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


UpperCamelCase  							=						logging.get_logger(__name__)



class   snake_case_       :
   __A	:		str
   __A	:		str         =				None






   @staticmethod
   def       __UpperCamelCase     (      )					->						Union[str, Any]:
       raise NotImplementedError






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     List[str]	,						lowercase_				:     int	,						lowercase_				:     str	,						**lowercase_				:     Dict  )					->						int:
       raise NotImplementedError






   def       __UpperCamelCase     (      self				:     Any	,						lowercase_				:     int  )					->						List[str]:
       raise NotImplementedError






   def       __UpperCamelCase     (      self				:     str  )					->						int:
       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       __UpperCamelCase     (      cls				:     Optional[Any]  )					->						Union[str, Any]:
       return F'''`pip install {cls.pip_package or cls.name}`'''



class   snake_case_       (		__A       ):
   __A	:		List[str]         =				"optuna"






   @staticmethod
   def       __UpperCamelCase     (      )					->						int:
       return is_optuna_available()






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     str	,						lowercase_				:     int	,						lowercase_				:     str	,						**lowercase_				:     Optional[Any]  )					->						Optional[int]:
       return run_hp_search_optuna(lowercase_	,						lowercase_	,						lowercase_	,						**lowercase_  )







   def       __UpperCamelCase     (      self				:     Tuple	,						lowercase_				:     Optional[int]  )					->						Any:
       return default_hp_space_optuna(lowercase_  )



class   snake_case_       (		__A       ):
   __A	:		Optional[Any]         =				"ray"
   __A	:		Dict         =				"'ray[tune]'"






   @staticmethod
   def       __UpperCamelCase     (      )					->						Tuple:
       return is_ray_available()






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     Dict	,						lowercase_				:     int	,						lowercase_				:     str	,						**lowercase_				:     Any  )					->						Optional[int]:
       return run_hp_search_ray(lowercase_	,						lowercase_	,						lowercase_	,						**lowercase_  )







   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     int  )					->						Tuple:
       return default_hp_space_ray(lowercase_  )



class   snake_case_       (		__A       ):
   __A	:		List[Any]         =				"sigopt"






   @staticmethod
   def       __UpperCamelCase     (      )					->						Union[str, Any]:
       return is_sigopt_available()






   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     str	,						lowercase_				:     int	,						lowercase_				:     str	,						**lowercase_				:     Any  )					->						Any:
       return run_hp_search_sigopt(lowercase_	,						lowercase_	,						lowercase_	,						**lowercase_  )







   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     Optional[Any]  )					->						Optional[Any]:
       return default_hp_space_sigopt(lowercase_  )



class   snake_case_       (		__A       ):
   __A	:		Any         =				"wandb"






   @staticmethod
   def       __UpperCamelCase     (      )					->						List[Any]:
       return is_wandb_available()






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     int	,						lowercase_				:     str	,						**lowercase_				:     Dict  )					->						Dict:
       return run_hp_search_wandb(lowercase_	,						lowercase_	,						lowercase_	,						**lowercase_  )







   def       __UpperCamelCase     (      self				:     List[str]	,						lowercase_				:     Optional[Any]  )					->						Dict:
       return default_hp_space_wandb(lowercase_  )



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

def 			lowercase_				(			):
    lowercase__ :   Dict    					=				[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
    if len(_lowerCamelCase) > 0:
        lowercase__ :   List[str]    					=				available_backends[0].name
        if len(_lowerCamelCase) > 1:
            logger.info(
                f'''{len(_lowerCamelCase)} 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()))











"""simple docstring"""


import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     Optional[int],       _lowerCamelCase :     Optional[int]=2,       _lowerCamelCase :     Optional[int]=3,       _lowerCamelCase :     int=64,       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     __A									= np.random.default_rng(_lowerCamelCase  )
     __A									= length
     __A									= rng.normal(size=(length,)  ).astype(np.floataa  )
     __A									= a * self.x + b + rng.normal(scale=0.1,       size=(length,)  ).astype(np.floataa  )


   def __len__(	self :     str  ):

     '''simple docstring'''
     return self.length






   def __getitem__(	self :     Dict,       _lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     return {"x": self.x[i], "y": self.y[i]}





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Tuple=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     Optional[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     Optional[Any]=None  ):

     '''simple docstring'''
     if self.first_batch:
       print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
       __A									= False
     return x * self.a[0] + self.b[0]





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     str,       _lowerCamelCase :     Optional[Any]=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     List[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     if self.first_batch:
       print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
       __A									= False
     return x * self.a + self.b





def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase = 1_6 ):




  """simple docstring"""







  from datasets import load_dataset
  from transformers import AutoTokenizer

  __A									= AutoTokenizer.from_pretrained('''bert-base-cased''' )
  __A									= {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
  __A									= load_dataset('''csv'''		,       data_files=__UpperCamelCase )
  __A									= datasets['''train'''].unique('''label''' )

  __A									= {v: i for i, v in enumerate(__UpperCamelCase )}

  def tokenize_function(__UpperCamelCase ):
    # max_length=None => use the model max length (it's actually the default)
    __A									= tokenizer(
        examples['''sentence1''']		,       examples['''sentence2''']		,       truncation=__UpperCamelCase		,       max_length=__UpperCamelCase		,       padding='''max_length''' )
    if "label" in examples:
      __A									= [label_to_id[l] for l in examples['''label''']]
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  __A									= datasets.map(
      __UpperCamelCase		,       batched=__UpperCamelCase		,       remove_columns=['''sentence1''', '''sentence2''', '''label''']		,       )

  def collate_fn(__UpperCamelCase ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(__UpperCamelCase		,       padding='''max_length'''		,       max_length=1_2_8		,       return_tensors='''pt''' )
    return tokenizer.pad(__UpperCamelCase		,       padding='''longest'''		,       return_tensors='''pt''' )

  # Instantiate dataloaders.
  __A									= DataLoader(tokenized_datasets['''train''']		,       shuffle=__UpperCamelCase		,       collate_fn=__UpperCamelCase		,       batch_size=2 )
  __A									= DataLoader(tokenized_datasets['''validation''']		,       shuffle=__UpperCamelCase		,       collate_fn=__UpperCamelCase		,       batch_size=1 )

  return train_dataloader, eval_dataloader




