

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers.generation import (
         MaxLengthCriteria,
         MaxNewTokensCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         validate_stopping_criteria,
     )



@require_torch
class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		3
      snake_case							:		Optional[Any]        =		250

      snake_case							:		Union[str, Any]        =		ids_tensor((batch_size, length)   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		Tuple        =		torch.ones((batch_size, length)   ,device=SCREAMING_SNAKE_CASE_   ,dtype=torch.float    ) / length
      return input_ids, scores





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		str        =		self._get_tensors(5    )

      snake_case							:		Optional[Any]        =		StoppingCriteriaList(
          [
              MaxLengthCriteria(max_length=10    ),
              MaxTimeCriteria(max_time=0.1    ),
          ]    )

      self.assertFalse(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )

      snake_case      ,		snake_case							:		List[str]        =		self._get_tensors(9    )
      self.assertFalse(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )

      snake_case      ,		snake_case							:		int        =		self._get_tensors(10    )
      self.assertTrue(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		MaxLengthCriteria(max_length=10    )

      snake_case      ,		snake_case							:		int        =		self._get_tensors(5    )
      self.assertFalse(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )

      snake_case      ,		snake_case							:		List[str]        =		self._get_tensors(9    )
      self.assertFalse(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )

      snake_case      ,		snake_case							:		List[str]        =		self._get_tensors(10    )
      self.assertTrue(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		str        =		MaxNewTokensCriteria(start_length=5   ,max_new_tokens=5    )

      snake_case      ,		snake_case							:		int        =		self._get_tensors(5    )
      self.assertFalse(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )

      snake_case      ,		snake_case							:		Union[str, Any]        =		self._get_tensors(9    )
      self.assertFalse(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )

      snake_case      ,		snake_case							:		Tuple        =		self._get_tensors(10    )
      self.assertTrue(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )

      snake_case							:		Optional[Any]        =		StoppingCriteriaList([criteria]    )
      self.assertEqual(criteria_list.max_length   ,10    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		Dict        =		self._get_tensors(5    )

      snake_case							:		Optional[Any]        =		MaxTimeCriteria(max_time=0.1    )
      self.assertFalse(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )

      snake_case							:		Optional[int]        =		MaxTimeCriteria(max_time=0.1   ,initial_timestamp=time.time() - 0.2    )
      self.assertTrue(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10    )]    )   ,10    )

      with self.assertWarns(SCREAMING_SNAKE_CASE_    ):
          validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10    )]    )   ,11    )

      snake_case							:		Union[str, Any]        =		validate_stopping_criteria(StoppingCriteriaList()   ,11    )

      self.assertEqual(len(SCREAMING_SNAKE_CASE_    )   ,1    )











"""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


lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			:
	__magic_name__       =							42
	__magic_name__       =							None





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     Optional[int]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     Optional[Any]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Union[str, Any]		)		->     str:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Dict:
				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   :					Tuple		)		->     List[str]:
				return f"""`pip install {cls.pip_package or cls.name}`"""







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''optuna'''





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





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     int:
				return run_hp_search_optuna(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Dict		)		->     Optional[Any]:
				return default_hp_space_optuna(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''ray'''
	__magic_name__       =							'''\'ray[tune]\''''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     Optional[int]:
				return is_ray_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     List[str]:
				return run_hp_search_ray(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Tuple		)		->     Optional[int]:
				return default_hp_space_ray(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''sigopt'''





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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					str		)		->     str:
				return run_hp_search_sigopt(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Any		)		->     Any:
				return default_hp_space_sigopt(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''wandb'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     int:
				return is_wandb_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Optional[Any]		)		->     Optional[Any]:
				return run_hp_search_wandb(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]		)		->     Any:
				return default_hp_space_wandb(lowerCAmelCase_		)





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

def 							snake_case (  ):
			UpperCAmelCase_	:					List[str]     	=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
			if len(A__		) > 0:
						UpperCAmelCase_	:					Optional[Any]     	=   available_backends[0].name
						if len(A__		) > 1:
									logger.info(
									    F"""{len(A__		)} 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()		)		)



