"""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


__lowercase		:		str								=				logging.get_logger(__name__)



class   _A						:




    """simple docstring"""




    UpperCamelCase_						:       Tuple      					=       4_2
    UpperCamelCase_						:       Union[str, Any]      					=       None
    @staticmethod
    def 			lowercase       (		)     ->							List[Any]:
         raise NotImplementedError
    def 			lowercase       (		self				:  List[str]						,   A_				:  Optional[int]						,   A_				:  Dict						,   A_				:  Optional[Any]						,   **A_				:  Optional[Any]     )     ->							str:
         raise NotImplementedError
    def 			lowercase       (		self				:  int						,   A_				:  Dict     )     ->							Optional[int]:
         raise NotImplementedError
    def 			lowercase       (		self				:  Optional[Any]     )     ->							str:







         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				:  List[Any]     )     ->							str:
         return f"`pip install {cls.pip_package or cls.name}`"






class   _A						(    UpperCamelCase_       ):




    """simple docstring"""




    UpperCamelCase_						:       str      					=       '''optuna'''
    @staticmethod
    def 			lowercase       (		)     ->							Dict:
         return is_optuna_available()
    def 			lowercase       (		self				:  Dict						,   A_				:  str						,   A_				:  Any						,   A_				:  Optional[int]						,   **A_				:  Tuple     )     ->							Optional[int]:
         return run_hp_search_optuna(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   **__SCREAMING_SNAKE_CASE     )







    def 			lowercase       (		self				:  Any						,   A_				:  Optional[int]     )     ->							int:
         return default_hp_space_optuna(__SCREAMING_SNAKE_CASE     )






class   _A						(    UpperCamelCase_       ):




    """simple docstring"""




    UpperCamelCase_						:       Tuple      					=       '''ray'''
    UpperCamelCase_						:       List[Any]      					=       '''\'ray[tune]\''''
    @staticmethod
    def 			lowercase       (		)     ->							Dict:
         return is_ray_available()
    def 			lowercase       (		self				:  List[str]						,   A_				:  Optional[int]						,   A_				:  Any						,   A_				:  List[Any]						,   **A_				:  List[str]     )     ->							Optional[Any]:
         return run_hp_search_ray(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   **__SCREAMING_SNAKE_CASE     )







    def 			lowercase       (		self				:  Optional[Any]						,   A_				:  Optional[int]     )     ->							Any:
         return default_hp_space_ray(__SCREAMING_SNAKE_CASE     )






class   _A						(    UpperCamelCase_       ):




    """simple docstring"""




    UpperCamelCase_						:       List[Any]      					=       '''sigopt'''
    @staticmethod
    def 			lowercase       (		)     ->							Dict:
         return is_sigopt_available()
    def 			lowercase       (		self				:  List[str]						,   A_				:  Tuple						,   A_				:  List[str]						,   A_				:  Optional[Any]						,   **A_				:  Union[str, Any]     )     ->							str:
         return run_hp_search_sigopt(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   **__SCREAMING_SNAKE_CASE     )







    def 			lowercase       (		self				:  str						,   A_				:  Optional[int]     )     ->							Any:
         return default_hp_space_sigopt(__SCREAMING_SNAKE_CASE     )






class   _A						(    UpperCamelCase_       ):




    """simple docstring"""




    UpperCamelCase_						:       Any      					=       '''wandb'''
    @staticmethod
    def 			lowercase       (		)     ->							List[str]:
         return is_wandb_available()
    def 			lowercase       (		self				:  str						,   A_				:  Optional[Any]						,   A_				:  Union[str, Any]						,   A_				:  Optional[int]						,   **A_				:  List[Any]     )     ->							Dict:
         return run_hp_search_wandb(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   **__SCREAMING_SNAKE_CASE     )







    def 			lowercase       (		self				:  Dict						,   A_				:  Any     )     ->							Union[str, Any]:
         return default_hp_space_wandb(__SCREAMING_SNAKE_CASE     )






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





def  SCREAMING_SNAKE_CASE							(  ):
     __snake_case				=				[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
     if len(UpperCamelCase__) > 0:
          __snake_case				=				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()))

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 3_2    ,    __SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize
           UpperCamelCase__     : Tuple											=					do_rescale
           UpperCamelCase__     : Dict											=					size_divisor
           UpperCamelCase__     : str											=					resample
           super().__init__(**__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					get_image_size(__SCREAMING_SNAKE_CASE	)
           # Rounds the height and width down to the closest multiple of size_divisor
           UpperCamelCase__     : List[Any]											=					height // size_divisor * size_divisor
           UpperCamelCase__     : Dict											=					width // size_divisor * size_divisor
           UpperCamelCase__     : str											=					resize(__SCREAMING_SNAKE_CASE    ,    (new_h, new_w)    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           return image






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           return rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> BatchFeature:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					size_divisor if size_divisor is not None else self.size_divisor
           UpperCamelCase__     : List[str]											=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                 raise ValueError('''size_divisor is required for resizing'''	)

           UpperCamelCase__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

           if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError('''Invalid image(s)'''	)

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for img in images]

           if do_resize:
                 UpperCamelCase__     : Optional[int]											=					[self.resize(__SCREAMING_SNAKE_CASE    ,    size_divisor=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_rescale:
                 UpperCamelCase__     : Union[str, Any]											=					[self.rescale(__SCREAMING_SNAKE_CASE    ,    scale=1 / 2_5_5	) for image in images]

           UpperCamelCase__     : List[str]											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Optional[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)


