



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


a_						:     Optional[Any]	     =		logging.get_logger(__name__)

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      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    lowerCamelCase__  (						A_):
     """simple docstring"""



     _A       =			['''pixel_values''']







     def __init__(self				,			__a = True				,			__a = None				,			__a = PILImageResampling.BILINEAR				,			__a = True				,			__a = None				,			__a = True				,			__a = 1 / 2_55				,			__a = True				,			__a = None				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           super().__init__(**__a    )
           lowerCamelCase								=		size if size is not None else {"""shortest_edge""": 2_24}
           lowerCamelCase								=		get_size_dict(__a				,			default_to_square=__a    )
           lowerCamelCase								=		crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
           lowerCamelCase								=		get_size_dict(__a				,			param_name="crop_size"    )

           lowerCamelCase								=		do_resize
           lowerCamelCase								=		size
           lowerCamelCase								=		do_center_crop
           lowerCamelCase								=		crop_size
           lowerCamelCase								=		resample
           lowerCamelCase								=		do_rescale
           lowerCamelCase								=		rescale_factor
           lowerCamelCase								=		do_normalize
           lowerCamelCase								=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowerCamelCase								=		image_std if image_std is not None else IMAGENET_STANDARD_STD







     def 						_a      (self				,			__a				,			__a				,			__a = PILImageResampling.BILINEAR				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		get_size_dict(__a				,			default_to_square=__a    )
           if "shortest_edge" in size:
                 lowerCamelCase								=		get_resize_output_image_size(__a				,			size["shortest_edge"]				,			default_to_square=__a    )
           elif "height" in size and "width" in size:
                 lowerCamelCase								=		(size["""height"""], size["""width"""])
           else:
                 raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""    )
           return resize(__a				,			size=__a				,			resample=__a				,			data_format=__a				,			**__a    )







     def 						_a      (self				,			__a				,			__a				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		get_size_dict(__a    )
           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(__a				,			size=(size["height"], size["width"])				,			data_format=__a				,			**__a    )







     def 						_a      (self				,			__a				,			__a				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           return rescale(__a				,			scale=__a				,			data_format=__a				,			**__a    )







     def 						_a      (self				,			__a				,			__a				,			__a				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           return normalize(__a				,			mean=__a				,			std=__a				,			data_format=__a				,			**__a    )







     def 						_a      (self				,			__a				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = ChannelDimension.FIRST				,			):






           '''simple docstring'''



           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.
           lowerCamelCase								=		to_numpy_array(__a    )

           if do_resize:
                 lowerCamelCase								=		self.resize(image=__a				,			size=__a				,			resample=__a    )

           if do_center_crop:
                 lowerCamelCase								=		self.center_crop(__a				,			size=__a    )

           if do_rescale:
                 lowerCamelCase								=		self.rescale(image=__a				,			scale=__a    )

           if do_normalize:
                 lowerCamelCase								=		self.normalize(image=__a				,			mean=__a				,			std=__a    )

           lowerCamelCase								=		to_channel_dimension_format(__a				,			__a    )
           return image







     def 						_a      (self				,			__a				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = ChannelDimension.FIRST				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		do_resize if do_resize is not None else self.do_resize
           lowerCamelCase								=		resample if resample is not None else self.resample
           lowerCamelCase								=		do_center_crop if do_center_crop is not None else self.do_center_crop
           lowerCamelCase								=		do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase								=		rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase								=		do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase								=		image_mean if image_mean is not None else self.image_mean
           lowerCamelCase								=		image_std if image_std is not None else self.image_std

           lowerCamelCase								=		size if size is not None else self.size
           lowerCamelCase								=		get_size_dict(__a				,			default_to_square=__a    )
           lowerCamelCase								=		crop_size if crop_size is not None else self.crop_size
           lowerCamelCase								=		get_size_dict(__a				,			param_name="crop_size"    )

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

           lowerCamelCase								=		make_batched(__a    )

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

           lowerCamelCase								=		{"""pixel_values""": videos}
           return BatchFeature(data=__a				,			tensor_type=__a    )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_						:     List[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:
     a_						:     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

     a_						:     Dict	     =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)