
'''simple docstring'''




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 					A							(				unittest.TestCase		):





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Tuple:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  List[Any]   =		'''sshleifer/tiny-gpt2'''
         A			:  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		,     )
         A			:  int   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      )
         A			:  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 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  str   =		'''sgugger/tiny-distilbert-classification'''
         A			:  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		,     )
         A			:  List[Any]   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      )
         A			:  Tuple   =		benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result      )
         self.check_results_dict_not_empty(results.memory_inference_result      )





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A			:  Union[str, Any]   =		'''sshleifer/tiny-gpt2'''
         A			:  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		,     )
         A			:  str   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      )
         A			:  Dict   =		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 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Union[str, Any]   =		'''sshleifer/tiny-gpt2'''
         A			:  Dict   =		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		,     )
         A			:  List[Any]   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      )
         A			:  Tuple   =		benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result      )
         self.check_results_dict_not_empty(results.memory_inference_result      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  str   =		'''sshleifer/tiny-gpt2'''
         A			:  Optional[Any]   =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
         # set architectures equal to `None`
         A			:  Optional[int]   =		None
         A			:  Dict   =		PyTorchBenchmarkArguments(
             models=[MODEL_ID]		,     training=SCREAMING_SNAKE_CASE		,     inference=SCREAMING_SNAKE_CASE		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=SCREAMING_SNAKE_CASE		,     )
         A			:  int   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE		,     configs=[config]      )
         A			:  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 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A			:  List[Any]   =		'''sshleifer/tiny-gpt2'''
         A			:  Dict   =		PyTorchBenchmarkArguments(
             models=[MODEL_ID]		,     training=SCREAMING_SNAKE_CASE		,     inference=SCREAMING_SNAKE_CASE		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=SCREAMING_SNAKE_CASE		,     )
         A			:  Any   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      )
         A			:  Optional[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 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A			:  int   =		'''sshleifer/tiny-gpt2'''
         A			:  Union[str, 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		,     )
         A			:  List[str]   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      )
         A			:  Dict   =		benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result      )
         self.check_results_dict_not_empty(results.memory_train_result      )





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  Optional[Any]   =		'''sshleifer/tiny-gpt2'''
         A			:  Any   =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
         A			:  Dict   =		PyTorchBenchmarkArguments(
             models=[MODEL_ID]		,     training=SCREAMING_SNAKE_CASE		,     inference=SCREAMING_SNAKE_CASE		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=SCREAMING_SNAKE_CASE		,     )
         A			:  int   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE		,     configs=[config]      )
         A			:  str   =		benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result      )
         self.check_results_dict_not_empty(results.memory_inference_result      )





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A			:  Any   =		'''sshleifer/tinier_bart'''
         A			:  int   =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
         A			:  Optional[int]   =		PyTorchBenchmarkArguments(
             models=[MODEL_ID]		,     training=SCREAMING_SNAKE_CASE		,     inference=SCREAMING_SNAKE_CASE		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=SCREAMING_SNAKE_CASE		,     )
         A			:  str   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE		,     configs=[config]      )
         A			:  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 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Optional[Any]   =		'''sshleifer/tiny-gpt2'''
         A			:  Dict   =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
         A			:  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		,     )
         A			:  Optional[int]   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE		,     configs=[config]      )
         A			:  List[Any]   =		benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result      )
         self.check_results_dict_not_empty(results.memory_train_result      )





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A			:  List[Any]   =		'''sshleifer/tinier_bart'''
         A			:  str   =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
         A			:  List[Any]   =		PyTorchBenchmarkArguments(
             models=[MODEL_ID]		,     training=SCREAMING_SNAKE_CASE		,     inference=SCREAMING_SNAKE_CASE		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=SCREAMING_SNAKE_CASE		,     )
         A			:  Optional[Any]   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE		,     configs=[config]      )
         A			:  Optional[Any]   =		benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result      )
         self.check_results_dict_not_empty(results.memory_train_result      )





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  Optional[int]   =		'''sshleifer/tiny-gpt2'''
         with tempfile.TemporaryDirectory() as tmp_dir:
             A			:  Dict   =		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		,     )
             A			:  Optional[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 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  int   =		'''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:
             A			:  Union[str, Any]   =		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		,     )
             A			:  Any   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      )
             A			:  Any   =		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()      )






'''simple docstring'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A,    A,    A			:  Any   =		[], [], []
    for element in data:
        if element < pivot:
            less.append(snake_case__		)
        elif element > pivot:
            greater.append(snake_case__		)
        else:
            equal.append(snake_case__		)
    return less, equal, greater







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if index >= len(snake_case__		) or index < 0:
        return None

    A			:  List[Any]   =		items[random.randint(0						,							len(snake_case__		) - 1		)]
    A			:  Optional[int]   =		0
    A,    A,    A			:  Dict   =		_partition(snake_case__						,							snake_case__		)
    A			:  int   =		len(snake_case__		)
    A			:  List[Any]   =		len(snake_case__		)

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(snake_case__						,							snake_case__		)
    # must be in larger
    else:
        return quick_select(snake_case__						,							index - (m + count)		)




