
"""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 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							List[str]			)->  Any:







              """simple docstring"""
              for model_result in results.values():
                     for batch_size, sequence_length in zip(model_result["bs"]       ,      model_result["ss"]			):
                            UpperCamelCase					 =		model_result["result"][batch_size][sequence_length]
                            self.assertIsNotNone(UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		"sshleifer/tiny-gpt2"
              UpperCamelCase					 =		PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      inference=UpperCAmelCase_       ,      sequence_lengths=[8]       ,      batch_sizes=[1]       ,      multi_process=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_			)
              UpperCamelCase					 =		benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result			)
              self.check_results_dict_not_empty(results.memory_inference_result			)






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







              """simple docstring"""
              UpperCamelCase					 =		"sgugger/tiny-distilbert-classification"
              UpperCamelCase					 =		PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      inference=UpperCAmelCase_       ,      sequence_lengths=[8]       ,      batch_sizes=[1]       ,      multi_process=UpperCAmelCase_       ,      only_pretrain_model=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_			)
              UpperCamelCase					 =		benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result			)
              self.check_results_dict_not_empty(results.memory_inference_result			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		"sshleifer/tiny-gpt2"
              UpperCamelCase					 =		PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      inference=UpperCAmelCase_       ,      torchscript=UpperCAmelCase_       ,      sequence_lengths=[8]       ,      batch_sizes=[1]       ,      multi_process=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_			)
              UpperCamelCase					 =		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 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		"sshleifer/tiny-gpt2"
              UpperCamelCase					 =		PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      inference=UpperCAmelCase_       ,      fpaa=UpperCAmelCase_       ,      sequence_lengths=[8]       ,      batch_sizes=[1]       ,      multi_process=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_			)
              UpperCamelCase					 =		benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result			)
              self.check_results_dict_not_empty(results.memory_inference_result			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		"sshleifer/tiny-gpt2"
              UpperCamelCase					 =		AutoConfig.from_pretrained(UpperCAmelCase_			)
              # set architectures equal to `None`
              UpperCamelCase					 =		None
              UpperCamelCase					 =		PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      inference=UpperCAmelCase_       ,      sequence_lengths=[8]       ,      batch_sizes=[1]       ,      multi_process=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_       ,      configs=[config]			)
              UpperCamelCase					 =		benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result			)
              self.check_results_dict_not_empty(results.memory_inference_result			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		"sshleifer/tiny-gpt2"
              UpperCamelCase					 =		PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      inference=UpperCAmelCase_       ,      sequence_lengths=[8]       ,      batch_sizes=[1]       ,      multi_process=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_			)
              UpperCamelCase					 =		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 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		"sshleifer/tiny-gpt2"
              UpperCamelCase					 =		PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      inference=UpperCAmelCase_       ,      sequence_lengths=[8]       ,      batch_sizes=[1]       ,      fpaa=UpperCAmelCase_       ,      multi_process=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_			)
              UpperCamelCase					 =		benchmark.run()
              self.check_results_dict_not_empty(results.time_train_result			)
              self.check_results_dict_not_empty(results.memory_train_result			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		"sshleifer/tiny-gpt2"
              UpperCamelCase					 =		AutoConfig.from_pretrained(UpperCAmelCase_			)
              UpperCamelCase					 =		PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      inference=UpperCAmelCase_       ,      sequence_lengths=[8]       ,      batch_sizes=[1]       ,      multi_process=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_       ,      configs=[config]			)
              UpperCamelCase					 =		benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result			)
              self.check_results_dict_not_empty(results.memory_inference_result			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		"sshleifer/tinier_bart"
              UpperCamelCase					 =		AutoConfig.from_pretrained(UpperCAmelCase_			)
              UpperCamelCase					 =		PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      inference=UpperCAmelCase_       ,      sequence_lengths=[8]       ,      batch_sizes=[1]       ,      multi_process=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_       ,      configs=[config]			)
              UpperCamelCase					 =		benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result			)
              self.check_results_dict_not_empty(results.memory_inference_result			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		"sshleifer/tiny-gpt2"
              UpperCamelCase					 =		AutoConfig.from_pretrained(UpperCAmelCase_			)
              UpperCamelCase					 =		PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      inference=UpperCAmelCase_       ,      sequence_lengths=[8]       ,      batch_sizes=[1]       ,      multi_process=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_       ,      configs=[config]			)
              UpperCamelCase					 =		benchmark.run()
              self.check_results_dict_not_empty(results.time_train_result			)
              self.check_results_dict_not_empty(results.memory_train_result			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		"sshleifer/tinier_bart"
              UpperCamelCase					 =		AutoConfig.from_pretrained(UpperCAmelCase_			)
              UpperCamelCase					 =		PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      inference=UpperCAmelCase_       ,      sequence_lengths=[8]       ,      batch_sizes=[1]       ,      multi_process=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_       ,      configs=[config]			)
              UpperCamelCase					 =		benchmark.run()
              self.check_results_dict_not_empty(results.time_train_result			)
              self.check_results_dict_not_empty(results.memory_train_result			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  List[Any]:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		"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:
                     UpperCamelCase					 =		PyTorchBenchmarkArguments(
                         models=[MODEL_ID]       ,      training=UpperCAmelCase_       ,      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_       ,      multi_process=UpperCAmelCase_       ,      )
                     UpperCamelCase					 =		PyTorchBenchmark(UpperCAmelCase_			)
                     UpperCamelCase					 =		benchmark.run()
                     _check_summary_is_not_empty(result.inference_summary			)
                     _check_summary_is_not_empty(result.train_summary			)
                     self.assertTrue(Path(os.path.join(UpperCAmelCase_       ,      "log.txt"			)			).exists()			)







"""simple docstring"""


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


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



class   __a  :
       UpperCamelCase_		:					str
       UpperCamelCase_		:					str   =			None






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Union[str, Any]:







              """simple docstring"""
              raise NotImplementedError






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Dict			)->  Union[str, Any]:







              """simple docstring"""
              raise NotImplementedError






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							str			)->  Any:







              """simple docstring"""
              raise NotImplementedError






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







              """simple docstring"""
              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 	_SCREAMING_SNAKE_CASE       (  cls :							Union[str, Any]			)->  int:







              """simple docstring"""
              return f"`pip install {cls.pip_package or cls.name}`"





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					List[str]   =			'''optuna'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Union[str, Any]:







              """simple docstring"""
              return is_optuna_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Optional[Any]			)->  Union[str, Any]:







              """simple docstring"""
              return run_hp_search_optuna(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Union[str, Any]			)->  Union[str, Any]:







              """simple docstring"""
              return default_hp_space_optuna(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Optional[int]   =			'''ray'''
       UpperCamelCase_		:					Tuple   =			'''\'ray[tune]\''''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Any:







              """simple docstring"""
              return is_ray_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							Any       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							int			)->  int:







              """simple docstring"""
              return run_hp_search_ray(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






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







              """simple docstring"""
              return default_hp_space_ray(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Union[str, Any]   =			'''sigopt'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  List[str]:







              """simple docstring"""
              return is_sigopt_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Tuple			)->  Tuple:







              """simple docstring"""
              return run_hp_search_sigopt(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							Tuple			)->  Optional[Any]:







              """simple docstring"""
              return default_hp_space_sigopt(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Any   =			'''wandb'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  List[Any]:







              """simple docstring"""
              return is_wandb_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							List[str]			)->  str:







              """simple docstring"""
              return run_hp_search_wandb(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							List[Any]			)->  List[Any]:







              """simple docstring"""
              return default_hp_space_wandb(UpperCAmelCase_			)





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



def 	lowerCamelCase__			(							)->							str:







       """simple docstring"""
       UpperCamelCase					 =		[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
       if len(UpperCAmelCase_				) > 0:
              UpperCamelCase					 =		available_backends[0].name
              if len(UpperCAmelCase_				) > 1:
                     logger.info(
                         F"{len(UpperCAmelCase_				)} 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()				)				)





