




'''simple docstring'''

import unittest

import numpy as np

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

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 GLPNImageProcessor



class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			def __init__(      self						:    int      ,		a_						:    Any      ,		a_						:    Tuple=7      ,		a_						:    Dict=3      ,		a_						:    Any=18      ,		a_						:    Tuple=30      ,		a_						:    Optional[Any]=400      ,		a_						:    Union[str, Any]=True      ,		a_						:    List[Any]=32      ,		a_						:    int=True      ,		):



						"""simple docstring"""




						__snake_case			=		parent
						__snake_case			=		batch_size
						__snake_case			=		num_channels
						__snake_case			=		image_size
						__snake_case			=		min_resolution
						__snake_case			=		max_resolution
						__snake_case			=		do_resize
						__snake_case			=		size_divisor
						__snake_case			=		do_rescale






			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						return {
						    "do_resize": self.do_resize,
						    "size_divisor": self.size_divisor,
						    "do_rescale": self.do_rescale,
						}







@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase				,     unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			GLPNImageProcessor if is_vision_available() else None





			def 							A (      self						:    Any							):



						"""simple docstring"""




						__snake_case			=		GLPNImageProcessingTester(self							)





			@property
			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						return self.image_processor_tester.prepare_image_processor_dict()





			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(a_      ,		"do_resize"							)							)
						self.assertTrue(hasattr(a_      ,		"size_divisor"							)							)
						self.assertTrue(hasattr(a_      ,		"resample"							)							)
						self.assertTrue(hasattr(a_      ,		"do_rescale"							)							)





			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						pass





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						__snake_case			=		self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						__snake_case			=		prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=a_							)
						for image in image_inputs:
									self.assertIsInstance(a_      ,		Image.Image							)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						__snake_case			=		image_processing(image_inputs[0]      ,		return_tensors="pt"							).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0							)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0							)





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						__snake_case			=		prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=a_      ,		numpify=a_							)
						for image in image_inputs:
									self.assertIsInstance(a_      ,		np.ndarray							)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						__snake_case			=		image_processing(image_inputs[0]      ,		return_tensors="pt"							).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0							)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0							)






			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						__snake_case			=		self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						__snake_case			=		prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=a_      ,		torchify=a_							)
						for image in image_inputs:
									self.assertIsInstance(a_      ,		torch.Tensor							)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						__snake_case			=		image_processing(image_inputs[0]      ,		return_tensors="pt"							).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0							)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0							)










"""simple docstring"""

import argparse
import json

from tqdm import tqdm
def  __snake_case							(	)     ->       Any:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							str							=     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'							,		)
				SCREAMING_SNAKE_CASE						:							Tuple							=     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:
								SCREAMING_SNAKE_CASE						:							Tuple							=     json.load(__A     )
								for dpr_record in tqdm(__A     ):
												SCREAMING_SNAKE_CASE						:							Tuple							=     dpr_record['question']
												SCREAMING_SNAKE_CASE						:							Optional[int]							=     [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()





