code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import warnings
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from .configuration_utils import ConfigMixin, register_to_config
  from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						CONFIG_NAME
  
  
   @register_to_config
   def __init__(	self				,					__A = True				,					__A = 8				,					__A = "lanczos"				,					__A = True				,					__A = False				,					)      ->  Union[str, Any]:
       super().__init__()
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A		)      ->  PIL.Image.Image:
       if images.ndim == 3:
           a					=images[None, ...]
       a					=(images * 255).round().astype('''uint8'''		)
       if images.shape[-1] == 1:
           # special case for grayscale (single channel) images
           a					=[Image.fromarray(image.squeeze()				,					mode='''L'''		) for image in images]
       else:
           a					=[Image.fromarray(__A		) for image in images]
  
       return pil_images
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A		)      ->  np.ndarray:
       if not isinstance(__A				,					__A		):
           a					=[images]
       a					=[np.array(__A		).astype(np.floataa		) / 255.0 for image in images]
       a					=np.stack(__A				,					axis=0		)
  
       return images
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A		)      ->  torch.FloatTensor:
       if images.ndim == 3:
           a					=images[..., None]
  
       a					=torch.from_numpy(images.transpose(0				,					3				,					1				,					2		)		)
       return images
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A		)      ->  np.ndarray:
       a					=images.cpu().permute(0				,					2				,					3				,					1		).float().numpy()
       return images
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A		)      ->  str:
       return 2.0 * images - 1.0
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A		)      ->  Optional[Any]:
       return (images / 2 + 0.5).clamp(0				,					1		)
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A		)      ->  PIL.Image.Image:
       a					=image.convert('''RGB'''		)
       return image
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A = None				,					__A = None				,					)      ->  PIL.Image.Image:
       if height is None:
           a					=image.height
       if width is None:
           a					=image.width
  
       a	,   a					=(
           x - x % self.config.vae_scale_factor for x in (width, height)
       )  # resize to integer multiple of vae_scale_factor
       a					=image.resize((width, height)				,					resample=PIL_INTERPOLATION[self.config.resample]		)
       return image
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A = None				,					__A = None				,					)      ->  torch.Tensor:
       a					=(PIL.Image.Image, np.ndarray, torch.Tensor)
       if isinstance(__A				,					__A		):
           a					=[image]
       elif not (isinstance(__A				,					__A		) and all(isinstance(__A				,					__A		) for i in image		)):
           raise ValueError(
               f'''Input is in incorrect format: {[type(__A		) for i in image]}. Currently, we only support {", ".join(__A		)}'''		)
  
       if isinstance(image[0]				,					PIL.Image.Image		):
           if self.config.do_convert_rgb:
               a					=[self.convert_to_rgb(__A		) for i in image]
           if self.config.do_resize:
               a					=[self.resize(__A				,					__A				,					__A		) for i in image]
           a					=self.pil_to_numpy(__A		)  # to np
           a					=self.numpy_to_pt(__A		)  # to pt
  
       elif isinstance(image[0]				,					np.ndarray		):
           a					=np.concatenate(__A				,					axis=0		) if image[0].ndim == 4 else np.stack(__A				,					axis=0		)
           a					=self.numpy_to_pt(__A		)
           a	,   a	,   a	,   a					=image.shape
           if self.config.do_resize and (
               height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
           ):
               raise ValueError(
                   f'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
                   f'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''		)
  
       elif isinstance(image[0]				,					torch.Tensor		):
           a					=torch.cat(__A				,					axis=0		) if image[0].ndim == 4 else torch.stack(__A				,					axis=0		)
           a	,   a	,   a	,   a					=image.shape
  
           # don't need any preprocess if the image is latents
           if channel == 4:
               return image
  
           if self.config.do_resize and (
               height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
           ):
               raise ValueError(
                   f'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
                   f'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''		)
  
          # expected range [0,1], normalize to [-1,1]
       a					=self.config.do_normalize
       if image.min() < 0:
           warnings.warn(
               '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
               f'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''				,					__A				,					)
           a					=False
  
       if do_normalize:
           a					=self.normalize(__A		)
  
       return image
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A = "pil"				,					__A = None				,					)      ->  Any:
       if not isinstance(__A				,					torch.Tensor		):
           raise ValueError(
               f'''Input for postprocessing is in incorrect format: {type(__A		)}. We only support pytorch tensor'''		)
       if output_type not in ["latent", "pt", "np", "pil"]:
           a					=(
               f'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
               '''`pil`, `np`, `pt`, `latent`'''
           )
           deprecate('''Unsupported output_type'''				,					'''1.0.0'''				,					__A				,					standard_warn=__A		)
           a					='''np'''
  
       if output_type == "latent":
           return image
  
       if do_denormalize is None:
           a					=[self.config.do_normalize] * image.shape[0]
  
       a					=torch.stack(
           [self.denormalize(image[i]		) if do_denormalize[i] else image[i] for i in range(image.shape[0]		)]		)
  
       if output_type == "pt":
           return image
  
       a					=self.pt_to_numpy(__A		)
  
       if output_type == "np":
           return image
  
       if output_type == "pil":
           return self.numpy_to_pil(__A		)
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						CONFIG_NAME
  
  
   @register_to_config
   def __init__(	self				,					__A = True				,					__A = 8				,					__A = "lanczos"				,					__A = True				,					)      ->  Optional[Any]:
       super().__init__()
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A		)      ->  Optional[int]:
       if images.ndim == 3:
           a					=images[None, ...]
       a					=(images * 255).round().astype('''uint8'''		)
       if images.shape[-1] == 1:
           # special case for grayscale (single channel) images
           a					=[Image.fromarray(image.squeeze()				,					mode='''L'''		) for image in images]
       else:
           a					=[Image.fromarray(image[:, :, :3]		) for image in images]
  
       return pil_images
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A		)      ->  Union[str, Any]:
       return image[:, :, 1] * 2**8 + image[:, :, 2]
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Any:
       if images.ndim == 3:
           a					=images[None, ...]
       a					=images[:, :, :, 3:]
       if images.shape[-1] == 6:
           a					=(images_depth * 255).round().astype('''uint8'''		)
           a					=[
               Image.fromarray(self.rgblike_to_depthmap(__A		)				,					mode='''I;16'''		) for image_depth in images_depth
           ]
       elif images.shape[-1] == 4:
           a					=(images_depth * 65_535.0).astype(np.uintaa		)
           a					=[Image.fromarray(__A				,					mode='''I;16'''		) for image_depth in images_depth]
       else:
           raise Exception('''Not supported'''		)
  
       return pil_images
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A = "pil"				,					__A = None				,					)      ->  Any:
       if not isinstance(__A				,					torch.Tensor		):
           raise ValueError(
               f'''Input for postprocessing is in incorrect format: {type(__A		)}. We only support pytorch tensor'''		)
       if output_type not in ["latent", "pt", "np", "pil"]:
           a					=(
               f'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
               '''`pil`, `np`, `pt`, `latent`'''
           )
           deprecate('''Unsupported output_type'''				,					'''1.0.0'''				,					__A				,					standard_warn=__A		)
           a					='''np'''
  
       if do_denormalize is None:
           a					=[self.config.do_normalize] * image.shape[0]
  
       a					=torch.stack(
           [self.denormalize(image[i]		) if do_denormalize[i] else image[i] for i in range(image.shape[0]		)]		)
  
       a					=self.pt_to_numpy(__A		)
  
       if output_type == "np":
           if image.shape[-1] == 6:
               a					=np.stack([self.rgblike_to_depthmap(im[:, :, 3:]		) for im in image]				,					axis=0		)
           else:
               a					=image[:, :, :, 3:]
           return image[:, :, :, :3], image_depth
  
       if output_type == "pil":
           return self.numpy_to_pil(__A		), self.numpy_to_depth(__A		)
       else:
           raise Exception(f'''This type {output_type} is not supported'''		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  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_     :     Any    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Tuple    		=						{
      """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"mobilevitv2"
  
  
   def __init__(	self				,					__A=3				,					__A=256				,					__A=2				,					__A=2.0				,					__A="swish"				,					__A=3				,					__A=32				,					__A=0.1				,					__A=0.02				,					__A=1E-5				,					__A=512				,					__A=[6, 12, 18]				,					__A=0.1				,					__A=255				,					__A=[2, 4, 3]				,					__A=[128, 192, 256]				,					__A=1.0				,					__A=2				,					__A=0.0				,					__A=0.0				,					**__A				,					)      ->  int:
       super().__init__(**__A		)
  
       a					=num_channels
       a					=image_size
       a					=patch_size
       a					=expand_ratio
       a					=hidden_act
       a					=conv_kernel_size
       a					=output_stride
       a					=initializer_range
       a					=layer_norm_eps
       a					=n_attn_blocks
       a					=base_attn_unit_dims
       a					=width_multiplier
       a					=ffn_multiplier
       a					=ffn_dropout
       a					=attn_dropout
       a					=classifier_dropout_prob
  
       # decode head attributes for semantic segmentation
       a					=aspp_out_channels
       a					=atrous_rates
       a					=aspp_dropout_prob
       a					=semantic_loss_ignore_index
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						version.parse("1.11"    )
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]		)
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]		)
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]		)
  
  
  
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  float:
       return 1E-4