






"""simple docstring"""



# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A_    =							re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')






@total_ordering
@dataclass
class 							__SCREAMING_SNAKE_CASE						:
							snake_case_     =							42
							snake_case_     =							None
							snake_case_     =							None
							snake_case_     =							None
							snake_case_     =							None


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





													A__  ,  A__  ,  A__						:  List[Any]		  =  _str_to_version_tuple(self.version_str							)


							def __repr__(			self      :						int							):
													'''simple docstring'''





													return F'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'


							@property
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													return self.major, self.minor, self.patch


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





													if isinstance(snake_case			,    snake_case							):
																			return Version(snake_case							)
													elif isinstance(snake_case			,    snake_case							):
																			return other
													raise TypeError(F'{other} (type {type(snake_case							)}) cannot be compared to version.'							)


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





													try:
																			A__						:  Tuple		  =  self._validate_operand(snake_case							)
													except (TypeError, ValueError):
																			return False
													else:
																			return self.tuple == other.tuple


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





													A__						:  Optional[Any]		  =  self._validate_operand(snake_case							)
													return self.tuple < other.tuple


							def __hash__(			self      :						List[str]							):
													'''simple docstring'''





													return hash(_version_tuple_to_str(self.tuple							)							)


							@classmethod
							def 						_UpperCamelCase       (			cls      :						List[Any]			,    snake_case      :						Any							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  {f.name for f in dataclasses.fields(cls							)}
													return cls(**{k: v for k, v in dic.items() if k in field_names}							)


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





													return self.version_str




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any]      )		->List[str]:
						A__						:  Tuple		  =  _VERSION_REG.match(UpperCAmelCase__      )
						if not res:
												raise ValueError(f'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'      )
						return tuple(int(UpperCAmelCase__      ) for v in [res.group("""major"""      ), res.group("""minor"""      ), res.group("""patch"""      )]      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int]      )		->Union[str, Any]:
						return ".".join(str(UpperCAmelCase__      ) for v in version_tuple      )














"""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 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


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





													torch.manual_seed(0							)
													A__						:  Optional[Any]		  =  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 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  self.dummy_uncond_unet
													A__						:  Dict		  =  ScoreSdeVeScheduler()

													A__						:  str		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Optional[int]		  =  torch.manual_seed(0							)
													A__						:  Any		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  List[str]		  =  torch.manual_seed(0							)
													A__						:  int		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case			,    return_dict=snake_case							)[
													    0
													]

													A__						:  int		  =  image[0, -3:, -3:, -1]
													A__						:  Optional[Any]		  =  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													A__						:  Optional[Any]		  =  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 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


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





													A__						:  Union[str, Any]		  =  """google/ncsnpp-church-256"""
													A__						:  Tuple		  =  UNetaDModel.from_pretrained(snake_case							)

													A__						:  int		  =  ScoreSdeVeScheduler.from_pretrained(snake_case							)

													A__						:  List[Any]		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Dict		  =  torch.manual_seed(0							)
													A__						:  Optional[int]		  =  sde_ve(num_inference_steps=10			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  Optional[int]		  =  image[0, -3:, -3:, -1]

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

													A__						:  Dict		  =  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






