




from collections import deque



def 							_A       (						lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			len(lowerCAmelCase_      )
     lowerCAmelCase__          =			deque()
     lowerCAmelCase__          =			[False for _ in range(lowerCAmelCase_      )]
     lowerCAmelCase__          =			[-1 for _ in range(lowerCAmelCase_      )]
     lowerCAmelCase__          =			index_of[:]

     def strong_connect(lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Dict      ):
          lowerCAmelCase__          =			index  # the number when this node is seen
          lowerCAmelCase__          =			index  # lowest rank node reachable from here
          index += 1
          stack.append(lowerCAmelCase_      )
          lowerCAmelCase__          =			True

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

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

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

     return components



def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			[[] for _ in range(lowerCAmelCase_      )]
     for u, v in edges:
          g[u].append(lowerCAmelCase_      )
     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 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	int:
         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(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			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					:					int       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sgugger/tiny-distilbert-classification"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     only_pretrain_model=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			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]       )				->	int:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     torchscript=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         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(torch_device == "cpu"       ,     "Cant do half precision"       )
    def        a				(	self					:					Dict       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			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]       )				->	Tuple:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         # set architectures equal to `None`
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     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       )


    def        a				(	self					:					Any       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			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					:					int       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			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[int]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     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       )


    def        a				(	self					:					Optional[Any]       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     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       )


    def        a				(	self					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[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        a				(	self					:					Optional[int]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[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        a				(	self					:					List[Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     save_to_csv=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ,     train_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ,     inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ,     train_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ,     env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              benchmark.run()
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ).exists()       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE__					:					List[Any]       ):
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "sequential"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "cumulative"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "current"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "total"       )       )

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