




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     :		List[Any]								=	logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	List[List[ImageInput]]:



       """simple docstring"""

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

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

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

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


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				['''pixel_values''']


    def __init__(      self				:     List[str] ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     PILImageResampling = PILImageResampling.BILINEAR ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Union[int, float] = 1 / 255 ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			**UpperCAmelCase__				:     List[str] ,			)						->None:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           A__							=   size if size is not None else {'''shortest_edge''': 224}
           A__							=   get_size_dict(UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
           A__							=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           A__							=   get_size_dict(UpperCAmelCase__ ,			param_name='''crop_size''')

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     Dict[str, int] ,			UpperCAmelCase__				:     PILImageResampling = PILImageResampling.BILINEAR ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     Union[str, Any] ,			)						->np.ndarray:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     Dict[str, int] ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     Optional[Any] ,			)						->np.ndarray:





           '''simple docstring'''

           A__							=   get_size_dict(UpperCAmelCase__)
           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(UpperCAmelCase__ ,			size=(size['''height'''], size['''width''']) ,			data_format=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     Union[int, float] ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     Optional[Any] ,			)						->List[str]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     Union[float, List[float]] ,			UpperCAmelCase__				:     Union[float, List[float]] ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     int ,			)						->np.ndarray:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     ImageInput ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     PILImageResampling = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     float = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[ChannelDimension] = ChannelDimension.FIRST ,			)						->np.ndarray:





           '''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.
           A__							=   to_numpy_array(UpperCAmelCase__)

           if do_resize:
                  A__							=   self.resize(image=UpperCAmelCase__ ,			size=UpperCAmelCase__ ,			resample=UpperCAmelCase__)

           if do_center_crop:
                  A__							=   self.center_crop(UpperCAmelCase__ ,			size=UpperCAmelCase__)

           if do_rescale:
                  A__							=   self.rescale(image=UpperCAmelCase__ ,			scale=UpperCAmelCase__)

           if do_normalize:
                  A__							=   self.normalize(image=UpperCAmelCase__ ,			mean=UpperCAmelCase__ ,			std=UpperCAmelCase__)

           A__							=   to_channel_dimension_format(UpperCAmelCase__ ,			UpperCAmelCase__)
           return image





    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     ImageInput ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     PILImageResampling = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     float = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[Union[str, TensorType]] = None ,			UpperCAmelCase__				:     ChannelDimension = ChannelDimension.FIRST ,			**UpperCAmelCase__				:     Dict ,			)						->PIL.Image.Image:





           '''simple docstring'''

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

           A__							=   size if size is not None else self.size
           A__							=   get_size_dict(UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
           A__							=   crop_size if crop_size is not None else self.crop_size
           A__							=   get_size_dict(UpperCAmelCase__ ,			param_name='''crop_size''')

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

           A__							=   make_batched(UpperCAmelCase__)

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

           A__							=   {'''pixel_values''': videos}
           return BatchFeature(data=UpperCAmelCase__ ,			tensor_type=UpperCAmelCase__)








_lowerCamelCase     :		dict[str, float]								=       {
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.609_344,
    "knot": 1.852,
}

_lowerCamelCase     :		dict[str, float]								=       {
    "km/h": 1.0,
    "m/s": 0.277_777_778,
    "mph": 0.621_371_192,
    "knot": 0.539_956_803,
}







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	float:



       """simple docstring"""

       if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
              A__							=   (
                  f"""Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"""
                  f"""Valid values are: {", ".join(lowercase_						)}"""
              )
              raise ValueError(lowercase_						)
       return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]			,    3						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


