






'''simple docstring'''



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
      import tensorflow as tf

      from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Any		)  ->  Dict:


       '''simple docstring'''

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






      def 				A__      ( self			:		List[Any]		)  ->  int:


       '''simple docstring'''

       lowercase					:   Any				  ='''sshleifer/tiny-gpt2'''
       lowercase					:   int				  =TensorFlowBenchmarkArguments(
           models=[MODEL_ID]     ,      training=UpperCAmelCase     ,      inference=UpperCAmelCase     ,      sequence_lengths=[8]     ,      batch_sizes=[1]     ,      eager_mode=UpperCAmelCase     ,      multi_process=UpperCAmelCase     ,      )
       lowercase					:   List[Any]				  =TensorFlowBenchmark(UpperCAmelCase		)
       lowercase					:   int				  =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]		)  ->  int:


       '''simple docstring'''

       lowercase					:   Dict				  ='''sgugger/tiny-distilbert-classification'''
       lowercase					:   Tuple				  =TensorFlowBenchmarkArguments(
           models=[MODEL_ID]     ,      training=UpperCAmelCase     ,      inference=UpperCAmelCase     ,      sequence_lengths=[8]     ,      batch_sizes=[1]     ,      multi_process=UpperCAmelCase     ,      only_pretrain_model=UpperCAmelCase     ,      )
       lowercase					:   List[str]				  =TensorFlowBenchmark(UpperCAmelCase		)
       lowercase					:   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 				A__      ( self			:		Dict		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Optional[int]				  ='''sshleifer/tiny-gpt2'''
       lowercase					:   Dict				  =TensorFlowBenchmarkArguments(
           models=[MODEL_ID]     ,      training=UpperCAmelCase     ,      inference=UpperCAmelCase     ,      sequence_lengths=[8]     ,      batch_sizes=[1]     ,      multi_process=UpperCAmelCase     ,      )
       lowercase					:   int				  =TensorFlowBenchmark(UpperCAmelCase		)
       lowercase					:   Dict				  =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		)  ->  str:


       '''simple docstring'''

       lowercase					:   List[Any]				  ='''sshleifer/tiny-gpt2'''
       lowercase					:   Dict				  =AutoConfig.from_pretrained(UpperCAmelCase		)
       lowercase					:   str				  =TensorFlowBenchmarkArguments(
           models=[MODEL_ID]     ,      training=UpperCAmelCase     ,      inference=UpperCAmelCase     ,      sequence_lengths=[8]     ,      batch_sizes=[1]     ,      eager_mode=UpperCAmelCase     ,      multi_process=UpperCAmelCase     ,      )
       lowercase					:   Union[str, Any]				  =TensorFlowBenchmark(UpperCAmelCase     ,      [config]		)
       lowercase					:   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 				A__      ( self			:		Any		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   List[str]				  ='''sshleifer/tiny-gpt2'''
       lowercase					:   Any				  =AutoConfig.from_pretrained(UpperCAmelCase		)
       lowercase					:   Dict				  =TensorFlowBenchmarkArguments(
           models=[MODEL_ID]     ,      training=UpperCAmelCase     ,      inference=UpperCAmelCase     ,      sequence_lengths=[8]     ,      batch_sizes=[1]     ,      multi_process=UpperCAmelCase     ,      )
       lowercase					:   Optional[Any]				  =TensorFlowBenchmark(UpperCAmelCase     ,      [config]		)
       lowercase					:   Dict				  =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		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Optional[int]				  ='''sshleifer/tiny-gpt2'''
       lowercase					:   Union[str, Any]				  =TensorFlowBenchmarkArguments(
           models=[MODEL_ID]     ,      training=UpperCAmelCase     ,      inference=UpperCAmelCase     ,      sequence_lengths=[8]     ,      batch_sizes=[1]     ,      multi_process=UpperCAmelCase     ,      )
       lowercase					:   Tuple				  =TensorFlowBenchmark(UpperCAmelCase		)
       lowercase					:   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 				A__      ( self			:		Optional[int]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   str				  ='''sshleifer/tiny-gpt2'''
       lowercase					:   int				  =AutoConfig.from_pretrained(UpperCAmelCase		)
       lowercase					:   Dict				  =TensorFlowBenchmarkArguments(
           models=[MODEL_ID]     ,      training=UpperCAmelCase     ,      inference=UpperCAmelCase     ,      sequence_lengths=[8]     ,      batch_sizes=[1]     ,      multi_process=UpperCAmelCase     ,      )
       lowercase					:   Any				  =TensorFlowBenchmark(UpperCAmelCase     ,      [config]		)
       lowercase					:   Dict				  =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			:		str		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  ='''patrickvonplaten/t5-tiny-random'''
       lowercase					:   str				  =AutoConfig.from_pretrained(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =TensorFlowBenchmarkArguments(
           models=[MODEL_ID]     ,      training=UpperCAmelCase     ,      inference=UpperCAmelCase     ,      sequence_lengths=[8]     ,      batch_sizes=[1]     ,      multi_process=UpperCAmelCase     ,      )
       lowercase					:   int				  =TensorFlowBenchmark(UpperCAmelCase     ,      configs=[config]		)
       lowercase					:   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(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''		)		) == 0     ,      '''Cannot do xla on CPU.'''		)
      def 				A__      ( self			:		int		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   List[Any]				  ='''sshleifer/tiny-gpt2'''
       lowercase					:   str				  =TensorFlowBenchmarkArguments(
           models=[MODEL_ID]     ,      training=UpperCAmelCase     ,      inference=UpperCAmelCase     ,      sequence_lengths=[8]     ,      batch_sizes=[1]     ,      use_xla=UpperCAmelCase     ,      multi_process=UpperCAmelCase     ,      )
       lowercase					:   int				  =TensorFlowBenchmark(UpperCAmelCase		)
       lowercase					:   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 				A__      ( self			:		Tuple		)  ->  Any:


       '''simple docstring'''

       lowercase					:   str				  ='''sshleifer/tiny-gpt2'''
       with tempfile.TemporaryDirectory() as tmp_dir:
        lowercase					:   List[str]				  =TensorFlowBenchmarkArguments(
            models=[MODEL_ID]     ,      inference=UpperCAmelCase     ,      save_to_csv=UpperCAmelCase     ,      sequence_lengths=[8]     ,      batch_sizes=[1]     ,      inference_time_csv_file=os.path.join(UpperCAmelCase     ,      '''inf_time.csv'''		)     ,      inference_memory_csv_file=os.path.join(UpperCAmelCase     ,      '''inf_mem.csv'''		)     ,      env_info_csv_file=os.path.join(UpperCAmelCase     ,      '''env.csv'''		)     ,      multi_process=UpperCAmelCase     ,      )
        lowercase					:   Optional[Any]				  =TensorFlowBenchmark(UpperCAmelCase		)
        benchmark.run()
        self.assertTrue(Path(os.path.join(UpperCAmelCase     ,      '''inf_time.csv'''		)		).exists()		)
        self.assertTrue(Path(os.path.join(UpperCAmelCase     ,      '''inf_mem.csv'''		)		).exists()		)
        self.assertTrue(Path(os.path.join(UpperCAmelCase     ,      '''env.csv'''		)		).exists()		)






      def 				A__      ( self			:		List[str]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   int				  ='''sshleifer/tiny-gpt2'''

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

       with tempfile.TemporaryDirectory() as tmp_dir:
        lowercase					:   Union[str, Any]				  =TensorFlowBenchmarkArguments(
            models=[MODEL_ID]     ,      inference=UpperCAmelCase     ,      sequence_lengths=[8]     ,      batch_sizes=[1]     ,      log_filename=os.path.join(UpperCAmelCase     ,      '''log.txt'''		)     ,      log_print=UpperCAmelCase     ,      trace_memory_line_by_line=UpperCAmelCase     ,      eager_mode=UpperCAmelCase     ,      multi_process=UpperCAmelCase     ,      )
        lowercase					:   Any				  =TensorFlowBenchmark(UpperCAmelCase		)
        lowercase					:   Optional[int]				  =benchmark.run()
        _check_summary_is_not_empty(result.inference_summary		)
        self.assertTrue(Path(os.path.join(UpperCAmelCase     ,      '''log.txt'''		)		).exists()		)











'''simple docstring'''



from __future__ import annotations

SCREAMING_SNAKE_CASE						=	8.988E9  # units = N * m^s * C^-2


def 							lowercase_				(    __A :       float					,      __A :       float					,      __A :       float					,      __A :       float			)       ->      dict[str, float]:



 """simple docstring"""




 lowercase					:   Dict				  =abs(chargea * chargea			)

 if (force, chargea, chargea, distance).count(0			) != 1:
  raise ValueError('''One and only one argument must be 0'''			)
 if distance < 0:
  raise ValueError('''Distance cannot be negative'''			)
 if force == 0:
  lowercase					:   Union[str, Any]				  =COULOMBS_CONSTANT * charge_product / (distance**2)
  return {"force": force}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge1": chargea}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge2": chargea}
 elif distance == 0:
  lowercase					:   Tuple				  =(COULOMBS_CONSTANT * charge_product / abs(__A			)) ** 0.5
  return {"distance": distance}
 raise ValueError('''Exactly one argument must be 0'''			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



