





"""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

lowerCamelCase__				  =   logging.get_logger(__name__)




def 					lowercase__    (					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      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):
  SCREAMING_SNAKE_CASE__							:str	     =					["pixel_values"]

  def __init__(		self   :       List[str]					,   __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 / 255					,   __a   :       bool = True					,   __a   :       bool = True					,   __a   :       Optional[Union[float, List[float]]] = None					,   __a   :       Optional[Union[float, List[float]]] = None					,   **__a   :       Tuple					,   )		->				None:
        super().__init__(**__a	)
        _UpperCamelCase      :	List[Any]						=       size if size is not None else {"shortest_edge": 256}
        _UpperCamelCase      :	Any						=       get_size_dict(__a					,   default_to_square=__a	)
        _UpperCamelCase      :	List[Any]						=       crop_size if crop_size is not None else {"height": 224, "width": 224}
        _UpperCamelCase      :	List[str]						=       get_size_dict(__a					,   param_name="crop_size"	)

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       np.ndarray					,   __a   :       Dict[str, int]					,   __a   :       PILImageResampling = PILImageResampling.BILINEAR					,   __a   :       Optional[Union[str, ChannelDimension]] = None					,   **__a   :       Any					,   )		->				np.ndarray:
        _UpperCamelCase      :	Tuple						=       get_size_dict(__a					,   default_to_square=__a	)
        if "shortest_edge" in size:
              _UpperCamelCase      :	Any						=       get_resize_output_image_size(__a					,   size["shortest_edge"]					,   default_to_square=__a	)
        elif "height" in size and "width" in size:
              _UpperCamelCase      :	Union[str, Any]						=       (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    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       np.ndarray					,   __a   :       Dict[str, int]					,   __a   :       Optional[Union[str, ChannelDimension]] = None					,   **__a   :       List[str]					,   )		->				np.ndarray:
        _UpperCamelCase      :	List[Any]						=       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    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       np.ndarray					,   __a   :       Union[int, float]					,   __a   :       bool = True					,   __a   :       Optional[Union[str, ChannelDimension]] = None					,   **__a   :       int					,   )		->				Optional[Any]:
        _UpperCamelCase      :	Any						=       image.astype(np.floataa	)
        if offset:
              _UpperCamelCase      :	Any						=       image - (scale / 2)
        return rescale(__a					,   scale=__a					,   data_format=__a					,   **__a	)

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __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   :       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."	)

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

        # All transformations expect numpy arrays.
        _UpperCamelCase      :	List[str]						=       to_numpy_array(__a	)

        if do_resize:
              _UpperCamelCase      :	Tuple						=       self.resize(image=__a					,   size=__a					,   resample=__a	)

        if do_center_crop:
              _UpperCamelCase      :	Tuple						=       self.center_crop(__a					,   size=__a	)

        if do_rescale:
              _UpperCamelCase      :	Dict						=       self.rescale(image=__a					,   scale=__a					,   offset=__a	)

        if do_normalize:
              _UpperCamelCase      :	Optional[Any]						=       self.normalize(image=__a					,   mean=__a					,   std=__a	)

        _UpperCamelCase      :	Tuple						=       to_channel_dimension_format(__a					,   __a	)
        return image


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[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   :       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   :       Optional[Any]					,   )		->				PIL.Image.Image:
        _UpperCamelCase      :	Optional[int]						=       do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase      :	Optional[int]						=       resample if resample is not None else self.resample
        _UpperCamelCase      :	int						=       do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCamelCase      :	List[str]						=       do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase      :	List[str]						=       rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase      :	Dict						=       offset if offset is not None else self.offset
        _UpperCamelCase      :	Optional[Any]						=       do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase      :	int						=       image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase      :	Union[str, Any]						=       image_std if image_std is not None else self.image_std

        _UpperCamelCase      :	int						=       size if size is not None else self.size
        _UpperCamelCase      :	int						=       get_size_dict(__a					,   default_to_square=__a	)
        _UpperCamelCase      :	Any						=       crop_size if crop_size is not None else self.crop_size
        _UpperCamelCase      :	List[Any]						=       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."	)

        _UpperCamelCase      :	List[str]						=       make_batched(__a	)

        _UpperCamelCase      :	int						=       [
            [
                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					,   offset=__a					,   do_normalize=__a					,   image_mean=__a					,   image_std=__a					,   data_format=__a					,   )
                for img in video
            ]
            for video in videos
        ]

        _UpperCamelCase      :	Any						=       {"pixel_values": videos}
        return BatchFeature(data=__a					,   tensor_type=__a	)







"""simple docstring"""


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCamelCase   )
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:str	     =					field(default="summarization"		,  metadata={"include_in_asdict_even_if_is_default": True}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"text": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"summary": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:str	     =					"text"
  SCREAMING_SNAKE_CASE__							:str	     =					"summary"

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Dict[str, str]:
        return {self.text_column: "text", self.summary_column: "summary"}
