

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     _a						(unittest.TestCase				):


					'''simple docstring'''






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    Tuple        =		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   __A			( self			):
						A__							:    List[Any]        =		self.dummy_uncond_unet
						A__							:    Dict        =		PNDMScheduler()

						A__							:    int        =		PNDMPipeline(unet=A__    ,       scheduler=A__			)
						pndm.to(A__			)
						pndm.set_progress_bar_config(disable=A__			)

						A__							:    Dict        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		pndm(generator=A__    ,       num_inference_steps=20    ,       output_type="""numpy"""			).images

						A__							:    List[str]        =		torch.manual_seed(0			)
						A__							:    str        =		pndm(generator=A__    ,       num_inference_steps=20    ,       output_type="""numpy"""    ,       return_dict=A__			)[0]

						A__							:    Any        =		image[0, -3:, -3:, -1]
						A__							:    Any        =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						A__							:    Dict        =		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     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    int        =		"""google/ddpm-cifar10-32"""

						A__							:    Union[str, Any]        =		UNetaDModel.from_pretrained(A__			)
						A__							:    int        =		PNDMScheduler()

						A__							:    List[Any]        =		PNDMPipeline(unet=A__    ,       scheduler=A__			)
						pndm.to(A__			)
						pndm.set_progress_bar_config(disable=A__			)
						A__							:    Optional[int]        =		torch.manual_seed(0			)
						A__							:    Union[str, Any]        =		pndm(generator=A__    ,       output_type="""numpy"""			).images

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

						assert image.shape == (1, 32, 32, 3)
						A__							:    int        =		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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_       : Optional[int]         =			{'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : str         =			[
	    'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'IBertForMaskedLM',
	    'IBertForMultipleChoice',
	    'IBertForQuestionAnswering',
	    'IBertForSequenceClassification',
	    'IBertForTokenClassification',
	    'IBertModel',
	    'IBertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ibert import (
		    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    IBertForMaskedLM,
		    IBertForMultipleChoice,
		    IBertForQuestionAnswering,
		    IBertForSequenceClassification,
		    IBertForTokenClassification,
		    IBertModel,
		    IBertPreTrainedModel,
		)

else:
	import sys

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



