



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


def 	_UpperCAmelCase       (   snake_case			,		snake_case=2_56    ):





					"""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(snake_case			,		snake_case			,		interpolation=PIL.Image.LANCZOS    )
					_lowerCAmelCase      			=					TF.center_crop(snake_case			,		output_size=2 * [target_image_size]    )
					_lowerCAmelCase      			=					torch.unsqueeze(T.ToTensor()(snake_case    )			,		0    )
					return img


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					2.0 * x - 1.0
					return x


def 	_UpperCAmelCase       (   snake_case			,		snake_case=True			,		snake_case="RGB"    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					x.detach().cpu()
					if process:
										_lowerCAmelCase      			=					post_process_tensor(snake_case    )
					_lowerCAmelCase      			=					x.numpy()
					if process:
										_lowerCAmelCase      			=					(2_55 * x).astype(np.uinta    )
					_lowerCAmelCase      			=					Image.fromarray(snake_case    )
					if not x.mode == mode:
										_lowerCAmelCase      			=					x.convert(snake_case    )
					return x


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

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


def 	_UpperCAmelCase       (   snake_case    ):





					"""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, is_vision_available


A__			   = {
    """configuration_videomae""": ["""VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VideoMAEConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """VideoMAEForPreTraining""",
	    """VideoMAEModel""",
	    """VideoMAEPreTrainedModel""",
	    """VideoMAEForVideoClassification""",
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = ["""VideoMAEFeatureExtractor"""]
	A__			   = ["""VideoMAEImageProcessor"""]

if TYPE_CHECKING:
	from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_videomae import (
		    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VideoMAEForPreTraining,
		    VideoMAEForVideoClassification,
		    VideoMAEModel,
		    VideoMAEPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_videomae import VideoMAEFeatureExtractor
		from .image_processing_videomae import VideoMAEImageProcessor

else:
	import sys

	A__			   = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



