






"""simple docstring"""



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 LevitImageProcessor






class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							def __init__(			self      :						List[Any]			,    snake_case      :						Dict			,    snake_case      :						Any=7			,    snake_case      :						Union[str, Any]=3			,    snake_case      :						int=18			,    snake_case      :						Optional[int]=30			,    snake_case      :						Optional[int]=400			,    snake_case      :						int=True			,    snake_case      :						str=None			,    snake_case      :						Tuple=True			,    snake_case      :						Optional[Any]=None			,    snake_case      :						Tuple=True			,    snake_case      :						Dict=[0.5, 0.5, 0.5]			,    snake_case      :						Tuple=[0.5, 0.5, 0.5]			,    ):
													'''simple docstring'''





													A__						:  Optional[int]		  =  size if size is not None else {"""shortest_edge""": 18}
													A__						:  Dict		  =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
													A__						:  str		  =  parent
													A__						:  str		  =  batch_size
													A__						:  int		  =  num_channels
													A__						:  str		  =  image_size
													A__						:  str		  =  min_resolution
													A__						:  Union[str, Any]		  =  max_resolution
													A__						:  List[str]		  =  do_resize
													A__						:  Optional[Any]		  =  size
													A__						:  int		  =  do_center_crop
													A__						:  Tuple		  =  crop_size
													A__						:  Tuple		  =  do_normalize
													A__						:  Union[str, Any]		  =  image_mean
													A__						:  int		  =  image_std


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}






@require_torch
@require_vision
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		unittest.TestCase		):
							snake_case_     =							LevitImageProcessor if is_vision_available() else None


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  LevitImageProcessingTester(self							)


							@property
							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													return self.image_processor_tester.prepare_image_processor_dict()


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Tuple		  =  self.image_processing_class(**self.image_processor_dict							)
													self.assertTrue(hasattr(snake_case			,    """image_mean"""							)							)
													self.assertTrue(hasattr(snake_case			,    """image_std"""							)							)
													self.assertTrue(hasattr(snake_case			,    """do_normalize"""							)							)
													self.assertTrue(hasattr(snake_case			,    """do_resize"""							)							)
													self.assertTrue(hasattr(snake_case			,    """do_center_crop"""							)							)
													self.assertTrue(hasattr(snake_case			,    """size"""							)							)


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													A__						:  str		  =  self.image_processing_class.from_dict(self.image_processor_dict							)
													self.assertEqual(image_processor.size			,    {"""shortest_edge""": 18}							)
													self.assertEqual(image_processor.crop_size			,    {"""height""": 18, """width""": 18}							)

													A__						:  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 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													pass


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Any		  =  self.image_processing_class(**self.image_processor_dict							)
													# create random PIL images
													A__						:  int		  =  prepare_image_inputs(self.image_processor_tester			,    equal_resolution=snake_case							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case			,    Image.Image							)

													# Test not batched input
													A__						:  Dict		  =  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
													A__						:  int		  =  image_processing(snake_case			,    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 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													A__						:  Dict		  =  self.image_processing_class(**self.image_processor_dict							)
													# create random numpy tensors
													A__						:  Optional[int]		  =  prepare_image_inputs(self.image_processor_tester			,    equal_resolution=snake_case			,    numpify=snake_case							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case			,    np.ndarray							)

													# Test not batched input
													A__						:  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
													A__						:  Any		  =  image_processing(snake_case			,    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 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.image_processing_class(**self.image_processor_dict							)
													# create random PyTorch tensors
													A__						:  Union[str, Any]		  =  prepare_image_inputs(self.image_processor_tester			,    equal_resolution=snake_case			,    torchify=snake_case							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case			,    torch.Tensor							)

													# Test not batched input
													A__						:  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
													A__						:  Union[str, Any]		  =  image_processing(snake_case			,    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"""],
													    )			,    )














"""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


A_    =							logging.get_logger(__name__)






class 							__SCREAMING_SNAKE_CASE						:
							snake_case_     =							42
							snake_case_     =							None


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Optional[int]							):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''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 						_UpperCamelCase       (			cls      :						Optional[int]							):
													'''simple docstring'''





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






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'optuna'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_optuna_available()


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													return run_hp_search_optuna(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						int							):
													'''simple docstring'''





													return default_hp_space_optuna(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'ray'
							snake_case_     =							'\'ray[tune]\''


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_ray_available()


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Tuple			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						List[Any]							):
													'''simple docstring'''





													return run_hp_search_ray(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						Dict							):
													'''simple docstring'''





													return default_hp_space_ray(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'sigopt'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_sigopt_available()


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return run_hp_search_sigopt(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						Any							):
													'''simple docstring'''





													return default_hp_space_sigopt(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'wandb'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_wandb_available()


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													return run_hp_search_wandb(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return default_hp_space_wandb(snake_case							)




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




def      _lowerCAmelCase		(							)		->str:
						A__						:  List[Any]		  =  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
						if len(UpperCAmelCase__      ) > 0:
												A__						:  Dict		  =  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()      )      )






