



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							):
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          for model_result in results.values():
             for batch_size, sequence_length in zip(model_result['bs'],      model_result['ss']	):
                UpperCamelCase   :  Dict						     =     model_result['result'][batch_size][sequence_length]
                self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  List[Any]						     =     'sshleifer/tiny-gpt2'
          UpperCamelCase   :  Any						     =     PyTorchBenchmarkArguments(
              models=[MODEL_ID],      training=SCREAMING_SNAKE_CASE_,      inference=SCREAMING_SNAKE_CASE_,      sequence_lengths=[8],      batch_sizes=[1],      multi_process=SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  Dict						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result	)
          self.check_results_dict_not_empty(results.memory_inference_result	)
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[int]						     =     'sgugger/tiny-distilbert-classification'
          UpperCamelCase   :  Optional[Any]						     =     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_,      )
          UpperCamelCase   :  List[str]						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result	)
          self.check_results_dict_not_empty(results.memory_inference_result	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[Any]						     =     'sshleifer/tiny-gpt2'
          UpperCamelCase   :  Tuple						     =     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_,      )
          UpperCamelCase   :  List[str]						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     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  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  List[str]						     =     'sshleifer/tiny-gpt2'
          UpperCamelCase   :  Optional[Any]						     =     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_,      )
          UpperCamelCase   :  int						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result	)
          self.check_results_dict_not_empty(results.memory_inference_result	)
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Optional[int]						     =     'sshleifer/tiny-gpt2'
          UpperCamelCase   :  Any						     =     AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          # set architectures equal to `None`
          UpperCamelCase   :  Dict						     =     None
          UpperCamelCase   :  Optional[Any]						     =     PyTorchBenchmarkArguments(
              models=[MODEL_ID],      training=SCREAMING_SNAKE_CASE_,      inference=SCREAMING_SNAKE_CASE_,      sequence_lengths=[8],      batch_sizes=[1],      multi_process=SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  List[str]						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_,      configs=[config]	)
          UpperCamelCase   :  Union[str, Any]						     =     benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result	)
          self.check_results_dict_not_empty(results.memory_inference_result	)
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  int						     =     'sshleifer/tiny-gpt2'
          UpperCamelCase   :  Tuple						     =     PyTorchBenchmarkArguments(
              models=[MODEL_ID],      training=SCREAMING_SNAKE_CASE_,      inference=SCREAMING_SNAKE_CASE_,      sequence_lengths=[8],      batch_sizes=[1],      multi_process=SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  int						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     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  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  Any						     =     'sshleifer/tiny-gpt2'
          UpperCamelCase   :  List[Any]						     =     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_,      )
          UpperCamelCase   :  Any						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result	)
          self.check_results_dict_not_empty(results.memory_train_result	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Optional[int]						     =     'sshleifer/tiny-gpt2'
          UpperCamelCase   :  Union[str, Any]						     =     AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     PyTorchBenchmarkArguments(
              models=[MODEL_ID],      training=SCREAMING_SNAKE_CASE_,      inference=SCREAMING_SNAKE_CASE_,      sequence_lengths=[8],      batch_sizes=[1],      multi_process=SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  Union[str, Any]						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_,      configs=[config]	)
          UpperCamelCase   :  str						     =     benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result	)
          self.check_results_dict_not_empty(results.memory_inference_result	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Any						     =     'sshleifer/tinier_bart'
          UpperCamelCase   :  Any						     =     AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     PyTorchBenchmarkArguments(
              models=[MODEL_ID],      training=SCREAMING_SNAKE_CASE_,      inference=SCREAMING_SNAKE_CASE_,      sequence_lengths=[8],      batch_sizes=[1],      multi_process=SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  str						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_,      configs=[config]	)
          UpperCamelCase   :  List[str]						     =     benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result	)
          self.check_results_dict_not_empty(results.memory_inference_result	)
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Optional[int]						     =     'sshleifer/tiny-gpt2'
          UpperCamelCase   :  Dict						     =     AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     PyTorchBenchmarkArguments(
              models=[MODEL_ID],      training=SCREAMING_SNAKE_CASE_,      inference=SCREAMING_SNAKE_CASE_,      sequence_lengths=[8],      batch_sizes=[1],      multi_process=SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  int						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_,      configs=[config]	)
          UpperCamelCase   :  int						     =     benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result	)
          self.check_results_dict_not_empty(results.memory_train_result	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  str						     =     'sshleifer/tinier_bart'
          UpperCamelCase   :  Dict						     =     AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     PyTorchBenchmarkArguments(
              models=[MODEL_ID],      training=SCREAMING_SNAKE_CASE_,      inference=SCREAMING_SNAKE_CASE_,      sequence_lengths=[8],      batch_sizes=[1],      multi_process=SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  str						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_,      configs=[config]	)
          UpperCamelCase   :  Union[str, Any]						     =     benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result	)
          self.check_results_dict_not_empty(results.memory_train_result	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Union[str, Any]						     =     'sshleifer/tiny-gpt2'
          with tempfile.TemporaryDirectory() as tmp_dir:
             UpperCamelCase   :  Optional[Any]						     =     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_,      )
             UpperCamelCase   :  Any						     =     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  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  List[str]						     =     'sshleifer/tiny-gpt2'

          def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE_	):
             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:
             UpperCamelCase   :  int						     =     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_,      )
             UpperCamelCase   :  Any						     =     PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Tuple						     =     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()	)








from graphs.minimum_spanning_tree_kruskal import kruskal
def 		UpperCamelCase     (       )     ->					Tuple:
   UpperCamelCase   :  List[str]						     =     9
   UpperCamelCase   :  Optional[Any]						     =     [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   UpperCamelCase   :  int						     =     kruskal(snake_case__	,					snake_case__     )

   UpperCamelCase   :  List[str]						     =     [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(snake_case__     ) == sorted(snake_case__     )



