code: 
  
  
  """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  __snake_case      (unittest.TestCase     ):
  
  
  
  
  							@property
  							def      __a      (    self:						List[str]      ):
  										__lowerCamelCase        =			1
  										__lowerCamelCase        =			3
  										__lowerCamelCase        =			(32, 32)
  
  										__lowerCamelCase        =			floats_tensor((batch_size, num_channels) + sizes    ,       rng=random.Random(0      )      ).to(A_      )
  										return image
  
  
  
  
  							@property
  							def      __a      (    self:						str      ):
  										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:						Any      ):
  										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:						Tuple      ):
  										__lowerCamelCase        =			"""cpu"""
  										__lowerCamelCase        =			self.dummy_uncond_unet
  										__lowerCamelCase        =			DDIMScheduler()
  										__lowerCamelCase        =			self.dummy_vq_model
  
  										__lowerCamelCase        =			LDMSuperResolutionPipeline(unet=A_    ,       vqvae=A_    ,       scheduler=A_      )
  										ldm.to(A_      )
  										ldm.set_progress_bar_config(disable=A_      )
  
  										__lowerCamelCase        =			self.dummy_image.to(A_      )
  
  										__lowerCamelCase        =			torch.Generator(device=A_      ).manual_seed(0      )
  										__lowerCamelCase        =			ldm(image=A_    ,       generator=A_    ,       num_inference_steps=2    ,       output_type="""numpy"""      ).images
  
  										__lowerCamelCase        =			image[0, -3:, -3:, -1]
  
  										assert image.shape == (1, 64, 64, 3)
  										__lowerCamelCase        =			np.array([0.8_678, 0.8_245, 0.6_381, 0.6_830, 0.4_385, 0.5_599, 0.4_641, 0.6_201, 0.5_150]      )
  
  										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
  
  
  
  
  							@unittest.skipIf(torch_device != """cuda"""    ,       """This test requires a GPU"""      )
  							def      __a      (    self:						List[str]      ):
  										__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=A_    ,       vqvae=A_    ,       scheduler=A_      )
  										ldm.to(A_      )
  										ldm.set_progress_bar_config(disable=A_      )
  
  										__lowerCamelCase        =			self.dummy_image.to(A_      )
  
  										__lowerCamelCase        =			ldm(A_    ,       num_inference_steps=2    ,       output_type="""numpy"""      ).images
  
  										assert image.shape == (1, 64, 64, 3)
  
  
  
  
  @slow
  @require_torch
  class  __snake_case      (unittest.TestCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										__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=A_      )
  
  										__lowerCamelCase        =			torch.manual_seed(0      )
  										__lowerCamelCase        =			ldm(image=A_    ,       generator=A_    ,       num_inference_steps=20    ,       output_type="""numpy"""      ).images
  
  										__lowerCamelCase        =			image[0, -3:, -3:, -1]
  
  										assert image.shape == (1, 2_56, 2_56, 3)
  										__lowerCamelCase        =			np.array([0.7_644, 0.7_679, 0.7_642, 0.7_633, 0.7_666, 0.7_560, 0.7_425, 0.7_257, 0.6_907]      )
  
  										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import logging
  import sys
  from unittest.mock import patch
  
  import run_glue_with_pabee
  
  from transformers.testing_utils import TestCasePlus
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  __magic_name__	:   Optional[int]          =							logging.getLogger()
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			argparse.ArgumentParser()
  			parser.add_argument("""-f"""    )
  			__lowerCamelCase        =			parser.parse_args()
  			return args.f
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										__lowerCamelCase        =			logging.StreamHandler(sys.stdout      )
  										logger.addHandler(A_      )
  
  										__lowerCamelCase        =			self.get_auto_remove_tmp_dir()
  										__lowerCamelCase        =			f'\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()
  
  										with patch.object(A_    ,       """argv"""    ,       A_      ):
  													__lowerCamelCase        =			run_glue_with_pabee.main()
  													for value in result.values():
  																self.assertGreaterEqual(A_    ,       0.75      )
  
  
  
  
  