


'''simple docstring'''






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    ):
	@property
	def 							__UpperCAmelCase						(	self  )   ->							Dict:
						torch.manual_seed(0  )
						_a      =							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  )   ->							Optional[int]:
						_a      =							self.dummy_uncond_unet
						_a      =							PNDMScheduler()

						_a      =							PNDMPipeline(unet=__magic_name__ ,			scheduler=__magic_name__  )
						pndm.to(__magic_name__  )
						pndm.set_progress_bar_config(disable=__magic_name__  )

						_a      =							torch.manual_seed(0  )
						_a      =							pndm(generator=__magic_name__ ,			num_inference_steps=20 ,			output_type='numpy'  ).images

						_a      =							torch.manual_seed(0  )
						_a      =							pndm(generator=__magic_name__ ,			num_inference_steps=20 ,			output_type='numpy' ,			return_dict=__magic_name__  )[0]

						_a      =							image[0, -3:, -3:, -1]
						_a      =							image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						_a      =							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    ):
	def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
						_a      =							'google/ddpm-cifar10-32'

						_a      =							UNetaDModel.from_pretrained(__magic_name__  )
						_a      =							PNDMScheduler()

						_a      =							PNDMPipeline(unet=__magic_name__ ,			scheduler=__magic_name__  )
						pndm.to(__magic_name__  )
						pndm.set_progress_bar_config(disable=__magic_name__  )
						_a      =							torch.manual_seed(0  )
						_a      =							pndm(generator=__magic_name__ ,			output_type='numpy'  ).images

						_a      =							image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						_a      =							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_   :      str				    =      {
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NllbMoeForConditionalGeneration",
     "NllbMoeModel",
     "NllbMoePreTrainedModel",
     "NllbMoeTop2Router",
     "NllbMoeSparseMLP",
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 a_   :      str				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

