import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase=256		)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					min(img.size		)

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

      _lowerCamelCase           =					target_image_size / s
      _lowerCamelCase           =					(round(r * img.size[1]		), round(r * img.size[0]		))
      _lowerCamelCase           =					TF.resize(lowercase__			,    lowercase__			,    interpolation=PIL.Image.LANCZOS		)
      _lowerCamelCase           =					TF.center_crop(lowercase__			,    output_size=2 * [target_image_size]		)
      _lowerCamelCase           =					torch.unsqueeze(T.ToTensor()(lowercase__		)			,    0		)
      return img
def    __magic_name__(	__UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					2.0 * x - 1.0
      return x
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase=True			,    __UpperCAmelCase="RGB"		)    ->     Optional[int]:



      '''simple docstring'''

      _lowerCamelCase           =					x.detach().cpu()
      if process:
            _lowerCamelCase           =					post_process_tensor(lowercase__		)
      _lowerCamelCase           =					x.numpy()
      if process:
            _lowerCamelCase           =					(255 * x).astype(np.uinta		)
      _lowerCamelCase           =					Image.fromarray(lowercase__		)
      if not x.mode == mode:
            _lowerCamelCase           =					x.convert(lowercase__		)
      return x
def    __magic_name__(	__UpperCAmelCase		)    ->     List[Any]:



      '''simple docstring'''

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




def    __magic_name__(	__UpperCAmelCase		)    ->     Optional[Any]:



      '''simple docstring'''

      _lowerCamelCase           =					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


__SCREAMING_SNAKE_CASE	:							Optional[int]           =  {'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWDForCTC''',
		    '''SEWDForSequenceClassification''',
		    '''SEWDModel''',
		    '''SEWDPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew_d import (
				    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWDForCTC,
				    SEWDForSequenceClassification,
				    SEWDModel,
				    SEWDPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE	:							Tuple           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

