


"""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 			_UpperCAmelCase (							lowercase__				):






		'''simple docstring'''

		lowerCamelCase__				=['''image_processor''', '''tokenizer''']
		lowerCamelCase__				='''LayoutLMv2ImageProcessor'''
		lowerCamelCase__				=('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')



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





			'''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.'''				,       _a				,       )
				__snake_case	:						Union[str, Any]						=				kwargs.pop('''feature_extractor'''					)

			__snake_case	:						Dict						=				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_ = None				,       a_ = None				,       a_ = 0				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = False				,       a_ = False				,       a_ = False				,       a_ = False				,       a_ = True				,       a_ = None				,       **a_				,       ):





			'''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
			__snake_case	:						List[Any]						=				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					):
					__snake_case	:						Optional[Any]						=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
				__snake_case	:						Tuple						=				features['words']

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

			return encoded_inputs



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				[]
			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 				SCREAMING_SNAKE_CASE  (self				,       *a_				,       **a_					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       *a_				,       **a_					):





			'''simple docstring'''

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



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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







'''simple docstring'''







def 					UpperCAmelCase_		(__a      :    list ,	__a      :    list ,	__a      :    int ):






   """simple docstring"""

   _a				:	Optional[Any]			=   len(__a )
   _a				:	int			=   [[0] * n for i in range(__a )]
   for i in range(__a ):
      _a				:	Tuple			=   y_points[i]

   for i in range(2 ,	__a ):
      for j in range(__a ,	__a ):
         _a				:	Tuple			=   (
             (xa - x_points[j - i + 1]) * q[j][i - 1]
             - (xa - x_points[j]) * q[j - 1][i - 1]
         ) / (x_points[j] - x_points[j - i + 1])

   return [q[n - 1][n - 1], q]


if __name__ == "__main__":
  import doctest

  doctest.testmod()



