





import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__=256			)  ->    Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    min(img.size			)

			if s < target_image_size:
						raise ValueError(F"""min dim for image {s} < {target_image_size}"""			)

			UpperCAmelCase       		=    target_image_size / s
			UpperCAmelCase       		=    (round(r * img.size[1]			), round(r * img.size[0]			))
			UpperCAmelCase       		=    TF.resize(UpperCamelCase__      ,      UpperCamelCase__      ,      interpolation=PIL.Image.LANCZOS			)
			UpperCAmelCase       		=    TF.center_crop(UpperCamelCase__      ,      output_size=2 * [target_image_size]			)
			UpperCAmelCase       		=    torch.unsqueeze(T.ToTensor()(UpperCamelCase__			)      ,      0			)
			return img





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Tuple:







			'''simple docstring'''

			UpperCAmelCase       		=    2.0 * x - 1.0
			return x





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__=True      ,      UpperCamelCase__="RGB"			)  ->    Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    x.detach().cpu()
			if process:
						UpperCAmelCase       		=    post_process_tensor(UpperCamelCase__			)
			UpperCAmelCase       		=    x.numpy()
			if process:
						UpperCAmelCase       		=    (255 * x).astype(np.uinta			)
			UpperCAmelCase       		=    Image.fromarray(UpperCamelCase__			)
			if not x.mode == mode:
						UpperCAmelCase       		=    x.convert(UpperCamelCase__			)
			return x





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    torch.clamp(UpperCamelCase__      ,      -1.0      ,      1.0			)
			UpperCAmelCase       		=    (x + 1.0) / 2.0
			UpperCAmelCase       		=    x.permute(1      ,      2      ,      0			)
			return x






def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Any:







			'''simple docstring'''

			UpperCAmelCase       		=    post_process_tensor(x.squeeze()			)
			return x.permute(2      ,      0      ,      1			).unsqueeze(0			)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__A    :   Tuple          =       {
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   List[str]          =       [
				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "MobileViTV2ForImageClassification",
				    "MobileViTV2ForSemanticSegmentation",
				    "MobileViTV2Model",
				    "MobileViTV2PreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				__A    :   Tuple          =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



