


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      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			int:




     """simple docstring"""
     torch.manual_seed(0	)
     snake_case_            =       UNetaDModel(
         block_out_channels=(3_2, 6_4)     ,					layers_per_block=2     ,					sample_size=3_2     ,					in_channels=3     ,					out_channels=3     ,					down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,					up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,					)
     return model



  def        lowerCAmelCase__	(  self							:							Dict	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       self.dummy_uncond_unet
     snake_case_            =       PNDMScheduler()

     snake_case_            =       PNDMPipeline(unet=_lowerCAmelCase     ,					scheduler=_lowerCAmelCase	)
     pndm.to(_lowerCAmelCase	)
     pndm.set_progress_bar_config(disable=_lowerCAmelCase	)

     snake_case_            =       torch.manual_seed(0	)
     snake_case_            =       pndm(generator=_lowerCAmelCase     ,					num_inference_steps=2_0     ,					output_type="numpy"	).images

     snake_case_            =       torch.manual_seed(0	)
     snake_case_            =       pndm(generator=_lowerCAmelCase     ,					num_inference_steps=2_0     ,					output_type="numpy"     ,					return_dict=_lowerCAmelCase	)[0]

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

     assert image.shape == (1, 3_2, 3_2, 3)
     snake_case_            =       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      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  def        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       "google/ddpm-cifar10-32"

     snake_case_            =       UNetaDModel.from_pretrained(_lowerCAmelCase	)
     snake_case_            =       PNDMScheduler()

     snake_case_            =       PNDMPipeline(unet=_lowerCAmelCase     ,					scheduler=_lowerCAmelCase	)
     pndm.to(_lowerCAmelCase	)
     pndm.set_progress_bar_config(disable=_lowerCAmelCase	)
     snake_case_            =       torch.manual_seed(0	)
     snake_case_            =       pndm(generator=_lowerCAmelCase     ,					output_type="numpy"	).images

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

     assert image.shape == (1, 3_2, 3_2, 3)
     snake_case_            =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]	)

     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_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE						:str			=  {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE						:List[str]			=  [
       '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechForCTC''',
       '''UniSpeechForPreTraining''',
       '''UniSpeechForSequenceClassification''',
       '''UniSpeechModel''',
       '''UniSpeechPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech import (
          UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechForCTC,
          UniSpeechForPreTraining,
          UniSpeechForSequenceClassification,
          UniSpeechModel,
          UniSpeechPreTrainedModel,
      )

else:
   import sys

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




