





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 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					@property
					def 	_lowerCamelCase						(  self       ):
									torch.manual_seed(0       )
									__a	:  List[str]         =					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 	_lowerCamelCase						(  self       ):
									__a	:  str         =					self.dummy_uncond_unet
									__a	:  Any         =					PNDMScheduler()

									__a	:  str         =					PNDMPipeline(unet=_a		,     scheduler=_a       )
									pndm.to(_a       )
									pndm.set_progress_bar_config(disable=_a       )

									__a	:  Optional[int]         =					torch.manual_seed(0       )
									__a	:  Dict         =					pndm(generator=_a		,     num_inference_steps=20		,     output_type='''numpy'''       ).images

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

									__a	:  str         =					image[0, -3:, -3:, -1]
									__a	:  List[str]         =					image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									__a	:  Optional[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 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					'''google/ddpm-cifar10-32'''

									__a	:  List[str]         =					UNetaDModel.from_pretrained(_a       )
									__a	:  Tuple         =					PNDMScheduler()

									__a	:  int         =					PNDMPipeline(unet=_a		,     scheduler=_a       )
									pndm.to(_a       )
									pndm.set_progress_bar_config(disable=_a       )
									__a	:  List[Any]         =					torch.manual_seed(0       )
									__a	:  Optional[Any]         =					pndm(generator=_a		,     output_type='''numpy'''       ).images

									__a	:  Optional[Any]         =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									__a	:  Dict         =					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"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A	     =					{
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InstructBlipQFormerModel''',
		    '''InstructBlipPreTrainedModel''',
		    '''InstructBlipForConditionalGeneration''',
		    '''InstructBlipVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    InstructBlipConfig,
		    InstructBlipQFormerConfig,
		    InstructBlipVisionConfig,
		)
		from .processing_instructblip import InstructBlipProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_instructblip import (
				    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InstructBlipForConditionalGeneration,
				    InstructBlipPreTrainedModel,
				    InstructBlipQFormerModel,
				    InstructBlipVisionModel,
				)

else:
		import sys

		A	     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)