





'''simple docstring'''


import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor






class       UpperCamelCase__  (		unittest.TestCase     ):







 def __init__(				self			:   Optional[Any] , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Any=7 , lowerCamelCase			:   Optional[Any]=3 , lowerCamelCase			:   List[str]=1_8 , lowerCamelCase			:   Optional[int]=3_0 , lowerCamelCase			:   int=4_0_0 , lowerCamelCase			:   Dict=True , lowerCamelCase			:   List[Any]=None , lowerCamelCase			:   Any=True , lowerCamelCase			:   List[str]=[0.5, 0.5, 0.5] , lowerCamelCase			:   List[Any]=[0.5, 0.5, 0.5] , ):




    '''simple docstring'''
    a__     = size if size is not None else {"height": 1_8, "width": 1_8}
    a__     = parent
    a__     = batch_size
    a__     = num_channels
    a__     = image_size
    a__     = min_resolution
    a__     = max_resolution
    a__     = do_resize
    a__     = size
    a__     = do_normalize
    a__     = image_mean
    a__     = image_std







 def 						__a   (				self			:   Tuple		):




    '''simple docstring'''
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "size": self.size,
    }






@require_torch
@require_vision
class       UpperCamelCase__  (		__lowerCAmelCase	,unittest.TestCase     ):
 lowerCAmelCase__					:			Dict								=						DPTImageProcessor if is_vision_available() else None







 def 						__a   (				self			:   Any		):




    '''simple docstring'''
    a__     = DPTImageProcessingTester(self		)







 @property
 def 						__a   (				self			:   int		):




    '''simple docstring'''
    return self.image_processor_tester.prepare_image_processor_dict()







 def 						__a   (				self			:   Tuple		):




    '''simple docstring'''
    a__     = self.image_processing_class(**self.image_processor_dict		)
    self.assertTrue(hasattr(lowerCamelCase , "image_mean"		)		)
    self.assertTrue(hasattr(lowerCamelCase , "image_std"		)		)
    self.assertTrue(hasattr(lowerCamelCase , "do_normalize"		)		)
    self.assertTrue(hasattr(lowerCamelCase , "do_resize"		)		)
    self.assertTrue(hasattr(lowerCamelCase , "size"		)		)







 def 						__a   (				self			:   Optional[int]		):




    '''simple docstring'''
    a__     = self.image_processing_class.from_dict(self.image_processor_dict		)
    self.assertEqual(image_processor.size , {"height": 1_8, "width": 1_8}		)

    a__     = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2		)
    self.assertEqual(image_processor.size , {"height": 4_2, "width": 4_2}		)







 def 						__a   (				self			:   Tuple		):




    '''simple docstring'''
    a__     = self.image_processing_class(**self.image_processor_dict		)
    # create random PIL images
    a__     = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase		)
    for image in image_inputs:
       self.assertIsInstance(lowerCamelCase , Image.Image		)

    # Test not batched input
    a__     = image_processing(image_inputs[0] , return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape , (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ) , )

    # Test batched
    a__     = image_processing(lowerCamelCase , return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ) , )







 def 						__a   (				self			:   Tuple		):




    '''simple docstring'''
    a__     = self.image_processing_class(**self.image_processor_dict		)
    # create random numpy tensors
    a__     = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , numpify=lowerCamelCase		)
    for image in image_inputs:
       self.assertIsInstance(lowerCamelCase , np.ndarray		)

    # Test not batched input
    a__     = image_processing(image_inputs[0] , return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape , (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ) , )

    # Test batched
    a__     = image_processing(lowerCamelCase , return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ) , )







 def 						__a   (				self			:   Optional[Any]		):




    '''simple docstring'''
    a__     = self.image_processing_class(**self.image_processor_dict		)
    # create random PyTorch tensors
    a__     = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , torchify=lowerCamelCase		)
    for image in image_inputs:
       self.assertIsInstance(lowerCamelCase , torch.Tensor		)

    # Test not batched input
    a__     = image_processing(image_inputs[0] , return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape , (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ) , )

    # Test batched
    a__     = image_processing(lowerCamelCase , return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ) , )










'''simple docstring'''


import argparse
import json

from tqdm import tqdm







def    _lowerCamelCase  ()   ->    Any:
			a__     = argparse.ArgumentParser()

			# Required parameters
			parser.add_argument(
			    "--src_path"					,  type=__lowerCamelCase					,  default="biencoder-nq-dev.json"					,  help="Path to raw DPR training data"					,  )
			parser.add_argument(
			    "--evaluation_set"					,  type=__lowerCamelCase					,  help="where to store parsed evaluation_set file"					,  )
			parser.add_argument(
			    "--gold_data_path"					,  type=__lowerCamelCase					,  help="where to store parsed gold_data_path file"					,  )
			a__     = 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__     = json.load(__lowerCamelCase     )
						for dpr_record in tqdm(__lowerCamelCase     ):
									a__     = dpr_record["question"]
									a__     = [context["title"] for context in dpr_record["positive_ctxs"]]
									eval_file.write(question + "\n"     )
									gold_file.write("\t".join(__lowerCamelCase     ) + "\n"     )


if __name__ == "__main__":
		main()



