"""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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


lowercase__					       =	logging.get_logger(__name__)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  a__:     int										=	b.T
  a__:     List[Any]										=	np.sum(np.square(_SCREAMING_SNAKE_CASE   )		,     axis=1   )
  a__:     int										=	np.sum(np.square(_SCREAMING_SNAKE_CASE   )		,     axis=0   )
  a__:     Any										=	np.matmul(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  a__:     Optional[Any]										=	aa[:, None] - 2 * ab + ba[None, :]
  return d







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Any:
  a__:     Tuple										=	x.reshape(-1		,     3   )
  a__:     Any										=	squared_euclidean_distance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  return np.argmin(_SCREAMING_SNAKE_CASE		,     axis=1   )




class    __snake_case       (     snake_case__				):
      a__        =		['''pixel_values''']



      def __init__(						self    ,			lowercase = None    ,			lowercase = True    ,			lowercase = None    ,			lowercase = PILImageResampling.BILINEAR    ,			lowercase = True    ,			lowercase = True    ,			**lowercase    ,			)	->			Optional[Any]:





        '''simple docstring'''





        super().__init__(**_A)
        a__:     Optional[Any]										=	size if size is not None else {'height': 2_56, 'width': 2_56}
        a__:     Any										=	get_size_dict(_A)
        a__:     List[str]										=	np.array(_A) if clusters is not None else None
        a__:     int										=	do_resize
        a__:     Union[str, Any]										=	size
        a__:     Dict										=	resample
        a__:     List[Any]										=	do_normalize
        a__:     str										=	do_color_quantize



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase = PILImageResampling.BILINEAR    ,			lowercase = None    ,			**lowercase    ,			)	->			int:





        '''simple docstring'''





        a__:     Optional[Any]										=	get_size_dict(_A)
        if "height" not in size or "width" not in size:
          raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}')
        return resize(
            _A    ,			size=(size['height'], size['width'])    ,			resample=_A    ,			data_format=_A    ,			**_A)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = None    ,			)	->			Dict:





        '''simple docstring'''





        a__:     Dict										=	rescale(image=_A    ,			scale=1 / 1_27.5    ,			data_format=_A)
        a__:     str										=	image - 1
        return image



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			lowercase = ChannelDimension.FIRST    ,			**lowercase    ,			)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Any										=	do_resize if do_resize is not None else self.do_resize
        a__:     Dict										=	size if size is not None else self.size
        a__:     Union[str, Any]										=	get_size_dict(_A)
        a__:     Optional[Any]										=	resample if resample is not None else self.resample
        a__:     Optional[Any]										=	do_normalize if do_normalize is not None else self.do_normalize
        a__:     Optional[int]										=	do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        a__:     List[str]										=	clusters if clusters is not None else self.clusters
        a__:     str										=	np.array(_A)

        a__:     int										=	make_list_of_images(_A)

        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.')

        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_color_quantize and clusters is None:
          raise ValueError('Clusters must be specified if do_color_quantize is True.')

        # All transformations expect numpy arrays.
        a__:     str										=	[to_numpy_array(_A) for image in images]

        if do_resize:
          a__:     int										=	[self.resize(image=_A    ,			size=_A    ,			resample=_A) for image in images]

        if do_normalize:
          a__:     List[Any]										=	[self.normalize(image=_A) for image in images]

        if do_color_quantize:
          a__:     List[Any]										=	[to_channel_dimension_format(_A    ,			ChannelDimension.LAST) for image in images]
          # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
          a__:     List[Any]										=	np.array(_A)
          a__:     str										=	color_quantize(_A    ,			_A).reshape(images.shape[:-1])

          # flatten to (batch_size, height*width)
          a__:     Dict										=	images.shape[0]
          a__:     List[Any]										=	images.reshape(_A    ,			-1)

          # We need to convert back to a list of images to keep consistent behaviour across processors.
          a__:     Union[str, Any]										=	list(_A)
        else:
          a__:     Any										=	[to_channel_dimension_format(_A    ,			_A) for image in images]

        a__:     List[Any]										=	{'input_ids': images}
        return BatchFeature(data=_A    ,			tensor_type=_A)









import argparse
import json

from tqdm import tqdm







def  _SCREAMING_SNAKE_CASE				(   )      ->      List[Any]:
					__A	:						Tuple       =				argparse.ArgumentParser()

					# Required parameters
					parser.add_argument(
					    '--src_path'					,						type=a					,						default='biencoder-nq-dev.json'					,						help='Path to raw DPR training data'					,						)
					parser.add_argument(
					    '--evaluation_set'					,						type=a					,						help='where to store parsed evaluation_set file'					,						)
					parser.add_argument(
					    '--gold_data_path'					,						type=a					,						help='where to store parsed gold_data_path file'					,						)
					__A	:						Optional[int]       =				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:
										__A	:						List[Any]       =				json.load(a  )
										for dpr_record in tqdm(a  ):
															__A	:						Dict       =				dpr_record['question']
															__A	:						Any       =				[context['title'] for context in dpr_record['positive_ctxs']]
															eval_file.write(question + '\n'  )
															gold_file.write('\t'.join(a  ) + '\n'  )


if __name__ == "__main__":
				main()




