"""simple docstring"""







import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()






class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @property
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					1
      __lowerCamelCase    		=					3
      __lowerCamelCase    		=					(32, 32)

      __lowerCamelCase    		=					floats_tensor((batch_size, num_channels) + sizes ,				rng=random.Random(0	)	).to(_SCREAMING_SNAKE_CASE	)
      return image







   @property
   def    _A				(	self	):


      '''simple docstring'''

      torch.manual_seed(0	)
      __lowerCamelCase    		=					UNetaDModel(
          block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=6 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
      return model







   @property
   def    _A				(	self	):


      '''simple docstring'''

      torch.manual_seed(0	)
      __lowerCamelCase    		=					VQModel(
          block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,				latent_channels=3 ,				)
      return model







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					'cpu'
      __lowerCamelCase    		=					self.dummy_uncond_unet
      __lowerCamelCase    		=					DDIMScheduler()
      __lowerCamelCase    		=					self.dummy_vq_model

      __lowerCamelCase    		=					LDMSuperResolutionPipeline(unet=_SCREAMING_SNAKE_CASE ,				vqvae=_SCREAMING_SNAKE_CASE ,				scheduler=_SCREAMING_SNAKE_CASE	)
      ldm.to(_SCREAMING_SNAKE_CASE	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.dummy_image.to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
      __lowerCamelCase    		=					ldm(image=_SCREAMING_SNAKE_CASE ,				generator=_SCREAMING_SNAKE_CASE ,				num_inference_steps=2 ,				output_type='numpy'	).images

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

      assert image.shape == (1, 64, 64, 3)
      __lowerCamelCase    		=					np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]	)

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







   @unittest.skipIf(torch_device != 'cuda' ,				'This test requires a GPU'	)
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.dummy_uncond_unet
      __lowerCamelCase    		=					DDIMScheduler()
      __lowerCamelCase    		=					self.dummy_vq_model

      # put models in fp16
      __lowerCamelCase    		=					unet.half()
      __lowerCamelCase    		=					vqvae.half()

      __lowerCamelCase    		=					LDMSuperResolutionPipeline(unet=_SCREAMING_SNAKE_CASE ,				vqvae=_SCREAMING_SNAKE_CASE ,				scheduler=_SCREAMING_SNAKE_CASE	)
      ldm.to(_SCREAMING_SNAKE_CASE	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.dummy_image.to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					ldm(_SCREAMING_SNAKE_CASE ,				num_inference_steps=2 ,				output_type='numpy'	).images

      assert image.shape == (1, 64, 64, 3)






@slow
@require_torch
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/vq_diffusion/teddy_bear_pool.png'	)
      __lowerCamelCase    		=					init_image.resize((64, 64) ,				resample=PIL_INTERPOLATION['lanczos']	)

      __lowerCamelCase    		=					LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution' ,				device_map='auto'	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.manual_seed(0	)
      __lowerCamelCase    		=					ldm(image=_SCREAMING_SNAKE_CASE ,				generator=_SCREAMING_SNAKE_CASE ,				num_inference_steps=20 ,				output_type='numpy'	).images

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

      assert image.shape == (1, 256, 256, 3)
      __lowerCamelCase    		=					np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]	)

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














"""simple docstring"""
from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		HfArgumentParser(_lowerCamelCase							)
 try:
  lowerCamelCase__	:     Dict								=		parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  lowerCamelCase__	:     Optional[int]								=		'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  lowerCamelCase__	:     Optional[Any]								=		' '.join(str(_lowerCamelCase							).split(' '							)[:-1]							)
  lowerCamelCase__	:     int								=		''
  lowerCamelCase__	:     Optional[int]								=		eval(str(_lowerCamelCase							).split(' '							)[-1]							)
  lowerCamelCase__	:     Tuple								=		[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]							)
   else:
    wrong_args.append(_lowerCamelCase							)
  if len(_lowerCamelCase							) > 0:
   lowerCamelCase__	:     Optional[int]								=		full_error_msg + begin_error_msg + str(_lowerCamelCase							)
  raise ValueError(_lowerCamelCase							)

 lowerCamelCase__	:     Any								=		PyTorchBenchmark(args=_lowerCamelCase							)
 benchmark.run()


if __name__ == "__main__":
      main()

