

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 SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  lowerCamelCase   :      str
  lowerCamelCase   :      str										=None







  @staticmethod
  def 	SCREAMING_SNAKE_CASE   (      )							->     Any:






     """simple docstring"""







     raise NotImplementedError







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   str	,							**lowerCAmelCase			:   Union[str, Any]   )							->     Tuple:






     """simple docstring"""







     raise NotImplementedError







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Optional[int]   )							->     List[Any]:






     """simple docstring"""







     raise NotImplementedError







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     int:






     """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			:   Dict   )							->     Dict:






     """simple docstring"""







     return f'''`pip install {cls.pip_package or cls.name}`'''






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										="optuna"







  @staticmethod
  def 	SCREAMING_SNAKE_CASE   (      )							->     str:






     """simple docstring"""







     return is_optuna_available()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   str	,							**lowerCAmelCase			:   int   )							->     int:






     """simple docstring"""







     return run_hp_search_optuna(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							**lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     return default_hp_space_optuna(lowerCAmelCase   )






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Dict										="ray"
  lowerCamelCase   :      Optional[Any]										="'ray[tune]'"







  @staticmethod
  def 	SCREAMING_SNAKE_CASE   (      )							->     str:






     """simple docstring"""







     return is_ray_available()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   str	,							**lowerCAmelCase			:   Optional[int]   )							->     Any:






     """simple docstring"""







     return run_hp_search_ray(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							**lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Optional[Any]   )							->     str:






     """simple docstring"""







     return default_hp_space_ray(lowerCAmelCase   )






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Any										="sigopt"







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






     """simple docstring"""







     return is_sigopt_available()







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   str	,							**lowerCAmelCase			:   Any   )							->     Optional[Any]:






     """simple docstring"""







     return run_hp_search_sigopt(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							**lowerCAmelCase   )







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






     """simple docstring"""







     return default_hp_space_sigopt(lowerCAmelCase   )






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      List[Any]										="wandb"







  @staticmethod
  def 	SCREAMING_SNAKE_CASE   (      )							->     Tuple:






     """simple docstring"""







     return is_wandb_available()







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






     """simple docstring"""







     return run_hp_search_wandb(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							**lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   List[Any]   )							->     Tuple:






     """simple docstring"""







     return default_hp_space_wandb(lowerCAmelCase   )






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





def      snake_case_				()      ->     str:
   __lowerCAmelCase  :      int    =						[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
   if len(__A					) > 0:
      __lowerCAmelCase  :      List[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()					)					)





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileViTImageProcessor



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   List[str]=3	,							lowerCAmelCase			:   int=18	,							lowerCAmelCase			:   int=30	,							lowerCAmelCase			:   Optional[int]=4_00	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Tuple=None	,							lowerCAmelCase			:   Any=True	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						size if size is not None else {"""shortest_edge""": 20}
     __lowerCAmelCase  :      Any    =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
     __lowerCAmelCase  :      str    =						parent
     __lowerCAmelCase  :      List[str]    =						batch_size
     __lowerCAmelCase  :      int    =						num_channels
     __lowerCAmelCase  :      List[str]    =						image_size
     __lowerCAmelCase  :      Optional[int]    =						min_resolution
     __lowerCAmelCase  :      List[str]    =						max_resolution
     __lowerCAmelCase  :      List[Any]    =						do_resize
     __lowerCAmelCase  :      Optional[int]    =						size
     __lowerCAmelCase  :      List[Any]    =						do_center_crop
     __lowerCAmelCase  :      Optional[Any]    =						crop_size
     __lowerCAmelCase  :      int    =						do_flip_channel_order







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






     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_flip_channel_order": self.do_flip_channel_order,
     }






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[str]										=MobileViTImageProcessor if is_vision_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						MobileViTImageProcessingTester(self   )







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_resize"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""size"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_flip_channel_order"""   )   )







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size	,							{"""shortest_edge""": 20}   )
     self.assertEqual(image_processor.crop_size	,							{"""height""": 18, """width""": 18}   )

     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class.from_dict(self.image_processor_dict	,							size=42	,							crop_size=84   )
     self.assertEqual(image_processor.size	,							{"""shortest_edge""": 42}   )
     self.assertEqual(image_processor.crop_size	,							{"""height""": 84, """width""": 84}   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     int:






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     __lowerCAmelCase  :      Union[str, Any]    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							Image.Image   )

     # Test not batched input
     __lowerCAmelCase  :      List[Any]    =						image_processing(image_inputs[0]	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)

     # Test batched
     __lowerCAmelCase  :      str    =						image_processing(lowerCAmelCase	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     __lowerCAmelCase  :      Any    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase	,							numpify=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							np.ndarray   )

     # Test not batched input
     __lowerCAmelCase  :      Optional[Any]    =						image_processing(image_inputs[0]	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)

     # Test batched
     __lowerCAmelCase  :      Tuple    =						image_processing(lowerCAmelCase	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     __lowerCAmelCase  :      str    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase	,							torchify=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							torch.Tensor   )

     # Test not batched input
     __lowerCAmelCase  :      Optional[int]    =						image_processing(image_inputs[0]	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)

     # Test batched
     __lowerCAmelCase  :      Tuple    =						image_processing(lowerCAmelCase	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)


