



'''simple docstring'''







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 				__lowerCAmelCase(							unittest.TestCase      ):






			@property
			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						: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 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      'cpu'
					SCREAMING_SNAKE_CASE_						:int										=      self.dummy_uncond_unet
					SCREAMING_SNAKE_CASE_						:str										=      DDPMScheduler()

					SCREAMING_SNAKE_CASE_						:Tuple										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:str										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'      ,       return_dict=SCREAMING_SNAKE_CASE	)[0]

					SCREAMING_SNAKE_CASE_						:Tuple										=      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_						:Any										=      np.array(
					    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.0_00, 1.1_99E-03, 2.6_48E-04, 5.1_01E-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 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.dummy_uncond_unet
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      DDPMScheduler(prediction_type='sample'	)

					SCREAMING_SNAKE_CASE_						:Any										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:int										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	)[0]

					SCREAMING_SNAKE_CASE_						:List[Any]										=      image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE_						:Dict										=      image_eps[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Tuple										=      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 				__lowerCAmelCase(							unittest.TestCase      ):






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      'google/ddpm-cifar10-32'

					SCREAMING_SNAKE_CASE_						:Any										=      UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DDPMScheduler.from_pretrained(SCREAMING_SNAKE_CASE	)

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

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       output_type='numpy'	).images

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

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Dict										=      np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]	)
					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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE	:							Any           =  {'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							str           =  ['''PegasusTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  ['''PegasusTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PegasusForCausalLM''',
		    '''PegasusForConditionalGeneration''',
		    '''PegasusModel''',
		    '''PegasusPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''TFPegasusForConditionalGeneration''',
		    '''TFPegasusModel''',
		    '''TFPegasusPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''FlaxPegasusForConditionalGeneration''',
		    '''FlaxPegasusModel''',
		    '''FlaxPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus import PegasusTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus_fast import PegasusTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pegasus import (
				    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PegasusForCausalLM,
				    PegasusForConditionalGeneration,
				    PegasusModel,
				    PegasusPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_pegasus import (
				    FlaxPegasusForConditionalGeneration,
				    FlaxPegasusModel,
				    FlaxPegasusPreTrainedModel,
				)

else:
		import sys

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

