



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  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		str      							=     ["image_processor", "tokenizer"]
					A__					:		str      							=     "LayoutLMv3ImageProcessor"
					A__					:		Tuple      							=     ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



					def __init__(  self       :							Optional[int]					,       _snake_case       :							Optional[int]=None					,       _snake_case       :							Optional[int]=None					,       **_snake_case       :							int       ):






								"""simple docstring"""


								A__	  =     None
								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					,       )
											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__(_snake_case					,       _snake_case       )



					def __call__(  self       :							Optional[int]					,       _snake_case       :							List[str]					,       _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       :							Optional[Any]					,       ):






								"""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.'       )

								# first, apply the image processor
								A__	  =     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       ):
														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=_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
								A__	  =     features.pop('pixel_values'       )
								if return_overflowing_tokens is True:
											A__	  =     self.get_overflowing_images(_snake_case					,       encoded_inputs['overflow_to_sample_mapping']       )
								A__	  =     images

								return encoded_inputs



					def      _a				(  self       :							int					,       _snake_case       :							List[str]					,       _snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     []
								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      _a				(  self       :							Union[str, Any]					,       *_snake_case       :							List[Any]					,       **_snake_case       :							Any       ):






								"""simple docstring"""


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



					def      _a				(  self       :							List[str]					,       *_snake_case       :							Optional[int]					,       **_snake_case       :							int       ):






								"""simple docstring"""


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



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






								"""simple docstring"""


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



					@property
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								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      _a				(  self       :							int       ):






								"""simple docstring"""


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









def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     0
			A__	  =     len(__UpperCamelCase       ) - 1

			while left <= right:
						# avoid divided by 0 during interpolation
						if sorted_collection[left] == sorted_collection[right]:
									if sorted_collection[left] == item:
												return left
									else:
												return None

						A__	  =     left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(__UpperCamelCase       ):
									return None

						A__	  =     sorted_collection[point]
						if current_item == item:
									return point
						else:
									if point < left:
												A__	  =     left
												A__	  =     point
									elif point > right:
												A__	  =     right
												A__	  =     point
									else:
												if item < current_item:
															A__	  =     point - 1
												else:
															A__	  =     point + 1
			return None


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			# avoid divided by 0 during interpolation
			if sorted_collection[left] == sorted_collection[right]:
						if sorted_collection[left] == item:
									return left
						else:
									return None

			A__	  =     left + ((item - sorted_collection[left]) * (right - left)) // (
			    sorted_collection[right] - sorted_collection[left]
			)

			# out of range check
			if point < 0 or point >= len(__UpperCamelCase       ):
						return None

			if sorted_collection[point] == item:
						return point
			elif point < left:
						return interpolation_search_by_recursion(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			elif point > right:
						return interpolation_search_by_recursion(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			else:
						if sorted_collection[point] > item:
									return interpolation_search_by_recursion(
									    __UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						point - 1       )
						else:
									return interpolation_search_by_recursion(
									    __UpperCamelCase			,						__UpperCamelCase			,						point + 1			,						__UpperCamelCase       )


def A    (							__UpperCamelCase       )    ->   List[str]:
			if collection != sorted(__UpperCamelCase       ):
						raise ValueError('Collection must be ascending sorted'       )
			return True


if __name__ == "__main__":
							import sys
							SCREAMING_SNAKE_CASE__   			=   0
							if debug == 1:
														SCREAMING_SNAKE_CASE__   			=   [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
														try:
																					__assert_sorted(collection)
														except ValueError:
																					sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
														SCREAMING_SNAKE_CASE__   			=   6_7

							SCREAMING_SNAKE_CASE__   			=   interpolation_search(collection, target)
							if result is not None:
														print(f'{target} found at positions: {result}')
							else:
														print('''Not found''')




