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
  
  
  UpperCamelCase__				       =			logging.get_logger(__name__)
  
  UpperCamelCase__				       =			{
      "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
      "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
      "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
      "apple/deeplabv3-mobilevit-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-x-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-xx-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
      ),
      # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
  }
  
  class 					UpperCAmelCase__   (      UpperCamelCase_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  '''mobilevit'''
  
  
  
  
       def __init__( self       :       Union[str, Any]	,      UpperCamelCase       :       List[Any]=3	,      UpperCamelCase       :       Optional[int]=2_56	,      UpperCamelCase       :       Any=2	,      UpperCamelCase       :       Dict=[1_44, 1_92, 2_40]	,      UpperCamelCase       :       Optional[int]=[16, 32, 64, 96, 1_28, 1_60, 6_40]	,      UpperCamelCase       :       List[Any]=4	,      UpperCamelCase       :       List[str]=2.0	,      UpperCamelCase       :       Dict=4.0	,      UpperCamelCase       :       List[str]="silu"	,      UpperCamelCase       :       str=3	,      UpperCamelCase       :       str=32	,      UpperCamelCase       :       List[str]=0.1	,      UpperCamelCase       :       Any=0.0	,      UpperCamelCase       :       str=0.1	,      UpperCamelCase       :       str=0.02	,      UpperCamelCase       :       Optional[int]=1E-5	,      UpperCamelCase       :       Tuple=True	,      UpperCamelCase       :       Optional[int]=2_56	,      UpperCamelCase       :       Optional[int]=[6, 12, 18]	,      UpperCamelCase       :       List[str]=0.1	,      UpperCamelCase       :       List[str]=2_55	,      **UpperCamelCase       :       Any	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__(**__UpperCamelCase		)
  
              _lowercase					:				List[str]           =			num_channels
              _lowercase					:				Optional[int]           =			image_size
              _lowercase					:				List[Any]           =			patch_size
              _lowercase					:				Optional[int]           =			hidden_sizes
              _lowercase					:				Union[str, Any]           =			neck_hidden_sizes
              _lowercase					:				List[Any]           =			num_attention_heads
              _lowercase					:				Dict           =			mlp_ratio
              _lowercase					:				Any           =			expand_ratio
              _lowercase					:				Optional[Any]           =			hidden_act
              _lowercase					:				List[str]           =			conv_kernel_size
              _lowercase					:				List[str]           =			output_stride
              _lowercase					:				List[Any]           =			hidden_dropout_prob
              _lowercase					:				Union[str, Any]           =			attention_probs_dropout_prob
              _lowercase					:				Optional[Any]           =			classifier_dropout_prob
              _lowercase					:				Tuple           =			initializer_range
              _lowercase					:				Any           =			layer_norm_eps
              _lowercase					:				List[Any]           =			qkv_bias
  
              # decode head attributes for semantic segmentation
              _lowercase					:				Optional[Any]           =			aspp_out_channels
              _lowercase					:				List[str]           =			atrous_rates
              _lowercase					:				List[str]           =			aspp_dropout_prob
              _lowercase					:				Optional[int]           =			semantic_loss_ignore_index
  
  
  
  
  class 					UpperCAmelCase__   (      UpperCamelCase_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  version.parse('''1.11'''		)
  
  
  
  
       @property
       def   lowerCAmelCase_   ( self       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]		)
  
  
  
  
       @property
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              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       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return 1E-4
style_context: 
  
  
  # limitations under the License.
  
  
  from typing import Optional, Tuple, Union
  
  import torch
  
  from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[str]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              self.register_modules(unet=UpperCamelCase	,      scheduler=UpperCamelCase		)
  
  
  
  
       @torch.no_grad()
       def __call__( self       :       Any	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       Optional[torch.Generator] = None	,      UpperCamelCase       :       int = 50	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      **UpperCamelCase       :       int	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			torch.randn(
                  (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,      generator=UpperCamelCase	,      )
              _lowercase					:				Dict           =			image.to(self.device		)
  
              # set step values
              self.scheduler.set_timesteps(UpperCamelCase		)
  
              for t in self.progress_bar(self.scheduler.timesteps		):
                     # 1. predict noise model_output
                     _lowercase					:				Dict           =			self.unet(UpperCamelCase	,      UpperCamelCase		).sample
  
                     # 2. predict previous mean of image x_t-1 and add variance depending on eta
                     # eta corresponds to η in paper and should be between [0, 1]
                     # do x_t -> x_t-1
                     _lowercase					:				Tuple           =			self.scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample
  
              _lowercase					:				Optional[int]           =			(image / 2 + 0.5).clamp(0	,      1		)
              _lowercase					:				int           =			image.cpu().permute(0	,      2	,      3	,      1		).numpy()
              if output_type == "pil":
                     _lowercase					:				List[Any]           =			self.numpy_to_pil(UpperCamelCase		)
  
              if not return_dict:
                     return (image,), "This is a local test"
  
              return ImagePipelineOutput(images=UpperCamelCase		), "This is a local test"