


"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import numpy
	import tensorflow as tf

	from transformers.models.esm.modeling_tf_esm import (
	    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFEsmForMaskedLM,
	    TFEsmForSequenceClassification,
	    TFEsmForTokenClassification,
	    TFEsmModel,
	)


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				parent
								SCREAMING_SNAKE_CASE__     							=				13
								SCREAMING_SNAKE_CASE__     							=				7
								SCREAMING_SNAKE_CASE__     							=				True
								SCREAMING_SNAKE_CASE__     							=				True
								SCREAMING_SNAKE_CASE__     							=				True
								SCREAMING_SNAKE_CASE__     							=				99
								SCREAMING_SNAKE_CASE__     							=				32
								SCREAMING_SNAKE_CASE__     							=				2
								SCREAMING_SNAKE_CASE__     							=				4
								SCREAMING_SNAKE_CASE__     							=				37
								SCREAMING_SNAKE_CASE__     							=				"gelu"
								SCREAMING_SNAKE_CASE__     							=				0.1
								SCREAMING_SNAKE_CASE__     							=				0.1
								SCREAMING_SNAKE_CASE__     							=				512
								SCREAMING_SNAKE_CASE__     							=				16
								SCREAMING_SNAKE_CASE__     							=				2
								SCREAMING_SNAKE_CASE__     							=				0.02
								SCREAMING_SNAKE_CASE__     							=				3
								SCREAMING_SNAKE_CASE__     							=				4
								SCREAMING_SNAKE_CASE__     							=				None



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size     )

								SCREAMING_SNAKE_CASE__     							=				None
								if self.use_input_mask:
												SCREAMING_SNAKE_CASE__     							=				random_attention_mask([self.batch_size, self.seq_length]     )

								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None
								if self.use_labels:
												SCREAMING_SNAKE_CASE__     							=				ids_tensor([self.batch_size]       ,		self.type_sequence_label_size     )
												SCREAMING_SNAKE_CASE__     							=				ids_tensor([self.batch_size, self.seq_length]       ,		self.num_labels     )
												SCREAMING_SNAKE_CASE__     							=				ids_tensor([self.batch_size]       ,		self.num_choices     )

								SCREAMING_SNAKE_CASE__     							=				EsmConfig(
								    vocab_size=self.vocab_size       ,		hidden_size=self.hidden_size       ,		num_hidden_layers=self.num_hidden_layers       ,		pad_token_id=1       ,		num_attention_heads=self.num_attention_heads       ,		intermediate_size=self.intermediate_size       ,		hidden_act=self.hidden_act       ,		hidden_dropout_prob=self.hidden_dropout_prob       ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,		max_position_embeddings=self.max_position_embeddings       ,		type_vocab_size=self.type_vocab_size       ,		initializer_range=self.initializer_range       ,		)

								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



				def   lowerCAmelCase__		(						self     ):
								(
								    (
								    SCREAMING_SNAKE_CASE__
								)							,      (
								    SCREAMING_SNAKE_CASE__
								)							,      (
								    SCREAMING_SNAKE_CASE__
								)							,      (
								    SCREAMING_SNAKE_CASE__
								)							,      (
								    SCREAMING_SNAKE_CASE__
								)							,      (
								    SCREAMING_SNAKE_CASE__
								)							,      
								)     							=				self.prepare_config_and_inputs()

								SCREAMING_SNAKE_CASE__     							=				True
								SCREAMING_SNAKE_CASE__     							=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
								SCREAMING_SNAKE_CASE__     							=				ids_tensor([self.batch_size, self.seq_length]       ,		vocab_size=2     )

								return (
								    config,
								    input_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				TFEsmModel(config=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				{"input_ids": input_ids, "attention_mask": input_mask}
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				[input_ids, input_mask]
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )

								self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				True

								SCREAMING_SNAKE_CASE__     							=				TFEsmModel(config=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				{
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								    "encoder_hidden_states": encoder_hidden_states,
								    "encoder_attention_mask": encoder_attention_mask,
								}
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				[input_ids, input_mask]
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		encoder_hidden_states=UpperCAmelCase__     )

								# Also check the case where encoder outputs are not passed
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__     )

								self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				TFEsmForMaskedLM(config=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				model([input_ids, input_mask]     )
								self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.num_labels
								SCREAMING_SNAKE_CASE__     							=				TFEsmForTokenClassification(config=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				{"input_ids": input_ids, "attention_mask": input_mask}
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )
								self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.num_labels)     )






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.prepare_config_and_inputs()
								(
								    (
								    SCREAMING_SNAKE_CASE__
								)							,      (
								    SCREAMING_SNAKE_CASE__
								)							,      (
								    SCREAMING_SNAKE_CASE__
								)							,      (
								    SCREAMING_SNAKE_CASE__
								)							,      (
								    SCREAMING_SNAKE_CASE__
								)							,      (
								    SCREAMING_SNAKE_CASE__
								)							,      
								)     							=				config_and_inputs
								SCREAMING_SNAKE_CASE__     							=				{"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_tf
class 			lowerCamelCase_		(			lowercase       ,  lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      int      							=					(
				    (
				        TFEsmModel,
				        TFEsmForMaskedLM,
				        TFEsmForSequenceClassification,
				        TFEsmForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowerCAmelCase      :      Any      							=					(
				    {
				        "feature-extraction": TFEsmModel,
				        "fill-mask": TFEsmForMaskedLM,
				        "text-classification": TFEsmForSequenceClassification,
				        "token-classification": TFEsmForTokenClassification,
				        "zero-shot": TFEsmForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				_lowerCAmelCase      :      List[Any]      							=					False
				_lowerCAmelCase      :      Union[str, Any]      							=					False



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				TFEsmModelTester(self     )
								SCREAMING_SNAKE_CASE__     							=				ConfigTester(self       ,		config_class=UpperCAmelCase__       ,		hidden_size=37     )



				def   lowerCAmelCase__		(						self     ):
								self.config_tester.run_common_tests()



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__     )



				@slow
				def   lowerCAmelCase__		(						self     ):
								for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												SCREAMING_SNAKE_CASE__     							=				TFEsmModel.from_pretrained(UpperCAmelCase__     )
												self.assertIsNotNone(UpperCAmelCase__     )



				@unittest.skip("Protein models do not support embedding resizing."     )
				def   lowerCAmelCase__		(						self     ):
								pass



				@unittest.skip("Protein models do not support embedding resizing."     )
				def   lowerCAmelCase__		(						self     ):
								pass






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												SCREAMING_SNAKE_CASE__     							=				model_class(UpperCAmelCase__     )
												assert isinstance(model.get_input_embeddings()       ,		tf.keras.layers.Layer     )
												if model_class is TFEsmForMaskedLM:
																# Output embedding test differs from the main test because they're a matrix, not a layer
																SCREAMING_SNAKE_CASE__     							=				model.get_bias()
																assert isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     )
																for k, v in name.items():
																				assert isinstance(UpperCAmelCase__       ,		tf.Variable     )
												else:
																SCREAMING_SNAKE_CASE__     							=				model.get_output_embeddings()
																assert x is None
																SCREAMING_SNAKE_CASE__     							=				model.get_bias()
																assert name is None


@require_tf
class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"     )

								SCREAMING_SNAKE_CASE__     							=				tf.constant([[0, 1, 2, 3, 4, 5]]     )
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )[0]
								SCREAMING_SNAKE_CASE__     							=				[1, 6, 33]
								self.assertEqual(list(output.numpy().shape     )       ,		UpperCAmelCase__     )
								# compare the actual values for a slice.
								SCREAMING_SNAKE_CASE__     							=				tf.constant(
								    [
								        [
								            [8.921_518, -10.589_814, -6.4_671_307],
								            [-6.3_967_156, -13.911_377, -1.1_211_915],
								            [-7.781_247, -13.951_557, -3.740_592],
								        ]
								    ]     )
								self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()       ,		expected_slice.numpy()       ,		atol=1e-2     )     )






				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"     )

								SCREAMING_SNAKE_CASE__     							=				tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]     )
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )[0]
								# compare the actual values for a slice.
								SCREAMING_SNAKE_CASE__     							=				tf.constant(
								    [
								        [
								            [0.14_443_092, 0.54_125_327, 0.3_247_739],
								            [0.30_340_484, 0.00_526_676, 0.31_077_722],
								            [0.32_278_043, -0.24_987_096, 0.3_414_628],
								        ]
								    ]     )
								self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()       ,		expected_slice.numpy()       ,		atol=1e-4     )     )




"""simple docstring"""






import heapq


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

				# 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
				SCREAMING_SNAKE_CASE__     							=				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
								SCREAMING_SNAKE_CASE__     							=				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]:
																SCREAMING_SNAKE_CASE__     							=				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()

	_lowerCamelCase     =  {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)}""")
