
"""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


lowercase_         =       logging.get_logger(__name__)




def       A_      (       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_						(lowerCamelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  Union[str, Any]					 =     ["""pixel_values"""]




 def __init__(			self							:			List[Any]  , 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 / 2_55  , a_							:			bool = True  , a_							:			Optional[Union[float, List[float]]] = None  , a_							:			Optional[Union[float, List[float]]] = None  , **a_							:			Dict  , )->       None:



      """simple docstring"""

      super().__init__(**a_			)
      UpperCAmelCase_    :     Any            =	size if size is not None else {"""shortest_edge""": 2_24}
      UpperCAmelCase_    :     str            =	get_size_dict(a_  , default_to_square=a_			)
      UpperCAmelCase_    :     List[Any]            =	crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
      UpperCAmelCase_    :     str            =	get_size_dict(a_  , param_name="""crop_size"""			)

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




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



      """simple docstring"""

      UpperCAmelCase_    :     Optional[Any]            =	get_size_dict(a_  , default_to_square=a_			)
      if "shortest_edge" in size:
           UpperCAmelCase_    :     Optional[Any]            =	get_resize_output_image_size(a_  , size["""shortest_edge"""]  , default_to_square=a_			)
      elif "height" in size and "width" in size:
           UpperCAmelCase_    :     Optional[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 	a   (			self							:			Any  , a_							:			np.ndarray  , a_							:			Dict[str, int]  , a_							:			Optional[Union[str, ChannelDimension]] = None  , **a_							:			Optional[int]  , )->       np.ndarray:



      """simple docstring"""

      UpperCAmelCase_    :     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 	a   (			self							:			Any  , a_							:			np.ndarray  , a_							:			Union[int, float]  , a_							:			Optional[Union[str, ChannelDimension]] = None  , **a_							:			Dict  , )->       Optional[Any]:



      """simple docstring"""

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




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



      """simple docstring"""

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




 def 	a   (			self							:			str  , 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_							:			Optional[Union[float, List[float]]] = None  , a_							:			Optional[Union[float, List[float]]] = None  , a_							:			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.
      UpperCAmelCase_    :     Dict            =	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_    :     str            =	self.rescale(image=a_  , scale=a_			)

      if do_normalize:
           UpperCAmelCase_    :     List[str]            =	self.normalize(image=a_  , mean=a_  , std=a_			)

      UpperCAmelCase_    :     Tuple            =	to_channel_dimension_format(a_  , a_			)
      return image






 def 	a   (			self							:			List[str]  , 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_							:			Optional[Union[float, List[float]]] = None  , a_							:			Optional[Union[float, List[float]]] = None  , a_							:			Optional[Union[str, TensorType]] = None  , a_							:			ChannelDimension = ChannelDimension.FIRST  , **a_							:			List[str]  , )->       PIL.Image.Image:



      """simple docstring"""

      UpperCAmelCase_    :     Tuple            =	do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase_    :     List[str]            =	resample if resample is not None else self.resample
      UpperCAmelCase_    :     List[Any]            =	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_    :     Optional[Any]            =	rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCAmelCase_    :     Optional[int]            =	do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase_    :     str            =	image_mean if image_mean is not None else self.image_mean
      UpperCAmelCase_    :     Dict            =	image_std if image_std is not None else self.image_std

      UpperCAmelCase_    :     str            =	size if size is not None else self.size
      UpperCAmelCase_    :     Optional[int]            =	get_size_dict(a_  , default_to_square=a_			)
      UpperCAmelCase_    :     Optional[Any]            =	crop_size if crop_size is not None else self.crop_size
      UpperCAmelCase_    :     Dict            =	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_    :     int            =	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_  , do_normalize=a_  , image_mean=a_  , image_std=a_  , data_format=a_  , )
              for img in video
          ]
          for video in videos
      ]

      UpperCAmelCase_    :     Tuple            =	{"""pixel_values""": videos}
      return BatchFeature(data=a_  , tensor_type=a_			)



"""simple docstring"""







import argparse
import json

from tqdm import tqdm




def       A_      (       )  ->				str:


     """simple docstring"""







     UpperCAmelCase_    :     List[Any]            =	argparse.ArgumentParser()

     # Required parameters
     parser.add_argument(
         """--src_path"""    ,			type=lowercase    ,			default="""biencoder-nq-dev.json"""    ,			help="""Path to raw DPR training data"""    ,			)
     parser.add_argument(
         """--evaluation_set"""    ,			type=lowercase    ,			help="""where to store parsed evaluation_set file"""    ,			)
     parser.add_argument(
         """--gold_data_path"""    ,			type=lowercase    ,			help="""where to store parsed gold_data_path file"""    ,			)
     UpperCAmelCase_    :     Dict            =	parser.parse_args()

     with open(args.src_path    ,			"""r"""   ) as src_file, open(args.evaluation_set    ,			"""w"""   ) as eval_file, open(
         args.gold_data_path    ,			"""w"""   ) as gold_file:
          UpperCAmelCase_    :     List[Any]            =	json.load(lowercase   )
          for dpr_record in tqdm(lowercase   ):
               UpperCAmelCase_    :     List[Any]            =	dpr_record["""question"""]
               UpperCAmelCase_    :     Dict            =	[context["""title"""] for context in dpr_record["""positive_ctxs"""]]
               eval_file.write(question + """\n"""   )
               gold_file.write("""\t""".join(lowercase   ) + """\n"""   )


if __name__ == "__main__":
       main()

