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

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

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

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



class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= ['pixel_values']




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







        '''simple docstring'''



        super().__init__(**lowercase__)
        lowerCAmelCase__					     = size if size is not None else {'shortest_edge': 224}
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)
        lowerCAmelCase__					     = crop_size if crop_size is not None else {'height': 224, 'width': 224}
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   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 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Dict[str, int]      ,   lowercase__ :  PILImageResampling = PILImageResampling.BILINEAR      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)
        if "shortest_edge" in size:
               lowerCAmelCase__					     = get_resize_output_image_size(lowercase__      ,   size['shortest_edge']      ,   default_to_square=lowercase__)
        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(lowercase__      ,   size=lowercase__      ,   resample=lowercase__      ,   data_format=lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Dict[str, int]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = get_size_dict(lowercase__)
        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(lowercase__      ,   size=(size['height'], size['width'])      ,   data_format=lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  int      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[int, float]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  List[str]      ,   ):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  str      ,   lowercase__ :  ImageInput      ,   lowercase__ :  bool = None      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  PILImageResampling = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  float = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[ChannelDimension] = 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(lowercase__)

        if do_resize:
               lowerCAmelCase__					     = self.resize(image=lowercase__      ,   size=lowercase__      ,   resample=lowercase__)

        if do_center_crop:
               lowerCAmelCase__					     = self.center_crop(lowercase__      ,   size=lowercase__)

        if do_rescale:
               lowerCAmelCase__					     = self.rescale(image=lowercase__      ,   scale=lowercase__)

        if do_normalize:
               lowerCAmelCase__					     = self.normalize(image=lowercase__      ,   mean=lowercase__      ,   std=lowercase__)

        lowerCAmelCase__					     = to_channel_dimension_format(lowercase__      ,   lowercase__)
        return image




 def 		__snake_case						(      self :  str      ,   lowercase__ :  ImageInput      ,   lowercase__ :  bool = None      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  PILImageResampling = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  float = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  ChannelDimension = ChannelDimension.FIRST      ,   **lowercase__ :  int      ,   ):







        '''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(lowercase__      ,   default_to_square=lowercase__)
        lowerCAmelCase__					     = crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   param_name='crop_size')

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

        lowerCAmelCase__					     = make_batched(lowercase__)

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

        lowerCAmelCase__					     = {'pixel_values': videos}
        return BatchFeature(data=lowercase__      ,   tensor_type=lowercase__)

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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


lowerCAmelCase__     						=			logging.get_logger(__name__)
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= ['pixel_values']




 def __init__(      self :  Tuple      ,   lowercase__ :  bool = True      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  PILImageResampling = PILImageResampling.BICUBIC      ,   lowercase__ :  bool = True      ,   lowercase__ :  Union[int, float] = 1 / 255      ,   lowercase__ :  bool = True      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  bool = True      ,   **lowercase__ :  List[Any]      ,   ):







        '''simple docstring'''



        super().__init__(**lowercase__)
        lowerCAmelCase__					     = size if size is not None else {'height': 384, 'width': 384}
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)

        lowerCAmelCase__					     = do_resize
        lowerCAmelCase__					     = size
        lowerCAmelCase__					     = resample
        lowerCAmelCase__					     = do_rescale
        lowerCAmelCase__					     = rescale_factor
        lowerCAmelCase__					     = do_normalize
        lowerCAmelCase__					     = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowerCAmelCase__					     = image_std if image_std is not None else OPENAI_CLIP_STD
        lowerCAmelCase__					     = do_convert_rgb




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Dict[str, int]      ,   lowercase__ :  PILImageResampling = PILImageResampling.BICUBIC      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)
        if "height" not in size or "width" not in size:
               raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""")
        lowerCAmelCase__					     = (size['height'], size['width'])
        return resize(lowercase__      ,   size=lowercase__      ,   resample=lowercase__      ,   data_format=lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[int, float]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Any      ,   ):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Any      ,   lowercase__ :  ImageInput      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Dict[str, int]] = None      ,   lowercase__ :  PILImageResampling = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[float] = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  ChannelDimension = ChannelDimension.FIRST      ,   **lowercase__ :  Dict      ,   ):







        '''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_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__					     = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowerCAmelCase__					     = size if size is not None else self.size
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)

        lowerCAmelCase__					     = make_list_of_images(lowercase__)

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

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
               lowerCAmelCase__					     = [convert_to_rgb(lowercase__) for image in images]

        # All transformations expect numpy arrays.
        lowerCAmelCase__					     = [to_numpy_array(lowercase__) for image in images]

        if do_resize:
               lowerCAmelCase__					     = [self.resize(image=lowercase__      ,   size=lowercase__      ,   resample=lowercase__) for image in images]

        if do_rescale:
               lowerCAmelCase__					     = [self.rescale(image=lowercase__      ,   scale=lowercase__) for image in images]

        if do_normalize:
               lowerCAmelCase__					     = [self.normalize(image=lowercase__      ,   mean=lowercase__      ,   std=lowercase__) for image in images]

        lowerCAmelCase__					     = [to_channel_dimension_format(lowercase__      ,   lowercase__) for image in images]

        lowerCAmelCase__					     = BatchFeature(data={'pixel_values': images}      ,   tensor_type=lowercase__)

        return encoded_outputs
