






"""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        UpperCamelCase			(	unittest.TestCase      ):


 @property
 def _lowercase	(     self		:						Optional[int]							)					->       Optional[Any]:
   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 _lowercase	(     self		:						Tuple							)					->       Optional[Any]:
   _a  :       int             =	self.dummy_uncond_unet
   _a  :       List[str]             =	KarrasVeScheduler()

   _a  :       Union[str, Any]             =	KarrasVePipeline(unet=UpperCAmelCase__							,						scheduler=UpperCAmelCase__							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       Optional[Any]             =	pipe(num_inference_steps=2							,						generator=UpperCAmelCase__							,						output_type="""numpy"""							).images

   _a  :       Union[str, Any]             =	torch.manual_seed(0							)
   _a  :       List[Any]             =	pipe(num_inference_steps=2							,						generator=UpperCAmelCase__							,						output_type="""numpy"""							,						return_dict=UpperCAmelCase__							)[0]

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

   assert image.shape == (1, 32, 32, 3)
   _a  :       List[str]             =	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        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						List[Any]							)					->       Tuple:
   _a  :       int             =	"""google/ncsnpp-celebahq-256"""
   _a  :       Optional[Any]             =	UNetaDModel.from_pretrained(UpperCAmelCase__							)
   _a  :       str             =	KarrasVeScheduler()

   _a  :       Optional[int]             =	KarrasVePipeline(unet=UpperCAmelCase__							,						scheduler=UpperCAmelCase__							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Optional[Any]             =	torch.manual_seed(0							)
   _a  :       Optional[int]             =	pipe(num_inference_steps=20							,						generator=UpperCAmelCase__							,						output_type="""numpy"""							).images

   _a  :       List[str]             =	image[0, -3:, -3:, -1]
   assert image.shape == (1, 256, 256, 3)
   _a  :       Dict             =	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









"""simple docstring"""




_snake_case          =   '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_snake_case          =   [{'type': 'code', 'content': INSTALL_CONTENT}]
_snake_case          =   {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

