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 	__snake_case      (   unittest.TestCase      ):
 def 		UpperCAmelCase__				(     self						,      snake_case__  )		->							List[Any]:
       '''simple docstring'''





       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(snake_case__  )
 def 		UpperCAmelCase__				(     self  )		->							str:
       '''simple docstring'''





       UpperCAmelCase			:       str			     ='''sshleifer/tiny-gpt2'''
       UpperCAmelCase			:       List[Any]			     =PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      multi_process=snake_case__						,      )
       UpperCAmelCase			:       Any			     =PyTorchBenchmark(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 		UpperCAmelCase__				(     self  )		->							Dict:
       '''simple docstring'''





       UpperCAmelCase			:       Tuple			     ='''sgugger/tiny-distilbert-classification'''
       UpperCAmelCase			:       Optional[Any]			     =PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      multi_process=snake_case__						,      only_pretrain_model=snake_case__						,      )
       UpperCAmelCase			:       List[str]			     =PyTorchBenchmark(snake_case__  )
       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 		UpperCAmelCase__				(     self  )		->							Tuple:
       '''simple docstring'''





       UpperCAmelCase			:       int			     ='''sshleifer/tiny-gpt2'''
       UpperCAmelCase			:       Union[str, Any]			     =PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      torchscript=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      multi_process=snake_case__						,      )
       UpperCAmelCase			:       int			     =PyTorchBenchmark(snake_case__  )
       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  )
 @unittest.skipIf(torch_device == '''cpu'''						,      '''Cant do half precision'''  )
 def 		UpperCAmelCase__				(     self  )		->							Dict:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     ='''sshleifer/tiny-gpt2'''
       UpperCAmelCase			:       Optional[int]			     =PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      fpaa=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      multi_process=snake_case__						,      )
       UpperCAmelCase			:       int			     =PyTorchBenchmark(snake_case__  )
       UpperCAmelCase			:       Optional[Any]			     =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  )		->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase			:       str			     ='''sshleifer/tiny-gpt2'''
       UpperCAmelCase			:       Any			     =AutoConfig.from_pretrained(snake_case__  )
       # set architectures equal to `None`
       UpperCAmelCase			:       Optional[Any]			     =None
       UpperCAmelCase			:       str			     =PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      multi_process=snake_case__						,      )
       UpperCAmelCase			:       List[str]			     =PyTorchBenchmark(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 		UpperCAmelCase__				(     self  )		->							int:
       '''simple docstring'''





       UpperCAmelCase			:       List[str]			     ='''sshleifer/tiny-gpt2'''
       UpperCAmelCase			:       str			     =PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      multi_process=snake_case__						,      )
       UpperCAmelCase			:       List[Any]			     =PyTorchBenchmark(snake_case__  )
       UpperCAmelCase			:       int			     =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 		UpperCAmelCase__				(     self  )		->							int:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     ='''sshleifer/tiny-gpt2'''
       UpperCAmelCase			:       Optional[int]			     =PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      fpaa=snake_case__						,      multi_process=snake_case__						,      )
       UpperCAmelCase			:       List[Any]			     =PyTorchBenchmark(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 		UpperCAmelCase__				(     self  )		->							Dict:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     ='''sshleifer/tiny-gpt2'''
       UpperCAmelCase			:       Union[str, Any]			     =AutoConfig.from_pretrained(snake_case__  )
       UpperCAmelCase			:       Optional[int]			     =PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      multi_process=snake_case__						,      )
       UpperCAmelCase			:       Optional[int]			     =PyTorchBenchmark(snake_case__						,      configs=[config]  )
       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 		UpperCAmelCase__				(     self  )		->							Any:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     ='''sshleifer/tinier_bart'''
       UpperCAmelCase			:       Any			     =AutoConfig.from_pretrained(snake_case__  )
       UpperCAmelCase			:       Tuple			     =PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      multi_process=snake_case__						,      )
       UpperCAmelCase			:       Tuple			     =PyTorchBenchmark(snake_case__						,      configs=[config]  )
       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 		UpperCAmelCase__				(     self  )		->							Tuple:
       '''simple docstring'''





       UpperCAmelCase			:       str			     ='''sshleifer/tiny-gpt2'''
       UpperCAmelCase			:       Dict			     =AutoConfig.from_pretrained(snake_case__  )
       UpperCAmelCase			:       List[str]			     =PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      multi_process=snake_case__						,      )
       UpperCAmelCase			:       Union[str, Any]			     =PyTorchBenchmark(snake_case__						,      configs=[config]  )
       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 		UpperCAmelCase__				(     self  )		->							Tuple:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     ='''sshleifer/tinier_bart'''
       UpperCAmelCase			:       Union[str, Any]			     =AutoConfig.from_pretrained(snake_case__  )
       UpperCAmelCase			:       str			     =PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      multi_process=snake_case__						,      )
       UpperCAmelCase			:       List[Any]			     =PyTorchBenchmark(snake_case__						,      configs=[config]  )
       UpperCAmelCase			:       str			     =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  )		->							List[str]:
       '''simple docstring'''





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






 def 		UpperCAmelCase__				(     self  )		->							List[str]:
       '''simple docstring'''





       UpperCAmelCase			:       str			     ='''sshleifer/tiny-gpt2'''

       def _check_summary_is_not_empty(snake_case__  ):
             self.assertTrue(hasattr(snake_case__						,      '''sequential'''  )  )
             self.assertTrue(hasattr(snake_case__						,      '''cumulative'''  )  )
             self.assertTrue(hasattr(snake_case__						,      '''current'''  )  )
             self.assertTrue(hasattr(snake_case__						,      '''total'''  )  )

       with tempfile.TemporaryDirectory() as tmp_dir:
             UpperCAmelCase			:       Dict			     =PyTorchBenchmarkArguments(
                 models=[MODEL_ID]						,      training=snake_case__						,      inference=snake_case__						,      sequence_lengths=[8]						,      batch_sizes=[1]						,      log_filename=os.path.join(snake_case__						,      '''log.txt'''  )						,      log_print=snake_case__						,      trace_memory_line_by_line=snake_case__						,      multi_process=snake_case__						,      )
             UpperCAmelCase			:       Union[str, Any]			     =PyTorchBenchmark(snake_case__  )
             UpperCAmelCase			:       Union[str, 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(snake_case__						,      '''log.txt'''  )  ).exists()  )







lowerCAmelCase_    =				{
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355_818,
}






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						float:






				"""simple docstring"""


				if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
								snake_case_						:    str				=  (
								    f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
								    f'''Valid values are: {", ".join(_UpperCamelCase )}'''
								)
								raise ValueError(_UpperCamelCase )
				return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
