





import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			lowercase				(							a_		,			unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	Tuple								=	DDIMPipeline
						_UpperCamelCase  :	int								=	UNCONDITIONAL_IMAGE_GENERATION_PARAMS
						_UpperCamelCase  :	int								=	PipelineTesterMixin.required_optional_params - {
						    "num_images_per_prompt",
						    "latents",
						    "callback",
						    "callback_steps",
						}
						_UpperCamelCase  :	Optional[int]								=	UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
						_UpperCamelCase  :	List[str]								=	False







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			List[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') ,				)
										_snake_case :			List[Any]  						=   DDIMScheduler()
										_snake_case :			Union[str, Any]  						=   {'unet': unet, 'scheduler': scheduler}
										return components







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		List[str]=0					):





										'''simple docstring'''







										if str(lowerCamelCase_					).startswith('mps'					):
														_snake_case :			str  						=   torch.manual_seed(lowerCamelCase_					)
										else:
														_snake_case :			Any  						=   torch.Generator(device=lowerCamelCase_					).manual_seed(lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   {
										    'batch_size': 1,
										    'generator': generator,
										    'num_inference_steps': 2,
										    'output_type': 'numpy',
										}
										return inputs







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   'cpu'

										_snake_case :			List[Any]  						=   self.get_dummy_components()
										_snake_case :			int  						=   self.pipeline_class(**lowerCamelCase_					)
										pipe.to(lowerCamelCase_					)
										pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			Dict  						=   self.get_dummy_inputs(lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   pipe(**lowerCamelCase_					).images
										_snake_case :			Optional[int]  						=   image[0, -3:, -3:, -1]

										self.assertEqual(image.shape ,				(1, 32, 32, 3)					)
										_snake_case :			int  						=   np.array(
										    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]					)
										_snake_case :			Union[str, Any]  						=   np.abs(image_slice.flatten() - expected_slice					).max()
										self.assertLessEqual(lowerCamelCase_ ,				1e-3					)







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										super().test_save_load_local(expected_max_difference=3e-3					)







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										super().test_save_load_optional_components(expected_max_difference=3e-3					)







						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										super().test_inference_batch_single_identical(expected_max_diff=3e-3					)



@slow
@require_torch_gpu
class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   'google/ddpm-cifar10-32'

										_snake_case :			Any  						=   UNetaDModel.from_pretrained(lowerCamelCase_					)
										_snake_case :			List[Any]  						=   DDIMScheduler()

										_snake_case :			List[str]  						=   DDIMPipeline(unet=lowerCamelCase_ ,				scheduler=lowerCamelCase_					)
										ddim.to(lowerCamelCase_					)
										ddim.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			List[str]  						=   torch.manual_seed(0					)
										_snake_case :			List[Any]  						=   ddim(generator=lowerCamelCase_ ,				eta=0.0 ,				output_type='numpy'					).images

										_snake_case :			Tuple  						=   image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										_snake_case :			Union[str, Any]  						=   np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]					)

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







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   'google/ddpm-ema-bedroom-256'

										_snake_case :			Union[str, Any]  						=   UNetaDModel.from_pretrained(lowerCamelCase_					)
										_snake_case :			Optional[int]  						=   DDIMScheduler.from_pretrained(lowerCamelCase_					)

										_snake_case :			str  						=   DDIMPipeline(unet=lowerCamelCase_ ,				scheduler=lowerCamelCase_					)
										ddpm.to(lowerCamelCase_					)
										ddpm.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   torch.manual_seed(0					)
										_snake_case :			Optional[Any]  						=   ddpm(generator=lowerCamelCase_ ,				output_type='numpy'					).images

										_snake_case :			Dict  						=   image[0, -3:, -3:, -1]

										assert image.shape == (1, 2_56, 2_56, 3)
										_snake_case :			Dict  						=   np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]					)

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













import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


lowercase_		:				str				=						logging.get_logger(__name__)


class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		int ,				*lowerCamelCase_			:		str ,				**lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										warnings.warn(
										    'The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
										    ' Please use SegformerImageProcessor instead.' ,				lowerCamelCase_ ,				)
										super().__init__(*lowerCamelCase_ ,				**lowerCamelCase_					)






