
from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__a  			=					logging.get_logger(__name__)
class  lowercase__:



	"""simple docstring"""




	a			:str
	a			:str         =	None
	@staticmethod
	def 						_lowercase  (       )       ->     Optional[int]:
					raise NotImplementedError
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			int       )       ->     Union[str, Any]:
					raise NotImplementedError
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     Optional[int]:
					raise NotImplementedError
	def 						_lowercase  (       self  :			Tuple       )       ->     Union[str, Any]:
					if not self.is_available():
									raise RuntimeError(
									    f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''       )




	@classmethod
	def 						_lowercase  (       cls  :			Dict       )       ->     Dict:
					return f'''`pip install {cls.pip_package or cls.name}`'''

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Any         =	'optuna'
	@staticmethod
	def 						_lowercase  (       )       ->     str:
					return is_optuna_available()
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			int       )       ->     str:
					return run_hp_search_optuna(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       )       ->     str:
					return default_hp_space_optuna(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Tuple         =	'ray'
	a			:Optional[int]         =	'\'ray[tune]\''
	@staticmethod
	def 						_lowercase  (       )       ->     Any:
					return is_ray_available()
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			Any       )       ->     str:
					return run_hp_search_ray(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     int:
					return default_hp_space_ray(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:List[str]         =	'sigopt'
	@staticmethod
	def 						_lowercase  (       )       ->     Union[str, Any]:
					return is_sigopt_available()
	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Union[str, Any]:
					return run_hp_search_sigopt(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Tuple:
					return default_hp_space_sigopt(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:str         =	'wandb'
	@staticmethod
	def 						_lowercase  (       )       ->     str:
					return is_wandb_available()
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Tuple:
					return run_hp_search_wandb(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     List[Any]:
					return default_hp_space_wandb(SCREAMING_SNAKE_CASE_       )

__a  			=					{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
				if len(snake_case__					) > 0:
								lowercase_       =					available_backends[0].name
								if len(snake_case__					) > 1:
												logger.info(
												    F'''{len(snake_case__					)} hyperparameter search backends available. Using {name} as the default.'''					)
								return name
				raise RuntimeError(
				    '''No hyperparameter search backend available.\n'''
				    + '''\n'''.join(
				        F''' - To install {backend.name} run {backend.pip_install()}'''
				        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()					)					)






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 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: int    )			->	str:







    """simple docstring"""






    for model_result in results.values():
      for batch_size, sequence_length in zip(model_result["""bs"""]			,							model_result["""ss"""]    ):
        __magic_name__				     =       model_result["""result"""][batch_size][sequence_length]
        self.assertIsNotNone(UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Optional[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sgugger/tiny-distilbert-classification"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							only_pretrain_model=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Any    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Tuple    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Any    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: List[Any]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result    )
    self.check_results_dict_not_empty(results.memory_train_result    )
  def 							_lowercase							( self			: Optional[Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result    )
    self.check_results_dict_not_empty(results.memory_train_result    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       """patrickvonplaten/t5-tiny-random"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							configs=[config]    )
    __magic_name__				     =       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 							_lowercase							( self			: Tuple    )			->	int:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							use_xla=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    with tempfile.TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       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__			,							)
      __magic_name__				     =       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 							_lowercase							( self			: int    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""

    def _check_summary_is_not_empty(UpperCamelCase__			: Dict    ):
      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:
      __magic_name__				     =       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__			,							)
      __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
      __magic_name__				     =       benchmark.run()
      _check_summary_is_not_empty(result.inference_summary    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""log.txt"""    )    ).exists()    )

