






'''simple docstring'''







import heapq
def 						A__			(				__lowerCAmelCase		:			dict						):
						lowerCamelCase__         =  []

						# 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
						lowerCamelCase__         =  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
												lowerCamelCase__         =  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]:
																								lowerCamelCase__         =  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()

							UpperCamelCase    :      Optional[int]  			=		{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)}')









'''simple docstring'''







import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf

							from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments




@require_tf
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								for model_result in results.values():
														for batch_size, sequence_length in zip(model_result["""bs"""]   ,model_result["""ss"""]       ):
																				lowerCamelCase__         =  model_result["""result"""][batch_size][sequence_length]
																				self.assertIsNotNone(_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,eager_mode=_lowerCAmelCase   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sgugger/tiny-distilbert-classification"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,only_pretrain_model=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,eager_mode=_lowerCAmelCase   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,[config]       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,[config]       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result       )
								self.check_results_dict_not_empty(results.memory_train_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,[config]       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result       )
								self.check_results_dict_not_empty(results.memory_train_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """patrickvonplaten/t5-tiny-random"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,configs=[config]       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""       )       ) == 0   ,"""Cannot do xla on CPU."""       )
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,use_xla=_lowerCAmelCase   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								with tempfile.TemporaryDirectory() as tmp_dir:
														lowerCamelCase__         =  TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]   ,inference=_lowerCAmelCase   ,save_to_csv=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,inference_time_csv_file=os.path.join(_lowerCAmelCase   ,"""inf_time.csv"""       )   ,inference_memory_csv_file=os.path.join(_lowerCAmelCase   ,"""inf_mem.csv"""       )   ,env_info_csv_file=os.path.join(_lowerCAmelCase   ,"""env.csv"""       )   ,multi_process=_lowerCAmelCase   ,)
														lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
														benchmark.run()
														self.assertTrue(Path(os.path.join(_lowerCAmelCase   ,"""inf_time.csv"""       )       ).exists()       )
														self.assertTrue(Path(os.path.join(_lowerCAmelCase   ,"""inf_mem.csv"""       )       ).exists()       )
														self.assertTrue(Path(os.path.join(_lowerCAmelCase   ,"""env.csv"""       )       ).exists()       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""

								def _check_summary_is_not_empty(_lowerCAmelCase       ):
														self.assertTrue(hasattr(_lowerCAmelCase   ,"""sequential"""       )       )
														self.assertTrue(hasattr(_lowerCAmelCase   ,"""cumulative"""       )       )
														self.assertTrue(hasattr(_lowerCAmelCase   ,"""current"""       )       )
														self.assertTrue(hasattr(_lowerCAmelCase   ,"""total"""       )       )

								with tempfile.TemporaryDirectory() as tmp_dir:
														lowerCamelCase__         =  TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,log_filename=os.path.join(_lowerCAmelCase   ,"""log.txt"""       )   ,log_print=_lowerCAmelCase   ,trace_memory_line_by_line=_lowerCAmelCase   ,eager_mode=_lowerCAmelCase   ,multi_process=_lowerCAmelCase   ,)
														lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
														lowerCamelCase__         =  benchmark.run()
														_check_summary_is_not_empty(result.inference_summary       )
														self.assertTrue(Path(os.path.join(_lowerCAmelCase   ,"""log.txt"""       )       ).exists()       )

