
"""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     _UpperCAmelCase(    lowerCamelCase	):
		lowercase__						=      ['image_processor', 'tokenizer']
		lowercase__						=      'OwlViTImageProcessor'
		lowercase__						=      ('CLIPTokenizer', 'CLIPTokenizerFast')



		def __init__(					self				,		__a=None				,		__a=None				,		**__a)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  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				,		)
												_UpperCamelCase							 =  kwargs.pop('''feature_extractor''')

							_UpperCamelCase							 =  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=None				,		__a=None				,		__a=None				,		__a="max_length"				,		__a="np"				,		**__a)       ->       List[str]:






							'''simple docstring'''

							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)):
																	_UpperCamelCase							 =  [self.tokenizer(__a				,		padding=__a				,		return_tensors=__a				,		**__a)]

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

																	# Maximum number of queries across batch
																	_UpperCamelCase							 =  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:
																											_UpperCamelCase							 =  t + [''' '''] * (max_num_queries - len(__a))

																						_UpperCamelCase							 =  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":
																	_UpperCamelCase							 =  np.concatenate([encoding['''input_ids'''] for encoding in encodings]				,		axis=0)
																	_UpperCamelCase							 =  np.concatenate([encoding['''attention_mask'''] for encoding in encodings]				,		axis=0)

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

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

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

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

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

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

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

												_UpperCamelCase							 =  BatchEncoding()
												_UpperCamelCase							 =  input_ids
												_UpperCamelCase							 =  attention_mask

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

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

							if text is not None and images is not None:
												_UpperCamelCase							 =  image_features.pixel_values
												return encoding
							elif query_images is not None and images is not None:
												_UpperCamelCase							 =  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				,		*__a				,		**__a)       ->       Any:






							'''simple docstring'''
							return self.image_processor.post_process(*__a				,		**__a)



		def 						UpperCAmelCase							(					self				,		*__a				,		**__a)       ->       List[Any]:






							'''simple docstring'''
							return self.image_processor.post_process_object_detection(*__a				,		**__a)



		def 						UpperCAmelCase							(					self				,		*__a				,		**__a)       ->       Tuple:






							'''simple docstring'''
							return self.image_processor.post_process_image_guided_detection(*__a				,		**__a)



		def 						UpperCAmelCase							(					self				,		*__a				,		**__a)       ->       Union[str, Any]:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a				,		**__a)



		def 						UpperCAmelCase							(					self				,		*__a				,		**__a)       ->       int:






							'''simple docstring'''
							return self.tokenizer.decode(*__a				,		**__a)



		@property
		def 						UpperCAmelCase							(					self)       ->       List[str]:






							'''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 						UpperCAmelCase							(					self)       ->       str:






							'''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"""





class     _UpperCAmelCase:



		def __init__(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  {}



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							print(self.vertex)
							for i in self.vertex:
												print(__a				,		''' -> '''				,		''' -> '''.join([str(__a) for j in self.vertex[i]]))



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# check if vertex is already present,
							if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__a)
							else:
												# else make a new vertex
												_UpperCamelCase							 =  [to_vertex]



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							# visited array for storing already visited nodes
							_UpperCamelCase							 =  [False] * len(self.vertex)

							# call the recursive helper function
							for i in range(len(self.vertex)):
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)




		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# mark start vertex as visited
							_UpperCamelCase							 =  True

							print(__a				,		end=''' ''')

							# Recur for all the vertices that are adjacent to this node
							for i in self.vertex:
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)


if __name__ == "__main__":
				_a														=		Graph()
				g.add_edge(0, 1)
				g.add_edge(0, 2)
				g.add_edge(1, 2)
				g.add_edge(2, 0)
				g.add_edge(2, 3)
				g.add_edge(3, 3)

				g.print_graph()
				print("""DFS:""")
				g.dfs()

				# OUTPUT:
				# 0  ->  1 -> 2
				# 1  ->  2
				# 2  ->  0 -> 3
				# 3  ->  3
				# DFS:
				#  0 1 2 3






