




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     A(		unittest.TestCase  ):



 '''simple docstring'''


 @property
 def 							a__					(							self   :    Dict    )  ->      int:

       """simple docstring"""






       torch.manual_seed(0    )
       lowerCamelCase_							=       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 							a__					(							self   :    Optional[Any]    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.dummy_uncond_unet
       lowerCamelCase_							=       ScoreSdeVeScheduler()

       lowerCamelCase_							=       ScoreSdeVePipeline(unet=A_		,				scheduler=A_    )
       sde_ve.to(A_    )
       sde_ve.set_progress_bar_config(disable=A_    )

       lowerCamelCase_							=       torch.manual_seed(0    )
       lowerCamelCase_							=       sde_ve(num_inference_steps=2		,				output_type='numpy'		,				generator=A_    ).images

       lowerCamelCase_							=       torch.manual_seed(0    )
       lowerCamelCase_							=       sde_ve(num_inference_steps=2		,				output_type='numpy'		,				generator=A_		,				return_dict=A_    )[
           0
       ]

       lowerCamelCase_							=       image[0, -3:, -3:, -1]
       lowerCamelCase_							=       image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       lowerCamelCase_							=       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     A(		unittest.TestCase  ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_							=       'google/ncsnpp-church-256'
       lowerCamelCase_							=       UNetaDModel.from_pretrained(A_    )

       lowerCamelCase_							=       ScoreSdeVeScheduler.from_pretrained(A_    )

       lowerCamelCase_							=       ScoreSdeVePipeline(unet=A_		,				scheduler=A_    )
       sde_ve.to(A_    )
       sde_ve.set_progress_bar_config(disable=A_    )

       lowerCamelCase_							=       torch.manual_seed(0    )
       lowerCamelCase_							=       sde_ve(num_inference_steps=10		,				output_type='numpy'		,				generator=A_    ).images

       lowerCamelCase_							=       image[0, -3:, -3:, -1]

       assert image.shape == (1, 256, 256, 3)

       lowerCamelCase_							=       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






import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_lowerCAmelCase  :      int			 ="""scheduler_config.json"""

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   1
					__magic_name__        =   2
					__magic_name__        =   3
					__magic_name__        =   4
					__magic_name__        =   5
					__magic_name__        =   6
					__magic_name__        =   7
					__magic_name__        =   8
					__magic_name__        =   9
					__magic_name__        =   1_0
					__magic_name__        =   1_1
					__magic_name__        =   1_2
					__magic_name__        =   1_3
					__magic_name__        =   1_4

@dataclass
class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   42

class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   SCHEDULER_CONFIG_NAME
					__magic_name__        =   []
					__magic_name__        =   True





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= cls.load_config(
						    pretrained_model_name_or_path=lowerCamelCase__				,      subfolder=lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      return_commit_hash=lowerCamelCase__				,      **lowerCamelCase__				,      )
						return cls.from_config(lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = False				,      **lowerCamelCase__						):
						self.save_config(save_directory=lowerCamelCase__				,      push_to_hub=lowerCamelCase__				,      **lowerCamelCase__						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self._get_compatibles()





					@classmethod
					def    _UpperCAmelCase    (				cls						):
						UpperCAmelCase__:	List[str]									= list(set([cls.__name__] + cls._compatibles						)						)
						UpperCAmelCase__:	Union[str, Any]									= importlib.import_module(__name__.split("."						)[0]						)
						UpperCAmelCase__:	int									= [
						    getattr(lowerCamelCase__				,      lowerCamelCase__						) for c in compatible_classes_str if hasattr(lowerCamelCase__				,      lowerCamelCase__						)
						]
						return compatible_classes