
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   =			logging.get_logger(__name__)



def 						lowercase_      (       A__		)							->							List[List[ImageInput]]:
    """simple docstring"""

    if isinstance(A__			,		(list, tuple)		) and isinstance(videos[0]			,		(list, tuple)		) and is_valid_image(videos[0][0]		):
        return videos

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

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

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







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							int									=		["pixel_values"]





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

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





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   np.ndarray    ,   _A				:   Dict[str, int]    ,   _A				:   PILImageResampling = PILImageResampling.BILINEAR    ,   _A				:   Optional[Union[str, ChannelDimension]] = None    ,   **_A				:   str    ,   )						->						np.ndarray:
         snake_case								= get_size_dict(_A    ,   default_to_square=_A       )
         if "shortest_edge" in size:
             snake_case								= get_resize_output_image_size(_A    ,   size["shortest_edge"]    ,   default_to_square=_A       )
         elif "height" in size and "width" in size:
             snake_case								= (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 		UpperCAmelCase(self				:   Tuple    ,   _A				:   np.ndarray    ,   _A				:   Dict[str, int]    ,   _A				:   Optional[Union[str, ChannelDimension]] = None    ,   **_A				:   int    ,   )						->						np.ndarray:
         snake_case								= 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 		UpperCAmelCase(self				:   int    ,   _A				:   np.ndarray    ,   _A				:   Union[int, float]    ,   _A				:   Optional[Union[str, ChannelDimension]] = None    ,   **_A				:   Dict    ,   )						->						List[Any]:
         return rescale(_A    ,   scale=_A    ,   data_format=_A    ,   **_A       )





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





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   ImageInput    ,   _A				:   bool = None    ,   _A				:   Dict[str, int] = None    ,   _A				:   PILImageResampling = None    ,   _A				:   bool = None    ,   _A				:   Dict[str, int] = None    ,   _A				:   bool = None    ,   _A				:   float = None    ,   _A				:   bool = None    ,   _A				:   Optional[Union[float, List[float]]] = None    ,   _A				:   Optional[Union[float, List[float]]] = None    ,   _A				:   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.
         snake_case								= to_numpy_array(_A       )

         if do_resize:
             snake_case								= self.resize(image=_A    ,   size=_A    ,   resample=_A       )

         if do_center_crop:
             snake_case								= self.center_crop(_A    ,   size=_A       )

         if do_rescale:
             snake_case								= self.rescale(image=_A    ,   scale=_A       )

         if do_normalize:
             snake_case								= self.normalize(image=_A    ,   mean=_A    ,   std=_A       )

         snake_case								= to_channel_dimension_format(_A    ,   _A       )
         return image





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

         snake_case								= size if size is not None else self.size
         snake_case								= get_size_dict(_A    ,   default_to_square=_A       )
         snake_case								= crop_size if crop_size is not None else self.crop_size
         snake_case								= 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."       )

         snake_case								= make_batched(_A       )

         snake_case								= [
             [
                 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
         ]

         snake_case								= {"pixel_values": videos}
         return BatchFeature(data=_A    ,   tensor_type=_A       )



from ..utils import DummyObject, requires_backends







class 					lowerCamelCase		(  metaclass=A_  ):
     UpperCAmelCase__    :							Union[str, Any]									=		["onnx"]





     def __init__(self				:   Tuple    ,   *_A				:   Optional[int]    ,   **_A				:   Any       )						->						Dict:
         requires_backends(self    ,   ["onnx"]       )





     @classmethod
     def 		UpperCAmelCase(cls				:   int    ,   *_A				:   Dict    ,   **_A				:   List[Any]       )						->						Optional[Any]:
         requires_backends(cls    ,   ["onnx"]       )





     @classmethod
     def 		UpperCAmelCase(cls				:   Dict    ,   *_A				:   Tuple    ,   **_A				:   Optional[Any]       )						->						int:
         requires_backends(cls    ,   ["onnx"]       )

