
"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class       lowerCAmelCase_		(				unittest.TestCase ):



							'''simple docstring'''





							@property
							def    A__						( self				)       ->  Union[str, Any]:
													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				)       ->  str:
													__lowerCAmelCase     =  self.dummy_uncond_unet
													__lowerCAmelCase     =  KarrasVeScheduler()

													__lowerCAmelCase     =  KarrasVePipeline(unet=snake_case_    ,				scheduler=snake_case_				)
													pipe.to(snake_case_				)
													pipe.set_progress_bar_config(disable=snake_case_				)

													__lowerCAmelCase     =  torch.manual_seed(0				)
													__lowerCAmelCase     =  pipe(num_inference_steps=2    ,				generator=snake_case_    ,				output_type="""numpy"""				).images

													__lowerCAmelCase     =  torch.manual_seed(0				)
													__lowerCAmelCase     =  pipe(num_inference_steps=2    ,				generator=snake_case_    ,				output_type="""numpy"""    ,				return_dict=snake_case_				)[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       lowerCAmelCase_		(				unittest.TestCase ):



							'''simple docstring'''





							def    A__						( self				)       ->  int:
													__lowerCAmelCase     =  """google/ncsnpp-celebahq-256"""
													__lowerCAmelCase     =  UNetaDModel.from_pretrained(snake_case_				)
													__lowerCAmelCase     =  KarrasVeScheduler()

													__lowerCAmelCase     =  KarrasVePipeline(unet=snake_case_    ,				scheduler=snake_case_				)
													pipe.to(snake_case_				)
													pipe.set_progress_bar_config(disable=snake_case_				)

													__lowerCAmelCase     =  torch.manual_seed(0				)
													__lowerCAmelCase     =  pipe(num_inference_steps=20    ,				generator=snake_case_    ,				output_type="""numpy"""				).images

													__lowerCAmelCase     =  image[0, -3:, -3:, -1]
													assert image.shape == (1, 256, 256, 3)
													__lowerCAmelCase     =  np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]				)

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


"""simple docstring"""
import warnings

from ..trainer import Trainer
from ..utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_=None    ,				**snake_case_				)       ->  Optional[Any]:
													warnings.warn(
													    """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
													    """instead."""    ,				snake_case_    ,				)
													super().__init__(args=snake_case_    ,				**snake_case_				)
