





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_					)						->							Optional[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_					)						->							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_					)						->							int:
										return self.tokenizer.batch_decode(*lowerCAmelCase_			,  **lowerCAmelCase_					)




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




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










import random





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    float    ,      UpperCamelCase__     :    bool = False  )     ->	dict:



					'''simple docstring'''




					_snake_case													=			{i: [] for i in range(UpperCamelCase__  )}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(UpperCamelCase__  )
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(UpperCamelCase__  ):
										for j in range(i + 1    ,      UpperCamelCase__  ):
															if random.random() < probability:
																				graph[i].append(UpperCamelCase__  )
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(UpperCamelCase__  )
					return graph





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	dict:



					'''simple docstring'''




					return {
					    i: [j for j in range(UpperCamelCase__  ) if i != j] for i in range(UpperCamelCase__  )
					}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



