

'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   torch.manual_seed(0			)
   __lowercase							  = 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 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = self.dummy_uncond_unet
   __lowercase							  = KarrasVeScheduler()

   __lowercase							  = KarrasVePipeline(unet=lowercase__					,scheduler=lowercase__			)
   pipe.to(lowercase__			)
   pipe.set_progress_bar_config(disable=lowercase__			)

   __lowercase							  = torch.manual_seed(0			)
   __lowercase							  = pipe(num_inference_steps=2					,generator=lowercase__					,output_type='''numpy'''			).images

   __lowercase							  = torch.manual_seed(0			)
   __lowercase							  = pipe(num_inference_steps=2					,generator=lowercase__					,output_type='''numpy'''					,return_dict=lowercase__			)[0]

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

   assert image.shape == (1, 3_2, 3_2, 3)
   __lowercase							  = np.array([0.0, 1.0, 0.0, 0.0, 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 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = '''google/ncsnpp-celebahq-256'''
   __lowercase							  = UNetaDModel.from_pretrained(lowercase__			)
   __lowercase							  = KarrasVeScheduler()

   __lowercase							  = KarrasVePipeline(unet=lowercase__					,scheduler=lowercase__			)
   pipe.to(lowercase__			)
   pipe.set_progress_bar_config(disable=lowercase__			)

   __lowercase							  = torch.manual_seed(0			)
   __lowercase							  = pipe(num_inference_steps=2_0					,generator=lowercase__					,output_type='''numpy'''			).images

   __lowercase							  = image[0, -3:, -3:, -1]
   assert image.shape == (1, 2_5_6, 2_5_6, 3)
   __lowercase							  = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]			)

   assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2









from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowercase  							=					{"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}


if TYPE_CHECKING:
	from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





