



"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, 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 			snake_case__      (    self   ):
									torch.manual_seed(0   )
									_lowerCamelCase        =				UNetaDModel(
									    block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=3				,       out_channels=3				,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')				,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')				,       )
									return model





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.dummy_uncond_unet
									_lowerCamelCase        =				PNDMScheduler()

									_lowerCamelCase        =				PNDMPipeline(unet=A_				,       scheduler=A_   )
									pndm.to(A_   )
									pndm.set_progress_bar_config(disable=A_   )

									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				pndm(generator=A_				,       num_inference_steps=2_0				,       output_type='''numpy'''   ).images

									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				pndm(generator=A_				,       num_inference_steps=2_0				,       output_type='''numpy'''				,       return_dict=A_   )[0]

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

									assert image.shape == (1, 3_2, 3_2, 3)
									_lowerCamelCase        =				np.array([1.0, 1.0, 0.0, 1.0, 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 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''google/ddpm-cifar10-32'''

									_lowerCamelCase        =				UNetaDModel.from_pretrained(A_   )
									_lowerCamelCase        =				PNDMScheduler()

									_lowerCamelCase        =				PNDMPipeline(unet=A_				,       scheduler=A_   )
									pndm.to(A_   )
									pndm.set_progress_bar_config(disable=A_   )
									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				pndm(generator=A_				,       output_type='''numpy'''   ).images

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

									assert image.shape == (1, 3_2, 3_2, 3)
									_lowerCamelCase        =				np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]   )

									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  :						Dict    							=      logging.get_logger(__name__)


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
									warnings.warn(
									    '''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
									    '''instead.'''				,       lowerCamelCase__				,       )
									super().__init__(args=lowerCamelCase__				,       **lowerCamelCase__   )
