





'''simple docstring'''



from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





				if isinstance(_lowerCamelCase       ,_lowerCamelCase	):
							__lowercase			  =							[label.strip() for label in labels.split(''','''	) if label.strip()]
				return labels


	def __call__(self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





				if len(_lowerCamelCase	) == 0 or len(_lowerCamelCase	) == 0:
							raise ValueError('''You must include at least one label and at least one sequence.'''	)
				if hypothesis_template.format(labels[0]	) == hypothesis_template:
							raise ValueError(
							    (
							        '''The provided hypothesis_template "{}" was not able to be formatted with the target labels. '''
							        '''Make sure the passed template includes formatting syntax such as {{}} where the label should go.'''
							    ).format(_lowerCamelCase	)	)

				if isinstance(_lowerCamelCase       ,_lowerCamelCase	):
							__lowercase			  =							[sequences]

				__lowercase			  =							[]
				for sequence in sequences:
							sequence_pairs.extend([[sequence, hypothesis_template.format(_lowerCamelCase	)] for label in labels]	)

				return sequence_pairs, sequences






@add_end_docstrings(lowerCAmelCase__  )
class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase=ZeroShotClassificationArgumentHandler()       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							Optional[int]:







				'''simple docstring'''





				__lowercase			  =							args_parser
				super().__init__(*_lowerCamelCase       ,**_lowerCamelCase	)
				if self.entailment_id == -1:
							logger.warning(
							    '''Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '''
							    '''-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.'''	)


	@property
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				for label, ind in self.model.config.labelaid.items():
							if label.lower().startswith('''entail'''	):
										return ind
				return -1


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase=TruncationStrategy.ONLY_FIRST       ,**_lowerCamelCase	)							->							int:







				'''simple docstring'''





				__lowercase			  =							self.framework
				if self.tokenizer.pad_token is None:
							# Override for tokenizers not supporting padding
							logger.error(
							    '''Tokenizer was not supporting padding necessary for zero-shot, attempting to use '''
							    ''' `pad_token=eos_token`'''	)
							__lowercase			  =							self.tokenizer.eos_token
				try:
							__lowercase			  =							self.tokenizer(
							    _lowerCamelCase       ,add_special_tokens=_lowerCamelCase       ,return_tensors=_lowerCamelCase       ,padding=_lowerCamelCase       ,truncation=_lowerCamelCase       ,)
				except Exception as e:
							if "too short" in str(_lowerCamelCase	):
										# tokenizers might yell that we want to truncate
										# to a value that is not even reached by the input.
										# In that case we don't want to truncate.
										# It seems there's not a really better way to catch that
										# exception.

										__lowercase			  =							self.tokenizer(
										    _lowerCamelCase       ,add_special_tokens=_lowerCamelCase       ,return_tensors=_lowerCamelCase       ,padding=_lowerCamelCase       ,truncation=TruncationStrategy.DO_NOT_TRUNCATE       ,)
							else:
										raise e

				return inputs


	def   _UpperCAmelCase       (self       ,**_lowerCamelCase	)							->							Any:







				'''simple docstring'''





				if kwargs.get('''multi_class'''       ,_lowerCamelCase	) is not None:
							__lowercase			  =							kwargs['''multi_class''']
							logger.warning(
							    '''The `multi_class` argument has been deprecated and renamed to `multi_label`. '''
							    '''`multi_class` will be removed in a future version of Transformers.'''	)
				__lowercase			  =							{}
				if "candidate_labels" in kwargs:
							__lowercase			  =							self._args_parser._parse_labels(kwargs['''candidate_labels''']	)
				if "hypothesis_template" in kwargs:
							__lowercase			  =							kwargs['''hypothesis_template''']

				__lowercase			  =							{}
				if "multi_label" in kwargs:
							__lowercase			  =							kwargs['''multi_label''']
				return preprocess_params, {}, postprocess_params


	def __call__(self       ,_lowerCamelCase       ,*_lowerCamelCase       ,**_lowerCamelCase       ,)							->							Optional[Any]:







				'''simple docstring'''





				if len(_lowerCamelCase	) == 0:
							pass
				elif len(_lowerCamelCase	) == 1 and "candidate_labels" not in kwargs:
							__lowercase			  =							args[0]
				else:
							raise ValueError(f"Unable to understand extra arguments {args}"	)

				return super().__call__(_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=None       ,_lowerCamelCase="This example is {}."	)							->							Dict:







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self._args_parser(_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)

				for i, (candidate_label, sequence_pair) in enumerate(zip(_lowerCamelCase       ,_lowerCamelCase	)	):
							__lowercase			  =							self._parse_and_tokenize([sequence_pair]	)

							yield {
							    "candidate_label": candidate_label,
							    "sequence": sequences[0],
							    "is_last": i == len(_lowerCamelCase	) - 1,
							    **model_input,
							}


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							inputs['''candidate_label''']
				__lowercase			  =							inputs['''sequence''']
				__lowercase			  =							{k: inputs[k] for k in self.tokenizer.model_input_names}
				__lowercase			  =							self.model(**_lowerCamelCase	)

				__lowercase			  =							{
				    '''candidate_label''': candidate_label,
				    '''sequence''': sequence,
				    '''is_last''': inputs['''is_last'''],
				    **outputs,
				}
				return model_outputs


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=False	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							[outputs['''candidate_label'''] for outputs in model_outputs]
				__lowercase			  =							[outputs['''sequence'''] for outputs in model_outputs]
				__lowercase			  =							np.concatenate([output['''logits'''].numpy() for output in model_outputs]	)
				__lowercase			  =							logits.shape[0]
				__lowercase			  =							len(_lowerCamelCase	)
				__lowercase			  =							N // n
				__lowercase			  =							logits.reshape((num_sequences, n, -1)	)

				if multi_label or len(_lowerCamelCase	) == 1:
							# softmax over the entailment vs. contradiction dim for each label independently
							__lowercase			  =							self.entailment_id
							__lowercase			  =							-1 if entailment_id == 0 else 0
							__lowercase			  =							reshaped_outputs[..., [contradiction_id, entailment_id]]
							__lowercase			  =							np.exp(_lowerCamelCase	) / np.exp(_lowerCamelCase	).sum(-1       ,keepdims=_lowerCamelCase	)
							__lowercase			  =							scores[..., 1]
				else:
							# softmax the "entailment" logits over all candidate labels
							__lowercase			  =							reshaped_outputs[..., self.entailment_id]
							__lowercase			  =							np.exp(_lowerCamelCase	) / np.exp(_lowerCamelCase	).sum(-1       ,keepdims=_lowerCamelCase	)

				__lowercase			  =							list(reversed(scores[0].argsort()	)	)
				return {
				    "sequence": sequences[0],
				    "labels": [candidate_labels[i] for i in top_inds],
				    "scores": scores[0, top_inds].tolist(),
				}












'''simple docstring'''



import heapq




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict			):
			__lowercase			  =							[]

			# for each node and his adjacency list add them and the rank of the node to queue
			# using heapq module the queue will be filled like a Priority Queue
			# heapq works with a min priority queue, so I used -1*len(v) to build it
			for key, value in graph.items():
						# O(log(n))
						heapq.heappush(lowerCamelCase_						,    [-1 * len(lowerCamelCase_			), (key, value)]			)

			# chosen_vertices = set of chosen vertices
			__lowercase			  =							set()

			# while queue isn't empty and there are still edges
			#   (queue[0][0] is the rank of the node with max rank)
			while queue and queue[0][0] != 0:
						# extract vertex with max rank from queue and add it to chosen_vertices
						__lowercase			  =							heapq.heappop(lowerCamelCase_			)[1][0]
						chosen_vertices.add(lowerCamelCase_			)

						# Remove all arcs adjacent to argmax
						for elem in queue:
									# if v haven't adjacent node, skip
									if elem[0] == 0:
												continue
									# if argmax is reachable from elem
									# remove argmax from elem's adjacent list and update his rank
									if argmax in elem[1][1]:
												__lowercase			  =							elem[1][1].index(lowerCamelCase_			)
												del elem[1][1][index]
												elem[0] += 1
        # re-order the queue
						heapq.heapify(lowerCamelCase_			)
			return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_SCREAMING_SNAKE_CASE   =      {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(f'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')





