




"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
  import PIL

__magic_name__				     =     logging.get_logger(__name__)




def     _A					( __lowercase    ):





       """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        SCREAMING_SNAKE_CASE__   (						UpperCAmelCase_      ):
   snake_case			   =       ["pixel_values"]
   def __init__(       self			:				int   ,	SCREAMING_SNAKE_CASE_			:				bool = True   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int] = None   ,	SCREAMING_SNAKE_CASE_			:				PILImageResampling = PILImageResampling.BILINEAR   ,	SCREAMING_SNAKE_CASE_			:				bool = True   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int] = None   ,	SCREAMING_SNAKE_CASE_			:				bool = True   ,	SCREAMING_SNAKE_CASE_			:				Union[int, float] = 1 / 255   ,	SCREAMING_SNAKE_CASE_			:				bool = True   ,	SCREAMING_SNAKE_CASE_			:				bool = True   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[float, List[float]]] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[float, List[float]]] = None   ,	**SCREAMING_SNAKE_CASE_			:				Tuple   ,	):
          super().__init__(**_snake_case							)
          lowerCamelCase__          =						size if size is not None else {"""shortest_edge""": 256}
          lowerCamelCase__          =						get_size_dict(_snake_case   ,	default_to_square=_snake_case							)
          lowerCamelCase__          =						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          lowerCamelCase__          =						get_size_dict(_snake_case   ,	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__          =						offset
          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    __UpperCAmelCase       (       self			:				Dict   ,	SCREAMING_SNAKE_CASE_			:				np.ndarray   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int]   ,	SCREAMING_SNAKE_CASE_			:				PILImageResampling = PILImageResampling.BILINEAR   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[str, ChannelDimension]] = None   ,	**SCREAMING_SNAKE_CASE_			:				int   ,	):
          lowerCamelCase__          =						get_size_dict(_snake_case   ,	default_to_square=_snake_case							)
          if "shortest_edge" in size:
                 lowerCamelCase__          =						get_resize_output_image_size(_snake_case   ,	size["""shortest_edge"""]   ,	default_to_square=_snake_case							)
          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(_snake_case   ,	size=_snake_case   ,	resample=_snake_case   ,	data_format=_snake_case   ,	**_snake_case							)
   def    __UpperCAmelCase       (       self			:				Any   ,	SCREAMING_SNAKE_CASE_			:				np.ndarray   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int]   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[str, ChannelDimension]] = None   ,	**SCREAMING_SNAKE_CASE_			:				str   ,	):
          lowerCamelCase__          =						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    __UpperCAmelCase       (       self			:				Optional[Any]   ,	SCREAMING_SNAKE_CASE_			:				np.ndarray   ,	SCREAMING_SNAKE_CASE_			:				Union[int, float]   ,	SCREAMING_SNAKE_CASE_			:				bool = True   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[str, ChannelDimension]] = None   ,	**SCREAMING_SNAKE_CASE_			:				str   ,	):
          lowerCamelCase__          =						image.astype(np.floataa							)
          if offset:
                 lowerCamelCase__          =						image - (scale / 2)
          return rescale(_snake_case   ,	scale=_snake_case   ,	data_format=_snake_case   ,	**_snake_case							)
   def    __UpperCAmelCase       (       self			:				Optional[int]   ,	SCREAMING_SNAKE_CASE_			:				np.ndarray   ,	SCREAMING_SNAKE_CASE_			:				Union[float, List[float]]   ,	SCREAMING_SNAKE_CASE_			:				Union[float, List[float]]   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[str, ChannelDimension]] = None   ,	**SCREAMING_SNAKE_CASE_			:				Tuple   ,	):
          return normalize(_snake_case   ,	mean=_snake_case   ,	std=_snake_case   ,	data_format=_snake_case   ,	**_snake_case							)
   def    __UpperCAmelCase       (       self			:				List[Any]   ,	SCREAMING_SNAKE_CASE_			:				ImageInput   ,	SCREAMING_SNAKE_CASE_			:				bool = None   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int] = None   ,	SCREAMING_SNAKE_CASE_			:				PILImageResampling = None   ,	SCREAMING_SNAKE_CASE_			:				bool = None   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int] = None   ,	SCREAMING_SNAKE_CASE_			:				bool = None   ,	SCREAMING_SNAKE_CASE_			:				float = None   ,	SCREAMING_SNAKE_CASE_			:				bool = None   ,	SCREAMING_SNAKE_CASE_			:				bool = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[float, List[float]]] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[float, List[float]]] = None   ,	SCREAMING_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."""							)

          if offset and not do_rescale:
                 raise ValueError("""For offset, do_rescale must also be set to True."""							)

          # All transformations expect numpy arrays.
          lowerCamelCase__          =						to_numpy_array(_snake_case							)

          if do_resize:
                 lowerCamelCase__          =						self.resize(image=_snake_case   ,	size=_snake_case   ,	resample=_snake_case							)

          if do_center_crop:
                 lowerCamelCase__          =						self.center_crop(_snake_case   ,	size=_snake_case							)

          if do_rescale:
                 lowerCamelCase__          =						self.rescale(image=_snake_case   ,	scale=_snake_case   ,	offset=_snake_case							)

          if do_normalize:
                 lowerCamelCase__          =						self.normalize(image=_snake_case   ,	mean=_snake_case   ,	std=_snake_case							)

          lowerCamelCase__          =						to_channel_dimension_format(_snake_case   ,	_snake_case							)
          return image





   def    __UpperCAmelCase       (       self			:				Union[str, Any]   ,	SCREAMING_SNAKE_CASE_			:				ImageInput   ,	SCREAMING_SNAKE_CASE_			:				bool = None   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int] = None   ,	SCREAMING_SNAKE_CASE_			:				PILImageResampling = None   ,	SCREAMING_SNAKE_CASE_			:				bool = None   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int] = None   ,	SCREAMING_SNAKE_CASE_			:				bool = None   ,	SCREAMING_SNAKE_CASE_			:				float = None   ,	SCREAMING_SNAKE_CASE_			:				bool = None   ,	SCREAMING_SNAKE_CASE_			:				bool = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[float, List[float]]] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[float, List[float]]] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[str, TensorType]] = None   ,	SCREAMING_SNAKE_CASE_			:				ChannelDimension = ChannelDimension.FIRST   ,	**SCREAMING_SNAKE_CASE_			:				str   ,	):
          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__          =						offset if offset is not None else self.offset
          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(_snake_case   ,	default_to_square=_snake_case							)
          lowerCamelCase__          =						crop_size if crop_size is not None else self.crop_size
          lowerCamelCase__          =						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."""							)

          lowerCamelCase__          =						make_batched(_snake_case							)

          lowerCamelCase__          =						[
              [
                  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   ,	offset=_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
          ]

          lowerCamelCase__          =						{"""pixel_values""": videos}
          return BatchFeature(data=_snake_case   ,	tensor_type=_snake_case							)











SCREAMING_SNAKE_CASE__   			=   '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

SCREAMING_SNAKE_CASE__   			=   [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE__   			=   {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




