





'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      List[str]						)	->	List[str]:
				return [
				    {
				        0: [1, 2],
				        1: [0, 2],
				        2: [0, 1, 3, 5],
				        3: [2, 4],
				        4: [3],
				        5: [2, 6, 8],
				        6: [5, 7],
				        7: [6, 8],
				        8: [5, 7],
				    },
				    {
				        0: [6],
				        1: [9],
				        2: [4, 5],
				        3: [4],
				        4: [2, 3],
				        5: [2],
				        6: [0, 7],
				        7: [6],
				        8: [],
				        9: [1],
				    },
				    {
				        0: [4],
				        1: [6],
				        2: [],
				        3: [5, 6, 7],
				        4: [0, 6],
				        5: [3, 8, 9],
				        6: [1, 3, 4, 7],
				        7: [3, 6, 8, 9],
				        8: [5, 7],
				        9: [5, 7],
				    },
				    {
				        0: [1, 3],
				        1: [0, 2, 4],
				        2: [1, 3, 4],
				        3: [0, 2, 4],
				        4: [1, 2, 3],
				    },
				][index]






def  _lowerCamelCase				( lowercase						:      dict[int, list[int]]						)	->	list[tuple[int, int]]:
				_a								= 0
				_a								= len(lowercase						)  # No of vertices in graph
				_a								= [0] * n
				_a								= [False] * n

				def dfs(lowercase						:      Optional[Any]    ,							lowercase						:      int    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Optional[Any]						):
								_a								= True
								_a								= id_
								id_ += 1
								for to in graph[at]:
												if to == parent:
																pass
												elif not visited[to]:
																dfs(lowercase    ,							lowercase    ,							lowercase    ,							id_						)
																_a								= min(low[at]    ,							low[to]						)
																if id_ <= low[to]:
																				bridges.append((at, to) if at < to else (to, at)						)
												else:
																# This edge is a back edge and cannot be a bridge
																_a								= min(low[at]    ,							low[to]						)

				_a								= []
				for i in range(lowercase						):
								if not visited[i]:
												dfs(lowercase    ,							-1    ,							lowercase    ,							id_						)
				return bridges


if __name__ == "__main__":
					import doctest

					doctest.testmod()







'''simple docstring'''







import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =['image_processor', 'tokenizer']
				__a            ='OwlViTImageProcessor'
				__a            =('CLIPTokenizer', 'CLIPTokenizerFast')




				def __init__(  self     :					List[Any]					,				__a     :					str=None					,				__a     :					List[str]=None					,				**__a     :					List[Any]					):
								_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."					,				__a					,				)
												_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__(__a					,				__a					)




				def __call__(  self     :					Union[str, Any]					,				__a     :					Any=None					,				__a     :					List[str]=None					,				__a     :					int=None					,				__a     :					Optional[int]="max_length"					,				__a     :					List[str]="np"					,				**__a     :					Any					):

								if text is None and query_images is None and images is None:
												raise ValueError(
												    "You have to specify at least one text or query image or image. All three cannot be none."					)

								if text is not None:
												if isinstance(__a					,				__a					) or (isinstance(__a					,				__a					) and not isinstance(text[0]					,				__a					)):
																_a								= [self.tokenizer(__a					,				padding=__a					,				return_tensors=__a					,				**__a					)]

												elif isinstance(__a					,				__a					) and isinstance(text[0]					,				__a					):
																_a								= []

																# Maximum number of queries across batch
																_a								= max([len(__a					) for t in text]					)

																# Pad all batch samples to max number of text queries
																for t in text:
																				if len(__a					) != max_num_queries:
																								_a								= t + [" "] * (max_num_queries - len(__a					))

																				_a								= self.tokenizer(__a					,				padding=__a					,				return_tensors=__a					,				**__a					)
																				encodings.append(__a					)
												else:
																raise TypeError("Input text should be a string, a list of strings or a nested list of strings"					)

												if return_tensors == "np":
																_a								= np.concatenate([encoding["input_ids"] for encoding in encodings]					,				axis=0					)
																_a								= np.concatenate([encoding["attention_mask"] for encoding in encodings]					,				axis=0					)

												elif return_tensors == "jax" and is_flax_available():
																import jax.numpy as jnp

																_a								= jnp.concatenate([encoding["input_ids"] for encoding in encodings]					,				axis=0					)
																_a								= jnp.concatenate([encoding["attention_mask"] for encoding in encodings]					,				axis=0					)

												elif return_tensors == "pt" and is_torch_available():
																import torch

																_a								= torch.cat([encoding["input_ids"] for encoding in encodings]					,				dim=0					)
																_a								= torch.cat([encoding["attention_mask"] for encoding in encodings]					,				dim=0					)

												elif return_tensors == "tf" and is_tf_available():
																import tensorflow as tf

																_a								= tf.stack([encoding["input_ids"] for encoding in encodings]					,				axis=0					)
																_a								= tf.stack([encoding["attention_mask"] for encoding in encodings]					,				axis=0					)

												else:
																raise ValueError("Target return tensor type could not be returned"					)

												_a								= BatchEncoding()
												_a								= input_ids
												_a								= attention_mask

								if query_images is not None:
												_a								= BatchEncoding()
												_a								= self.image_processor(
												    __a					,				return_tensors=__a					,				**__a					).pixel_values
												_a								= query_pixel_values

								if images is not None:
												_a								= self.image_processor(__a					,				return_tensors=__a					,				**__a					)

								if text is not None and images is not None:
												_a								= image_features.pixel_values
												return encoding
								elif query_images is not None and images is not None:
												_a								= image_features.pixel_values
												return encoding
								elif text is not None or query_images is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**__a					)					,				tensor_type=__a					)




				def   UpperCamelCase__   (  self     :					List[str]					,				*__a     :					Union[str, Any]					,				**__a     :					int					):
								return self.image_processor.post_process(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					Optional[int]					,				*__a     :					Optional[Any]					,				**__a     :					List[str]					):
								return self.image_processor.post_process_object_detection(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*__a     :					Dict					,				**__a     :					Union[str, Any]					):
								return self.image_processor.post_process_image_guided_detection(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					str					,				*__a     :					Tuple					,				**__a     :					Tuple					):
								return self.tokenizer.batch_decode(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					List[str]					,				*__a     :					List[Any]					,				**__a     :					Optional[int]					):
								return self.tokenizer.decode(*__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					List[str]					):
								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   UpperCamelCase__   (  self     :					str					):
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,				__a					,				)
								return self.image_processor
