





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_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	['''image_processor''', '''tokenizer''']
					lowerCAmelCase_							=	'''LayoutLMv2ImageProcessor'''
					lowerCAmelCase_							=	('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')




					def __init__(		self				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None				,						**lowerCAmelCase_  )		->							List[Any]:
										if "feature_extractor" in kwargs:
															warnings.warn(
															    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
															    ' instead.'				,						lowerCAmelCase_				,						)
															_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__(lowerCAmelCase_				,						lowerCAmelCase_  )




					def __call__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = 0				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							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=lowerCAmelCase_				,						return_tensors=lowerCAmelCase_  )

										# second, apply the tokenizer
										if text is not None and self.image_processor.apply_ocr and text_pair is None:
															if isinstance(lowerCAmelCase_				,						lowerCAmelCase_  ):
																				_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=lowerCAmelCase_				,						add_special_tokens=lowerCAmelCase_				,						padding=lowerCAmelCase_				,						truncation=lowerCAmelCase_				,						max_length=lowerCAmelCase_				,						stride=lowerCAmelCase_				,						pad_to_multiple_of=lowerCAmelCase_				,						return_token_type_ids=lowerCAmelCase_				,						return_attention_mask=lowerCAmelCase_				,						return_overflowing_tokens=lowerCAmelCase_				,						return_special_tokens_mask=lowerCAmelCase_				,						return_offsets_mapping=lowerCAmelCase_				,						return_length=lowerCAmelCase_				,						verbose=lowerCAmelCase_				,						return_tensors=lowerCAmelCase_				,						**lowerCAmelCase_				,						)

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

										return encoded_inputs




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							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(lowerCAmelCase_  ) != len(lowerCAmelCase_  ):
															raise ValueError(
															    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
															    F''' {len(lowerCAmelCase_  )} and {len(lowerCAmelCase_  )}'''  )

										return images_with_overflow




					def 	lowerCAmelCase			(		self				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							List[str]:
										return self.tokenizer.batch_decode(*lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							str:
										return self.tokenizer.decode(*lowerCAmelCase_				,						**lowerCAmelCase_  )




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




					@property
					def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										warnings.warn(
										    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,						lowerCAmelCase_				,						)
										return self.image_processor_class






					@property
					def 	lowerCAmelCase			(		self  )		->							List[str]:
										warnings.warn(
										    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,						lowerCAmelCase_				,						)
										return self.image_processor










def 						lowerCamelCase__     (     UpperCamelCase__			:       List[Any]       ,    UpperCamelCase__			:       Dict       ,    UpperCamelCase__			:       str       ,    UpperCamelCase__			:       int       ,    UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       str						)			-> List[Any]:



					'''simple docstring'''




					if index == r:
										for j in range(UpperCamelCase__						):
															print(data[j]       ,    end=' '						)
										print(' '						)
										return
					#  When no more elements are there to put in data[]
					if i >= n:
										return
					# current is included, put next at next location
					_snake_case									=				arr[i]
					combination_util(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    index + 1       ,    UpperCamelCase__       ,    i + 1						)
					# current is excluded, replace it with
					# next (Note that i+1 is passed, but
					# index is not changed)
					combination_util(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    i + 1						)
					# The main function that prints all combinations
					# of size r in arr[] of size n. This function
					# mainly uses combinationUtil()





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[int]       ,    UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       List[str]						)			-> Tuple:



					'''simple docstring'''




					_snake_case									=				[0] * r
					# Print all combination using temporary array 'data[]'
					combination_util(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    0       ,    UpperCamelCase__       ,    0						)


if __name__ == "__main__":
					# Driver code to check the function above
					UpperCAmelCase_      							=     [10, 20, 30, 40, 50]
					print_combination(arr, len(arr), 3)
					# This code is contributed by Ambuj sahu



