




'''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   __UpperCamelCase       ( unittest.TestCase				):






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              torch.manual_seed(0  )
              lowerCamelCase_							=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  ):



              """simple docstring"""







              lowerCamelCase_							=self.dummy_uncond_unet
              lowerCamelCase_							=PNDMScheduler()

              lowerCamelCase_							=PNDMPipeline(unet=UpperCamelCase__,		scheduler=UpperCamelCase__  )
              pndm.to(UpperCamelCase__  )
              pndm.set_progress_bar_config(disable=UpperCamelCase__  )

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

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

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

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






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							='''google/ddpm-cifar10-32'''

              lowerCamelCase_							=UNetaDModel.from_pretrained(UpperCamelCase__  )
              lowerCamelCase_							=PNDMScheduler()

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

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

              assert image.shape == (1, 32, 32, 3)
              lowerCamelCase_							=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


__lowerCAmelCase			:     Any           =   {
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     int           =   [
        'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MCTCTForCTC',
        'MCTCTModel',
        'MCTCTPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
    from .feature_extraction_mctct import MCTCTFeatureExtractor
    from .processing_mctct import MCTCTProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
    import sys

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

