resize is True.'''					)

        if do_center_crop and crop_pct is None:
               raise ValueError('''Crop_pct 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.'''					)

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

        if do_resize:
               A__		   =		[self.resize(image=__snake_case,size=__snake_case,crop_pct=__snake_case,resample=__snake_case					) for image in images]

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

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

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

        A__		   =		[to_channel_dimension_format(__snake_case,__snake_case					) for image in images]

        A__		   =		{'''pixel_values''': images}
        return BatchFeature(data=__snake_case,tensor_type=__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"""






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



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

        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__(__snake_case   ,       __snake_case						)



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

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

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

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

        return encoded_inputs



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

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

        return images_with_overflow



     def      lowerCAmelCase			(   self							:			int   ,       *__snake_case							:			Optional[int]   ,       **__snake_case							:			Tuple						)->	str:
        return self.tokenizer.batch_decode(*__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Dict   ,       *__snake_case							:			Tuple   ,       **__snake_case							:			Optional[int]						)->	List[Any]:
        return self.tokenizer.decode(*__snake_case   ,       **__snake_case						)



     @property
     def      lowerCAmelCase			(   self							:			str						)->	int:
        return ["input_ids", "bbox", "attention_mask", "image"]



     @property
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	List[str]:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,       __snake_case   ,       )
        return self.image_processor_class



     @property
     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Tuple:
        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,       __snake_case   ,       )
        return self.image_processor

