"""simple docstring"""


from collections import deque







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      int:
     snake_case_					       =					len(UpperCAmelCase					)
     snake_case_					       =					deque()
     snake_case_					       =					[False for _ in range(UpperCAmelCase					)]
     snake_case_					       =					[-1 for _ in range(UpperCAmelCase					)]
     snake_case_					       =					index_of[:]

     def strong_connect(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					):
          snake_case_					       =					index  # the number when this node is seen
          snake_case_					       =					index  # lowest rank node reachable from here
          index += 1
          stack.append(UpperCAmelCase					)
          snake_case_					       =					True

          for w in g[v]:
               if index_of[w] == -1:
                    snake_case_					       =					strong_connect(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)
                    snake_case_					       =					(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )
               elif on_stack[w]:
                    snake_case_					       =					(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )

          if lowlink_of[v] == index_of[v]:
               snake_case_					       =					[]
               snake_case_					       =					stack.pop()
               snake_case_					       =					False
               component.append(UpperCAmelCase					)
               while w != v:
                    snake_case_					       =					stack.pop()
                    snake_case_					       =					False
                    component.append(UpperCAmelCase					)
               components.append(UpperCAmelCase					)
          return index

     snake_case_					       =					[]
     for v in range(UpperCAmelCase					):
          if index_of[v] == -1:
               strong_connect(UpperCAmelCase						,					0						,					UpperCAmelCase					)

     return components







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      Optional[int]:
     snake_case_					       =					[[] for _ in range(UpperCAmelCase					)]
     for u, v in edges:
          g[u].append(UpperCAmelCase					)
     return g


if __name__ == "__main__":
    # Test
    __UpperCamelCase									=			7
    __UpperCamelCase									=			[0, 0, 1, 2, 3, 3, 4, 4, 6]
    __UpperCamelCase									=			[1, 3, 2, 0, 1, 4, 5, 6, 5]
    __UpperCamelCase									=			[(u, v) for u, v in zip(source, target)]
    __UpperCamelCase									=			create_graph(n_vertices, edges)

    assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
			from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments




@require_torch
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    List[Any]			,  lowercase	:    Dict	):


				'''simple docstring'''


				for model_result in results.values():
							for batch_size, sequence_length in zip(model_result['bs']			,  model_result['ss']	):
										_snake_case		=			model_result['result'][batch_size][sequence_length]
										self.assertIsNotNone(lowercase	)



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			'sgugger/tiny-distilbert-classification'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  only_pretrain_model=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  torchscript=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	@unittest.skipIf(torch_device == 'cpu'			,  'Cant do half precision'	)
	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  fpaa=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				# set architectures equal to `None`
				_snake_case		=			None
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	@unittest.skipIf(torch_device == 'cpu'			,  'Can\'t do half precision'	)
	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  fpaa=lowercase			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tinier_bart'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tinier_bart'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				with tempfile.TemporaryDirectory() as tmp_dir:
							_snake_case		=			PyTorchBenchmarkArguments(
							    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  save_to_csv=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  inference_time_csv_file=os.path.join(lowercase			,  'inf_time.csv'	)			,  train_memory_csv_file=os.path.join(lowercase			,  'train_mem.csv'	)			,  inference_memory_csv_file=os.path.join(lowercase			,  'inf_mem.csv'	)			,  train_time_csv_file=os.path.join(lowercase			,  'train_time.csv'	)			,  env_info_csv_file=os.path.join(lowercase			,  'env.csv'	)			,  multi_process=lowercase			,  )
							_snake_case		=			PyTorchBenchmark(lowercase	)
							benchmark.run()
							self.assertTrue(Path(os.path.join(lowercase			,  'inf_time.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'train_time.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'inf_mem.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'train_mem.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'env.csv'	)	).exists()	)





	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'

				def _check_summary_is_not_empty(lowercase	:    Optional[Any]	):
							self.assertTrue(hasattr(lowercase			,  'sequential'	)	)
							self.assertTrue(hasattr(lowercase			,  'cumulative'	)	)
							self.assertTrue(hasattr(lowercase			,  'current'	)	)
							self.assertTrue(hasattr(lowercase			,  'total'	)	)

				with tempfile.TemporaryDirectory() as tmp_dir:
							_snake_case		=			PyTorchBenchmarkArguments(
							    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  log_filename=os.path.join(lowercase			,  'log.txt'	)			,  log_print=lowercase			,  trace_memory_line_by_line=lowercase			,  multi_process=lowercase			,  )
							_snake_case		=			PyTorchBenchmark(lowercase	)
							_snake_case		=			benchmark.run()
							_check_summary_is_not_empty(result.inference_summary	)
							_check_summary_is_not_empty(result.train_summary	)
							self.assertTrue(Path(os.path.join(lowercase			,  'log.txt'	)	).exists()	)