






"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


__lowerCamelCase							=  logging.get_logger(__name__)



def   a    (     __UpperCAmelCase      :    List[str]  )    ->							List[List[ImageInput]]:
						if isinstance(__UpperCAmelCase      ,   (list, tuple)  ) and isinstance(videos[0]      ,   (list, tuple)  ) and is_valid_image(videos[0][0]  ):
												return videos

						elif isinstance(__UpperCAmelCase      ,   (list, tuple)  ) and is_valid_image(videos[0]  ):
												return [videos]

						elif is_valid_image(__UpperCAmelCase  ):
												return [[videos]]

						raise ValueError(f'Could not make batched video from {videos}'  )



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				["pixel_values"]

					def __init__(			self  :      str     ,		__snake_case  :      bool = True     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      PILImageResampling = PILImageResampling.BILINEAR     ,		__snake_case  :      bool = True     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      bool = True     ,		__snake_case  :      Union[int, float] = 1 / 2_5_5     ,		__snake_case  :      bool = True     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		**__snake_case  :      List[str]     ,		)			->							None:
											super().__init__(**__snake_case					)
											__magic_name__:	Tuple							=       size if size is not None else {"""shortest_edge""": 2_2_4}
											__magic_name__:	str							=       get_size_dict(__snake_case     ,		default_to_square=__snake_case					)
											__magic_name__:	Optional[int]							=       crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
											__magic_name__:	str							=       get_size_dict(__snake_case     ,		param_name="""crop_size"""					)

											__magic_name__:	List[str]							=       do_resize
											__magic_name__:	Optional[int]							=       size
											__magic_name__:	Dict							=       do_center_crop
											__magic_name__:	Any							=       crop_size
											__magic_name__:	List[str]							=       resample
											__magic_name__:	str							=       do_rescale
											__magic_name__:	int							=       rescale_factor
											__magic_name__:	List[str]							=       do_normalize
											__magic_name__:	Any							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__magic_name__:	List[str]							=       image_std if image_std is not None else IMAGENET_STANDARD_STD

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Dict[str, int]     ,		__snake_case  :      PILImageResampling = PILImageResampling.BILINEAR     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      Optional[Any]     ,		)			->							np.ndarray:
											__magic_name__:	Optional[Any]							=       get_size_dict(__snake_case     ,		default_to_square=__snake_case					)
											if "shortest_edge" in size:
																	__magic_name__:	Tuple							=       get_resize_output_image_size(__snake_case     ,		size["""shortest_edge"""]     ,		default_to_square=__snake_case					)
											elif "height" in size and "width" in size:
																	__magic_name__:	Optional[Any]							=       (size["""height"""], size["""width"""])
											else:
																	raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'					)
											return resize(__snake_case     ,		size=__snake_case     ,		resample=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Dict[str, int]     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      Optional[int]     ,		)			->							np.ndarray:
											__magic_name__:	str							=       get_size_dict(__snake_case					)
											if "height" not in size or "width" not in size:
																	raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'					)
											return center_crop(__snake_case     ,		size=(size["""height"""], size["""width"""])     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Union[int, float]     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      Optional[Any]     ,		)			->							Union[str, Any]:
											return rescale(__snake_case     ,		scale=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Union[float, List[float]]     ,		__snake_case  :      Union[float, List[float]]     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      Any     ,		)			->							np.ndarray:
											return normalize(__snake_case     ,		mean=__snake_case     ,		std=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      ImageInput     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      PILImageResampling = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      float = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[ChannelDimension] = ChannelDimension.FIRST     ,		)			->							np.ndarray:
											if do_resize and size is None or resample is None:
																	raise ValueError("""Size and resample must be specified if do_resize is True."""					)

											if do_center_crop and crop_size is None:
																	raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

											if do_rescale and rescale_factor is None:
																	raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

											if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

											# All transformations expect numpy arrays.
											__magic_name__:	int							=       to_numpy_array(__snake_case					)

											if do_resize:
																	__magic_name__:	Union[str, Any]							=       self.resize(image=__snake_case     ,		size=__snake_case     ,		resample=__snake_case					)

											if do_center_crop:
																	__magic_name__:	str							=       self.center_crop(__snake_case     ,		size=__snake_case					)

											if do_rescale:
																	__magic_name__:	str							=       self.rescale(image=__snake_case     ,		scale=__snake_case					)

											if do_normalize:
																	__magic_name__:	List[Any]							=       self.normalize(image=__snake_case     ,		mean=__snake_case     ,		std=__snake_case					)

											__magic_name__:	str							=       to_channel_dimension_format(__snake_case     ,		__snake_case					)
											return image






					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      ImageInput     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      PILImageResampling = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      float = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[str, TensorType]] = None     ,		__snake_case  :      ChannelDimension = ChannelDimension.FIRST     ,		**__snake_case  :      int     ,		)			->							PIL.Image.Image:
											__magic_name__:	List[Any]							=       do_resize if do_resize is not None else self.do_resize
											__magic_name__:	List[str]							=       resample if resample is not None else self.resample
											__magic_name__:	Optional[int]							=       do_center_crop if do_center_crop is not None else self.do_center_crop
											__magic_name__:	Optional[Any]							=       do_rescale if do_rescale is not None else self.do_rescale
											__magic_name__:	Dict							=       rescale_factor if rescale_factor is not None else self.rescale_factor
											__magic_name__:	Optional[Any]							=       do_normalize if do_normalize is not None else self.do_normalize
											__magic_name__:	Dict							=       image_mean if image_mean is not None else self.image_mean
											__magic_name__:	List[str]							=       image_std if image_std is not None else self.image_std

											__magic_name__:	List[str]							=       size if size is not None else self.size
											__magic_name__:	Tuple							=       get_size_dict(__snake_case     ,		default_to_square=__snake_case					)
											__magic_name__:	Optional[Any]							=       crop_size if crop_size is not None else self.crop_size
											__magic_name__:	List[str]							=       get_size_dict(__snake_case     ,		param_name="""crop_size"""					)

											if not valid_images(__snake_case					):
																	raise ValueError(
																	    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																	    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

											__magic_name__:	Tuple							=       make_batched(__snake_case					)

											__magic_name__:	int							=       [
											    [
											        self._preprocess_image(
											            image=__snake_case     ,		do_resize=__snake_case     ,		size=__snake_case     ,		resample=__snake_case     ,		do_center_crop=__snake_case     ,		crop_size=__snake_case     ,		do_rescale=__snake_case     ,		rescale_factor=__snake_case     ,		do_normalize=__snake_case     ,		image_mean=__snake_case     ,		image_std=__snake_case     ,		data_format=__snake_case     ,		)
											        for img in video
											    ]
											    for video in videos
											]

											__magic_name__:	Tuple							=       {"""pixel_values""": videos}
											return BatchFeature(data=__snake_case     ,		tensor_type=__snake_case					)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__			:      str								={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__			:      Optional[Any]								=[
				    'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'CLIPSegModel',
				    'CLIPSegPreTrainedModel',
				    'CLIPSegTextModel',
				    'CLIPSegVisionModel',
				    'CLIPSegForImageSegmentation',
				]

if TYPE_CHECKING:
				from .configuration_clipseg import (
				    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    CLIPSegConfig,
				    CLIPSegTextConfig,
				    CLIPSegVisionConfig,
				)
				from .processing_clipseg import CLIPSegProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_clipseg import (
								    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CLIPSegForImageSegmentation,
								    CLIPSegModel,
								    CLIPSegPreTrainedModel,
								    CLIPSegTextModel,
								    CLIPSegVisionModel,
								)

else:
				import sys

				lowerCAmelCase__			:      str								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





