


"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)
			__snake_case	:						Tuple						=				UNetaDModel(
			    block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=3				,       out_channels=3				,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')				,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')				,       )
			return model



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				self.dummy_uncond_unet
			__snake_case	:						int						=				ScoreSdeVeScheduler()

			__snake_case	:						Tuple						=				ScoreSdeVePipeline(unet=a_				,       scheduler=a_					)
			sde_ve.to(a_					)
			sde_ve.set_progress_bar_config(disable=a_					)

			__snake_case	:						Any						=				torch.manual_seed(0					)
			__snake_case	:						Union[str, Any]						=				sde_ve(num_inference_steps=2				,       output_type='''numpy'''				,       generator=a_					).images

			__snake_case	:						Optional[int]						=				torch.manual_seed(0					)
			__snake_case	:						str						=				sde_ve(num_inference_steps=2				,       output_type='''numpy'''				,       generator=a_				,       return_dict=a_					)[
			    0
			]

			__snake_case	:						List[str]						=				image[0, -3:, -3:, -1]
			__snake_case	:						Union[str, Any]						=				image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__snake_case	:						Tuple						=				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2



@slow
@require_torch
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				'''google/ncsnpp-church-256'''
			__snake_case	:						List[Any]						=				UNetaDModel.from_pretrained(a_					)

			__snake_case	:						Dict						=				ScoreSdeVeScheduler.from_pretrained(a_					)

			__snake_case	:						Any						=				ScoreSdeVePipeline(unet=a_				,       scheduler=a_					)
			sde_ve.to(a_					)
			sde_ve.set_progress_bar_config(disable=a_					)

			__snake_case	:						Dict						=				torch.manual_seed(0					)
			__snake_case	:						int						=				sde_ve(num_inference_steps=10				,       output_type='''numpy'''				,       generator=a_					).images

			__snake_case	:						Union[str, Any]						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 2_56, 2_56, 3)

			__snake_case	:						Optional[Any]						=				np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2












'''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							:	Optional[Any]			=logging.get_logger(__name__)






class    UpperCAmelCase	:
 __lowercase   		=				42
 __lowercase   		=				None
 @staticmethod
 def      UpperCAmelCase_							(				)->							Dict:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :str						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							str:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :int							)->							Any:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Optional[Any]:
     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      :int							)->							Any:
     return F"`pip install {cls.pip_package or cls.name}`"






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""optuna"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							int:
     return is_optuna_available()
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :str						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :List[Any]							)->							Tuple:
     return run_hp_search_optuna(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



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






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""ray"""
 __lowercase   		=				"""'ray[tune]'"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							str:
     return is_ray_available()
 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :Dict						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :List[str]							)->							int:
     return run_hp_search_ray(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Dict							)->							int:
     return default_hp_space_ray(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""sigopt"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							Union[str, Any]:
     return is_sigopt_available()
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :Union[str, Any]						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							Dict:
     return run_hp_search_sigopt(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Optional[int]							)->							List[str]:
     return default_hp_space_sigopt(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""wandb"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							List[str]:
     return is_wandb_available()
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							List[str]:
     return run_hp_search_wandb(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :str							)->							Dict:
     return default_hp_space_wandb(lowercase_							)

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





def 						UpperCamelCase				(							):
    A__				=			[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
    if len(_lowerCamelCase       ) > 0:
        A__				=			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()       )       )



