


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 A    (  unittest.TestCase	):






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	Tuple					)   ->		List[str]:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Any            ='sshleifer/tiny-gpt2'
             _lowerCamelCase			:						str            =TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]     ,  training=lowercase_     ,  inference=lowercase_     ,  sequence_lengths=[8]     ,  batch_sizes=[1]     ,  eager_mode=lowercase_     ,  multi_process=lowercase_     ,  )
             _lowerCamelCase			:						Union[str, Any]            =TensorFlowBenchmark(lowercase_					)
             _lowerCamelCase			:						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		:	Optional[int]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            ='sgugger/tiny-distilbert-classification'
             _lowerCamelCase			:						int            =TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]     ,  training=lowercase_     ,  inference=lowercase_     ,  sequence_lengths=[8]     ,  batch_sizes=[1]     ,  multi_process=lowercase_     ,  only_pretrain_model=lowercase_     ,  )
             _lowerCamelCase			:						Tuple            =TensorFlowBenchmark(lowercase_					)
             _lowerCamelCase			:						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		:	Optional[int]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            ='sshleifer/tiny-gpt2'
             _lowerCamelCase			:						Dict            =TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]     ,  training=lowercase_     ,  inference=lowercase_     ,  sequence_lengths=[8]     ,  batch_sizes=[1]     ,  multi_process=lowercase_     ,  )
             _lowerCamelCase			:						Union[str, Any]            =TensorFlowBenchmark(lowercase_					)
             _lowerCamelCase			:						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[Any]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						Any            ='sshleifer/tiny-gpt2'
             _lowerCamelCase			:						str            =AutoConfig.from_pretrained(lowercase_					)
             _lowerCamelCase			:						Union[str, Any]            =TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]     ,  training=lowercase_     ,  inference=lowercase_     ,  sequence_lengths=[8]     ,  batch_sizes=[1]     ,  eager_mode=lowercase_     ,  multi_process=lowercase_     ,  )
             _lowerCamelCase			:						int            =TensorFlowBenchmark(lowercase_     ,  [config]					)
             _lowerCamelCase			:						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		:	Optional[Any]					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            ='sshleifer/tiny-gpt2'
             _lowerCamelCase			:						int            =AutoConfig.from_pretrained(lowercase_					)
             _lowerCamelCase			:						List[Any]            =TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]     ,  training=lowercase_     ,  inference=lowercase_     ,  sequence_lengths=[8]     ,  batch_sizes=[1]     ,  multi_process=lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =TensorFlowBenchmark(lowercase_     ,  [config]					)
             _lowerCamelCase			:						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		:	Any					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            ='sshleifer/tiny-gpt2'
             _lowerCamelCase			:						Optional[Any]            =TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]     ,  training=lowercase_     ,  inference=lowercase_     ,  sequence_lengths=[8]     ,  batch_sizes=[1]     ,  multi_process=lowercase_     ,  )
             _lowerCamelCase			:						Optional[Any]            =TensorFlowBenchmark(lowercase_					)
             _lowerCamelCase			:						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		:	Tuple					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            ='sshleifer/tiny-gpt2'
             _lowerCamelCase			:						Tuple            =AutoConfig.from_pretrained(lowercase_					)
             _lowerCamelCase			:						Optional[int]            =TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]     ,  training=lowercase_     ,  inference=lowercase_     ,  sequence_lengths=[8]     ,  batch_sizes=[1]     ,  multi_process=lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =TensorFlowBenchmark(lowercase_     ,  [config]					)
             _lowerCamelCase			:						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		:	Optional[Any]					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            ='patrickvonplaten/t5-tiny-random'
             _lowerCamelCase			:						str            =AutoConfig.from_pretrained(lowercase_					)
             _lowerCamelCase			:						str            =TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]     ,  training=lowercase_     ,  inference=lowercase_     ,  sequence_lengths=[8]     ,  batch_sizes=[1]     ,  multi_process=lowercase_     ,  )
             _lowerCamelCase			:						str            =TensorFlowBenchmark(lowercase_     ,  configs=[config]					)
             _lowerCamelCase			:						Optional[Any]            =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 			lowerCamelCase			(   self		:	Optional[int]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            ='sshleifer/tiny-gpt2'
             _lowerCamelCase			:						Tuple            =TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]     ,  training=lowercase_     ,  inference=lowercase_     ,  sequence_lengths=[8]     ,  batch_sizes=[1]     ,  use_xla=lowercase_     ,  multi_process=lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =TensorFlowBenchmark(lowercase_					)
             _lowerCamelCase			:						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		:	List[Any]					)   ->		Any:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	int					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            ='sshleifer/tiny-gpt2'

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

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






import numpy as np




def       a_ (       SCREAMING_SNAKE_CASE__   : np.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       return np.where(vector > 0						,     SCREAMING_SNAKE_CASE__						,     (alpha * (np.exp(SCREAMING_SNAKE_CASE__	) - 1))	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


