



import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()



class        __snake_case		(  unittest.TestCase		):




							@property
							def      UpperCAmelCase__    ( self  : int):
									torch.manual_seed(0)
									lowerCAmelCase_    :  Tuple             =				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      UpperCAmelCase__    ( self  : int):
									lowerCAmelCase_    :  Optional[Any]             =				'''cpu'''
									lowerCAmelCase_    :  Any             =				self.dummy_uncond_unet
									lowerCAmelCase_    :  List[str]             =				DDPMScheduler()

									lowerCAmelCase_    :  Optional[Any]             =				DDPMPipeline(unet=A_       ,       scheduler=A_)
									ddpm.to(A_)
									ddpm.set_progress_bar_config(disable=A_)

									lowerCAmelCase_    :  Optional[int]             =				torch.Generator(device=A_).manual_seed(0)
									lowerCAmelCase_    :  List[str]             =				ddpm(generator=A_       ,       num_inference_steps=2       ,       output_type='''numpy''').images

									lowerCAmelCase_    :  Dict             =				torch.Generator(device=A_).manual_seed(0)
									lowerCAmelCase_    :  Union[str, Any]             =				ddpm(generator=A_       ,       num_inference_steps=2       ,       output_type='''numpy'''       ,       return_dict=A_)[0]

									lowerCAmelCase_    :  Union[str, Any]             =				image[0, -3:, -3:, -1]
									lowerCAmelCase_    :  List[Any]             =				image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									lowerCAmelCase_    :  List[Any]             =				np.array(
									    [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04])

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







							def      UpperCAmelCase__    ( self  : int):
									lowerCAmelCase_    :  List[Any]             =				self.dummy_uncond_unet
									lowerCAmelCase_    :  Dict             =				DDPMScheduler(prediction_type='''sample''')

									lowerCAmelCase_    :  str             =				DDPMPipeline(unet=A_       ,       scheduler=A_)
									ddpm.to(A_)
									ddpm.set_progress_bar_config(disable=A_)

									lowerCAmelCase_    :  Tuple             =				torch.manual_seed(0)
									lowerCAmelCase_    :  int             =				ddpm(generator=A_       ,       num_inference_steps=2       ,       output_type='''numpy''').images

									lowerCAmelCase_    :  Tuple             =				torch.manual_seed(0)
									lowerCAmelCase_    :  Tuple             =				ddpm(generator=A_       ,       num_inference_steps=2       ,       output_type='''numpy''')[0]

									lowerCAmelCase_    :  Union[str, Any]             =				image[0, -3:, -3:, -1]
									lowerCAmelCase_    :  Union[str, Any]             =				image_eps[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									lowerCAmelCase_    :  List[str]             =				1e-2 if torch_device != '''mps''' else 3e-2
									assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance





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




							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  Optional[Any]             =				'''google/ddpm-cifar10-32'''

									lowerCAmelCase_    :  str             =				UNetaDModel.from_pretrained(A_)
									lowerCAmelCase_    :  List[Any]             =				DDPMScheduler.from_pretrained(A_)

									lowerCAmelCase_    :  Union[str, Any]             =				DDPMPipeline(unet=A_       ,       scheduler=A_)
									ddpm.to(A_)
									ddpm.set_progress_bar_config(disable=A_)

									lowerCAmelCase_    :  Optional[int]             =				torch.manual_seed(0)
									lowerCAmelCase_    :  List[Any]             =				ddpm(generator=A_       ,       output_type='''numpy''').images

									lowerCAmelCase_    :  List[Any]             =				image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									lowerCAmelCase_    :  List[str]             =				np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385])
									assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A__						:						List[str]          =      {
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						int          =      ['''SegformerFeatureExtractor''']
						A__						:						Dict          =      ['''SegformerImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						Any          =      [
						    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SegformerDecodeHead''',
						    '''SegformerForImageClassification''',
						    '''SegformerForSemanticSegmentation''',
						    '''SegformerLayer''',
						    '''SegformerModel''',
						    '''SegformerPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						List[Any]          =      [
						    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFSegformerDecodeHead''',
						    '''TFSegformerForImageClassification''',
						    '''TFSegformerForSemanticSegmentation''',
						    '''TFSegformerModel''',
						    '''TFSegformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_segformer import SegformerFeatureExtractor
												from .image_processing_segformer import SegformerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_segformer import (
												    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SegformerDecodeHead,
												    SegformerForImageClassification,
												    SegformerForSemanticSegmentation,
												    SegformerLayer,
												    SegformerModel,
												    SegformerPreTrainedModel,
												)
						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_segformer import (
												    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSegformerDecodeHead,
												    TFSegformerForImageClassification,
												    TFSegformerForSemanticSegmentation,
												    TFSegformerModel,
												    TFSegformerPreTrainedModel,
												)

else:
						import sys

						A__						:						Dict          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





