code: 
  
  
  '''simple docstring'''
  
  import posixpath
  from pathlib import Path
  from unittest.mock import patch
  
  import pytest
  from fsspec.implementations.local import AbstractFileSystem, LocalFileSystem, stringify_path
  from fsspec.registry import _registry as _fsspec_registry
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Union[str, Any]   					=       """mock"""
  
  
  
  
  
  
  
  		def __init__(      self	:		List[Any]						,  *_A	:		Dict						,  _A	:		Optional[Any]						,  **_A	:		Optional[int]    )						->				Optional[Any]:
  			super().__init__()
  			__magic_name__ :					Dict  			=  LocalFileSystem(*_A						,  **_A    )
  			__magic_name__ :					List[Any]  			=  Path(_A    ).resolve().as_posix() + '/'
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple						,  _A	:		str						,  *_A	:		Tuple						,  **_A	:		Any    )						->				Tuple:
  			__magic_name__ :					List[str]  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			return self._fs.mkdir(_A						,  *_A						,  **_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		Union[str, Any]						,  *_A	:		str						,  **_A	:		int    )						->				Optional[int]:
  			__magic_name__ :					Any  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			return self._fs.makedirs(_A						,  *_A						,  **_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any						,  _A	:		int    )						->				int:
  			__magic_name__ :					List[str]  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			return self._fs.rmdir(_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str						,  _A	:		Union[str, Any]						,  _A	:		Any=True						,  *_A	:		Optional[int]						,  **_A	:		Dict    )						->				Union[str, Any]:
  			__magic_name__ :					Any  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			__magic_name__ :					Dict  			=  self._fs.ls(_A						,  detail=_A						,  *_A						,  **_A    )
  			if detail:
  				return [{**info, "name": info["name"][len(self.local_root_dir    ) :]} for info in out]
  			else:
  				return [name[len(self.local_root_dir    ) :] for name in out]
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		Union[str, Any]						,  *_A	:		Optional[int]						,  **_A	:		str    )						->				int:
  			__magic_name__ :					Optional[int]  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			__magic_name__ :					int  			=  dict(self._fs.info(_A						,  *_A						,  **_A    )    )
  			__magic_name__ :					int  			=  out['name'][len(self.local_root_dir    ) :]
  			return out
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]						,  _A	:		Union[str, Any]						,  _A	:		str						,  *_A	:		Union[str, Any]						,  **_A	:		Tuple    )						->				Dict:
  			__magic_name__ :					Any  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			__magic_name__ :					List[Any]  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			return self._fs.cp_file(_A						,  _A						,  *_A						,  **_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any						,  _A	:		Union[str, Any]						,  *_A	:		Optional[int]						,  **_A	:		int    )						->				str:
  			__magic_name__ :					Tuple  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			return self._fs.rm_file(_A						,  *_A						,  **_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  _A	:		List[Any]						,  *_A	:		Any						,  **_A	:		str    )						->				List[Any]:
  			__magic_name__ :					Tuple  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			return self._fs.rm(_A						,  *_A						,  **_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]						,  _A	:		Tuple						,  *_A	:		Optional[Any]						,  **_A	:		Union[str, Any]    )						->				int:
  			__magic_name__ :					Optional[int]  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			return self._fs._open(_A						,  *_A						,  **_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]						,  _A	:		Union[str, Any]    )						->				Union[str, Any]:
  			__magic_name__ :					Any  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			return self._fs.created(_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple						,  _A	:		Dict    )						->				Optional[Any]:
  			__magic_name__ :					Tuple  			=  posixpath.join(self.local_root_dir						,  self._strip_protocol(_A    )    )
  			return self._fs.modified(_A    )
  
  
  
  
  
  
  
  		@classmethod
  		def      __lowerCAmelCase	(      cls	:		Optional[Any]						,  _A	:		Optional[int]    )						->				Tuple:
  			__magic_name__ :					Any  			=  stringify_path(_A    )
  			if path.startswith('mock://'    ):
  				__magic_name__ :					Optional[int]  			=  path[7:]
  			return path
  
  
  
  
  
  
  
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Tuple   					=       """tmp"""
  		A_			:    Dict   					=       None
  
  
  
  
  
  
  
  		def __init__(      self	:		int						,  *_A	:		int						,  **_A	:		str    )						->				Any:
  			assert self.tmp_dir is not None, "TmpDirFileSystem.tmp_dir is not set"
  			super().__init__(*_A						,  **_A						,  local_root_dir=self.tmp_dir						,  auto_mkdir=_A    )
  
  
  
  
  
  
  
  		@classmethod
  		def      __lowerCAmelCase	(      cls	:		Optional[Any]						,  _A	:		str    )						->				Any:
  			__magic_name__ :					List[str]  			=  stringify_path(_A    )
  			if path.startswith('tmp://'    ):
  				__magic_name__ :					List[str]  			=  path[6:]
  			return path
  
  
  
  
  
  
  
  @pytest.fixture
  def lowerCamelCase    (    ):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Dict  			=  MockFileSystem
  	__magic_name__ :					Tuple  			=  TmpDirFileSystem
  	yield
  	del _fsspec_registry["mock"]
  	del _fsspec_registry["tmp"]
  
  
  
  
  
  @pytest.fixture
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[int]	,	lowerCAmelCase       :			Tuple	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Optional[int]  			=  tmp_path_factory.mktemp('mockfs'	)
  	return MockFileSystem(local_root_dir=lowerCAmelCase	,	auto_mkdir=lowerCAmelCase	)
  
  
  
  
  
  @pytest.fixture
  def lowerCamelCase    (    lowerCAmelCase       :			Dict	,	lowerCAmelCase       :			Dict	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					int  			=  tmp_path_factory.mktemp('tmpfs'	)
  	with patch.object(lowerCAmelCase	,	'tmp_dir'	,	lowerCAmelCase	):
  		yield TmpDirFileSystem()
style_context: 
  
  
  '''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 _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				List[Any]:
  			__magic_name__ :					Union[str, Any]  			=  1
  			__magic_name__ :					str  			=  3
  			__magic_name__ :					Optional[Any]  			=  (32, 32)
  
  			__magic_name__ :					Dict  			=  floats_tensor((batch_size, num_channels) + sizes						,  rng=random.Random(0    )    ).to(_A    )
  			return image
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[str]:
  			torch.manual_seed(0    )
  			__magic_name__ :					Dict  			=  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      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
  			torch.manual_seed(0    )
  			__magic_name__ :					Union[str, Any]  			=  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      __lowerCAmelCase	(      self	:		Optional[int]    )						->				int:
  			__magic_name__ :					Dict  			=  'cpu'
  			__magic_name__ :					str  			=  self.dummy_uncond_unet
  			__magic_name__ :					Optional[int]  			=  DDIMScheduler()
  			__magic_name__ :					Optional[Any]  			=  self.dummy_vq_model
  
  			__magic_name__ :					Tuple  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
  			ldm.to(_A    )
  			ldm.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					Optional[int]  			=  self.dummy_image.to(_A    )
  
  			__magic_name__ :					List[Any]  			=  torch.Generator(device=_A    ).manual_seed(0    )
  			__magic_name__ :					str  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=2						,  output_type='numpy'    ).images
  
  			__magic_name__ :					List[Any]  			=  image[0, -3:, -3:, -1]
  
  			assert image.shape == (1, 64, 64, 3)
  			__magic_name__ :					Optional[Any]  			=  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      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[Any]:
  			__magic_name__ :					Union[str, Any]  			=  self.dummy_uncond_unet
  			__magic_name__ :					Tuple  			=  DDIMScheduler()
  			__magic_name__ :					Union[str, Any]  			=  self.dummy_vq_model
  
  			# put models in fp16
  			__magic_name__ :					Optional[int]  			=  unet.half()
  			__magic_name__ :					int  			=  vqvae.half()
  
  			__magic_name__ :					Optional[Any]  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
  			ldm.to(_A    )
  			ldm.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					str  			=  self.dummy_image.to(_A    )
  
  			__magic_name__ :					Union[str, Any]  			=  ldm(_A						,  num_inference_steps=2						,  output_type='numpy'    ).images
  
  			assert image.shape == (1, 64, 64, 3)
  
  
  
  
  
  
  
  @slow
  @require_torch
  class _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				Dict:
  			__magic_name__ :					Tuple  			=  load_image(
  			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
  			    '/vq_diffusion/teddy_bear_pool.png'    )
  			__magic_name__ :					Dict  			=  init_image.resize((64, 64)						,  resample=PIL_INTERPOLATION['lanczos']    )
  
  			__magic_name__ :					List[Any]  			=  LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'						,  device_map='auto'    )
  			ldm.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					int  			=  torch.manual_seed(0    )
  			__magic_name__ :					Optional[Any]  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=20						,  output_type='numpy'    ).images
  
  			__magic_name__ :					List[str]  			=  image[0, -3:, -3:, -1]
  
  			assert image.shape == (1, 256, 256, 3)
  			__magic_name__ :					int  			=  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