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




def     a						(							snake_case__: List[str]						,       snake_case__: Tuple=256 ):


				'''simple docstring'''
				lowercase_					  = min(img.size )

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

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




def     a						(							snake_case__: List[Any] ):


				'''simple docstring'''
				lowercase_					  = 2.0 * x - 1.0
				return x




def     a						(							snake_case__: Union[str, Any]						,       snake_case__: Optional[Any]=True						,       snake_case__: Tuple="RGB" ):


				'''simple docstring'''
				lowercase_					  = x.detach().cpu()
				if process:
								lowercase_					  = post_process_tensor(snake_case__ )
				lowercase_					  = x.numpy()
				if process:
								lowercase_					  = (255 * x).astype(np.uinta )
				lowercase_					  = Image.fromarray(snake_case__ )
				if not x.mode == mode:
								lowercase_					  = x.convert(snake_case__ )
				return x




def     a						(							snake_case__: Tuple ):


				'''simple docstring'''
				lowercase_					  = torch.clamp(snake_case__						,       -1.0						,       1.0 )
				lowercase_					  = (x + 1.0) / 2.0
				lowercase_					  = x.permute(1						,       2						,       0 )
				return x




def     a						(							snake_case__: Dict ):


				'''simple docstring'''
				lowercase_					  = post_process_tensor(x.squeeze() )
				return x.permute(2						,       0						,       1 ).unsqueeze(0 )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a        = {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'NystromformerForMaskedLM',
						    'NystromformerForMultipleChoice',
						    'NystromformerForQuestionAnswering',
						    'NystromformerForSequenceClassification',
						    'NystromformerForTokenClassification',
						    'NystromformerLayer',
						    'NystromformerModel',
						    'NystromformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nystromformer import (
												    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NystromformerForMaskedLM,
												    NystromformerForMultipleChoice,
												    NystromformerForQuestionAnswering,
												    NystromformerForSequenceClassification,
												    NystromformerForTokenClassification,
												    NystromformerLayer,
												    NystromformerModel,
												    NystromformerPreTrainedModel,
												)


else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




