
from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase						:      Union[str, Any]									= logging.get_logger(__name__)




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ['pixel_values']







	def __init__(      self	:Optional[int]  ,     a	:bool = True  ,     a	:int = 3_2  ,     a	:Dict=PILImageResampling.BILINEAR  ,     a	:bool = True  ,     **a	:Union[str, Any]  ,     )	->				None:
								__UpperCamelCase			:     Optional[Any]         =      do_resize
								__UpperCamelCase			:     str         =      do_rescale
								__UpperCamelCase			:     List[str]         =      size_divisor
								__UpperCamelCase			:     str         =      resample
								super().__init__(**a		)







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:np.ndarray  ,     a	:int  ,     a	:str  ,     a	:Optional[ChannelDimension] = None  ,     **a	:List[str]		)	->				np.ndarray:
								__UpperCamelCase     ,		__UpperCamelCase			:     int         =      get_image_size(a		)
								# Rounds the height and width down to the closest multiple of size_divisor
								__UpperCamelCase			:     Dict         =      height // size_divisor * size_divisor
								__UpperCamelCase			:     str         =      width // size_divisor * size_divisor
								__UpperCamelCase			:     str         =      resize(a  ,     (new_h, new_w)  ,     resample=a  ,     data_format=a  ,     **a		)
								return image







	def      _lowerCamelCase       (      self	:str  ,     a	:np.ndarray  ,     a	:float  ,     a	:Optional[ChannelDimension] = None  ,     **a	:Union[str, Any]		)	->				np.ndarray:
								return rescale(image=a  ,     scale=a  ,     data_format=a  ,     **a		)







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]  ,     a	:Optional[bool] = None  ,     a	:Optional[int] = None  ,     a	:Tuple=None  ,     a	:Optional[bool] = None  ,     a	:Optional[Union[TensorType, str]] = None  ,     a	:ChannelDimension = ChannelDimension.FIRST  ,     **a	:Dict  ,     )	->				BatchFeature:
								__UpperCamelCase			:     Union[str, Any]         =      do_resize if do_resize is not None else self.do_resize
								__UpperCamelCase			:     Optional[int]         =      do_rescale if do_rescale is not None else self.do_rescale
								__UpperCamelCase			:     Dict         =      size_divisor if size_divisor is not None else self.size_divisor
								__UpperCamelCase			:     List[str]         =      resample if resample is not None else self.resample

								if do_resize and size_divisor is None:
															raise ValueError("size_divisor is required for resizing"		)

								__UpperCamelCase			:     int         =      make_list_of_images(a		)

								if not valid_images(a		):
															raise ValueError("Invalid image(s)"		)

								# All transformations expect numpy arrays.
								__UpperCamelCase			:     Tuple         =      [to_numpy_array(a		) for img in images]

								if do_resize:
															__UpperCamelCase			:     Optional[int]         =      [self.resize(a  ,     size_divisor=a  ,     resample=a		) for image in images]

								if do_rescale:
															__UpperCamelCase			:     List[str]         =      [self.rescale(a  ,     scale=1 / 2_5_5		) for image in images]

								__UpperCamelCase			:     Optional[Any]         =      [to_channel_dimension_format(a  ,     a		) for image in images]

								__UpperCamelCase			:     Optional[int]         =      {"pixel_values": images}
								return BatchFeature(data=a  ,     tensor_type=a		)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Tuple									= {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Union[str, Any]									= [
		    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Swinv2ForImageClassification',
		    'Swinv2ForMaskedImageModeling',
		    'Swinv2Model',
		    'Swinv2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_swinva import (
				    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwinvaForImageClassification,
				    SwinvaForMaskedImageModeling,
				    SwinvaModel,
				    SwinvaPreTrainedModel,
				)


else:
		import sys

		lowercase						:      Optional[Any]									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)