code: 
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  lowerCAmelCase   :Optional[Any]								=    logging.get_logger(__name__)
  
  lowerCAmelCase   :Tuple								=    {
      """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
  }
  class _lowerCamelCase (		lowerCAmelCase_							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[int]   					=       """mobilevitv2"""
  
  
  
  
  
  
  
  		def __init__(      self	:		str						,  _A	:		Optional[Any]=3						,  _A	:		int=256						,  _A	:		List[Any]=2						,  _A	:		List[str]=2.0						,  _A	:		List[Any]="swish"						,  _A	:		Tuple=3						,  _A	:		Tuple=32						,  _A	:		Optional[int]=0.1						,  _A	:		Optional[Any]=0.02						,  _A	:		Optional[Any]=1E-5						,  _A	:		int=512						,  _A	:		Any=[6, 12, 18]						,  _A	:		int=0.1						,  _A	:		Optional[Any]=255						,  _A	:		List[Any]=[2, 4, 3]						,  _A	:		str=[128, 192, 256]						,  _A	:		Dict=1.0						,  _A	:		List[Any]=2						,  _A	:		Dict=0.0						,  _A	:		Optional[int]=0.0						,  **_A	:		List[Any]						,  )						->				List[str]:
  			super().__init__(**__SCREAMING_SNAKE_CASE    )
  
  			__magic_name__ :					List[Any]  			=  num_channels
  			__magic_name__ :					Tuple  			=  image_size
  			__magic_name__ :					str  			=  patch_size
  			__magic_name__ :					List[Any]  			=  expand_ratio
  			__magic_name__ :					Union[str, Any]  			=  hidden_act
  			__magic_name__ :					Dict  			=  conv_kernel_size
  			__magic_name__ :					Union[str, Any]  			=  output_stride
  			__magic_name__ :					Union[str, Any]  			=  initializer_range
  			__magic_name__ :					Tuple  			=  layer_norm_eps
  			__magic_name__ :					Union[str, Any]  			=  n_attn_blocks
  			__magic_name__ :					Optional[int]  			=  base_attn_unit_dims
  			__magic_name__ :					Optional[int]  			=  width_multiplier
  			__magic_name__ :					Tuple  			=  ffn_multiplier
  			__magic_name__ :					int  			=  ffn_dropout
  			__magic_name__ :					Optional[Any]  			=  attn_dropout
  			__magic_name__ :					List[Any]  			=  classifier_dropout_prob
  
  			# decode head attributes for semantic segmentation
  			__magic_name__ :					List[Any]  			=  aspp_out_channels
  			__magic_name__ :					Union[str, Any]  			=  atrous_rates
  			__magic_name__ :					Tuple  			=  aspp_dropout_prob
  			__magic_name__ :					List[str]  			=  semantic_loss_ignore_index
  
  
  
  
  
  
  
  class _lowerCamelCase (		lowerCAmelCase_							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[int]   					=       version.parse("""1.11"""							)
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		str    )						->				Dict:
  			return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]    )
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		int    )						->				int:
  
  			if self.task == "image-classification":
  				return OrderedDict([('logits', {0: 'batch'})]    )
  			else:
  				return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]    )
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Tuple    )						->				Tuple:
  			return 1E-4
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