



'''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


__a          =		logging.get_logger(__name__)





def 						__snake_case(				_lowerCAmelCase		)		-> List[List[ImageInput]]:
      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     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		["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 / 255 ,     snake_case_					:     bool = True ,     snake_case_					:     Optional[Union[float, List[float]]] = None ,     snake_case_					:     Optional[Union[float, List[float]]] = None ,     **snake_case_					:     Union[str, Any] ,     ):
          super().__init__(**snake_case_						)
          snake_case__  :    Dict    						=      size if size is not None else {"""shortest_edge""": 224}
          snake_case__  :    Optional[int]    						=      get_size_dict(snake_case_ ,     default_to_square=snake_case_						)
          snake_case__  :    List[Any]    						=      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          snake_case__  :    Dict    						=      get_size_dict(snake_case_ ,     param_name="""crop_size"""						)

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




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




    def lowerCamelCase       (    self					:     Optional[int] ,     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_					:     List[str] ,     ):
          return normalize(snake_case_ ,     mean=snake_case_ ,     std=snake_case_ ,     data_format=snake_case_ ,     **snake_case_						)




    def lowerCamelCase       (    self					:     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 ,     ):
          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__  :    Union[str, Any]    						=      to_numpy_array(snake_case_						)

          if do_resize:
                snake_case__  :    Any    						=      self.resize(image=snake_case_ ,     size=snake_case_ ,     resample=snake_case_						)

          if do_center_crop:
                snake_case__  :    Dict    						=      self.center_crop(snake_case_ ,     size=snake_case_						)

          if do_rescale:
                snake_case__  :    Optional[Any]    						=      self.rescale(image=snake_case_ ,     scale=snake_case_						)

          if do_normalize:
                snake_case__  :    Any    						=      self.normalize(image=snake_case_ ,     mean=snake_case_ ,     std=snake_case_						)

          snake_case__  :    List[Any]    						=      to_channel_dimension_format(snake_case_ ,     snake_case_						)
          return image




    def lowerCamelCase       (    self					:     List[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_					:     Optional[int] ,     ):
          snake_case__  :    List[str]    						=      do_resize if do_resize is not None else self.do_resize
          snake_case__  :    str    						=      resample if resample is not None else self.resample
          snake_case__  :    Optional[Any]    						=      do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case__  :    Optional[int]    						=      do_rescale if do_rescale is not None else self.do_rescale
          snake_case__  :    List[Any]    						=      rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case__  :    Optional[Any]    						=      do_normalize if do_normalize is not None else self.do_normalize
          snake_case__  :    str    						=      image_mean if image_mean is not None else self.image_mean
          snake_case__  :    Union[str, Any]    						=      image_std if image_std is not None else self.image_std

          snake_case__  :    List[str]    						=      size if size is not None else self.size
          snake_case__  :    str    						=      get_size_dict(snake_case_ ,     default_to_square=snake_case_						)
          snake_case__  :    List[str]    						=      crop_size if crop_size is not None else self.crop_size
          snake_case__  :    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."""						)

          snake_case__  :    Tuple    						=      make_batched(snake_case_						)

          snake_case__  :    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
          ]

          snake_case__  :    Union[str, Any]    						=      {"""pixel_values""": videos}
          return BatchFeature(data=snake_case_ ,     tensor_type=snake_case_						)





'''simple docstring'''





from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		CustomTokenizer
    pass
