
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   __UpperCamelCase      (				unittest.TestCase    ):






     """simple docstring"""







     @property
     def       UpperCAmelCase__				(							self				:				Optional[Any]      ):







       """simple docstring"""




       torch.manual_seed(0      )
       __SCREAMING_SNAKE_CASE	:     Optional[int] =						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				:				int      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						self.dummy_uncond_unet
       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						PNDMScheduler()

       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						PNDMPipeline(unet=_A			,		scheduler=_A      )
       pndm.to(_A      )
       pndm.set_progress_bar_config(disable=_A      )

       __SCREAMING_SNAKE_CASE	:     int =						torch.manual_seed(0      )
       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						pndm(generator=_A			,		num_inference_steps=20			,		output_type='''numpy'''      ).images

       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						torch.manual_seed(0      )
       __SCREAMING_SNAKE_CASE	:     Optional[int] =						pndm(generator=_A			,		num_inference_steps=20			,		output_type='''numpy'''			,		return_dict=_A      )[0]

       __SCREAMING_SNAKE_CASE	:     str =						image[0, -3:, -3:, -1]
       __SCREAMING_SNAKE_CASE	:     Dict =						image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						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   __UpperCamelCase      (				unittest.TestCase    ):






     """simple docstring"""







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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						"google/ddpm-cifar10-32"

       __SCREAMING_SNAKE_CASE	:     Dict =						UNetaDModel.from_pretrained(_A      )
       __SCREAMING_SNAKE_CASE	:     int =						PNDMScheduler()

       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						PNDMPipeline(unet=_A			,		scheduler=_A      )
       pndm.to(_A      )
       pndm.set_progress_bar_config(disable=_A      )
       __SCREAMING_SNAKE_CASE	:     List[Any] =						torch.manual_seed(0      )
       __SCREAMING_SNAKE_CASE	:     Tuple =						pndm(generator=_A			,		output_type='''numpy'''      ).images

       __SCREAMING_SNAKE_CASE	:     Any =						image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __SCREAMING_SNAKE_CASE	:     Optional[int] =						np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]      )

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

from typing import TYPE_CHECKING

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


__lowerCamelCase					:	Dict       =  {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Any       =  [
		    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViTMAEForPreTraining''',
		    '''ViTMAELayer''',
		    '''ViTMAEModel''',
		    '''ViTMAEPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Optional[Any]       =  [
		    '''TFViTMAEForPreTraining''',
		    '''TFViTMAEModel''',
		    '''TFViTMAEPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_mae import (
				    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMAEForPreTraining,
				    ViTMAELayer,
				    ViTMAEModel,
				    ViTMAEPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
		import sys

		__lowerCamelCase					:	Any       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
