





'''simple docstring'''


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor






class       UpperCamelCase__  (		unittest.TestCase     ):







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




				'''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







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				return {
				    # here we create 2 clusters for the sake of simplicity
				    "clusters": np.asarray(
				        [
				            [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
				            [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
				        ]		),
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				}






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







	def 						__a   (				self			:   List[Any]		):




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







	@property
	def 						__a   (				self			:   Any		):




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







	def 						__a   (				self			:   Optional[int]		):




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







	def 						__a   (				self			:   List[str]		):




				'''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			:   int		):




				'''simple docstring'''
				a__     = self.image_processing_class(**self.image_processor_dict		)
				a__     = json.loads(image_processor.to_json_string()		)
				for key, value in self.image_processor_dict.items():
							if key == "clusters":
										self.assertTrue(np.array_equal(lowerCamelCase , obj[key]		)		)
							else:
										self.assertEqual(obj[key] , lowerCamelCase		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = self.image_processing_class(**self.image_processor_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							a__     = os.path.join(lowerCamelCase , "image_processor.json"		)
							image_processor_first.to_json_file(lowerCamelCase		)
							a__     = self.image_processing_class.from_json_file(lowerCamelCase		).to_dict()

				a__     = image_processor_first.to_dict()
				for key, value in image_processor_first.items():
							if key == "clusters":
										self.assertTrue(np.array_equal(lowerCamelCase , image_processor_second[key]		)		)
							else:
										self.assertEqual(image_processor_first[key] , lowerCamelCase		)







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = self.image_processing_class(**self.image_processor_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							image_processor_first.save_pretrained(lowerCamelCase		)
							a__     = self.image_processing_class.from_pretrained(lowerCamelCase		).to_dict()

				a__     = image_processor_first.to_dict()
				for key, value in image_processor_first.items():
							if key == "clusters":
										self.assertTrue(np.array_equal(lowerCamelCase , image_processor_second[key]		)		)
							else:
										self.assertEqual(image_processor_first[key] , lowerCamelCase		)







	@unittest.skip("ImageGPT requires clusters at initialization"		)
	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				pass







def    _lowerCamelCase  ()   ->    Optional[Any]:
			a__     = load_dataset("hf-internal-testing/fixtures_image_utils"					,  split="test"     )

			a__     = Image.open(dataset[4]["file"]     )
			a__     = Image.open(dataset[5]["file"]     )

			a__     = [imagea, imagea]

			return images






@require_vision
@require_torch
class       UpperCamelCase__  (		unittest.TestCase     ):







	@slow
	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"		)

				a__     = prepare_images()

				# test non-batched
				a__     = image_processing(images[0] , return_tensors="pt"		)

				self.assertIsInstance(encoding.input_ids , torch.LongTensor		)
				self.assertEqual(encoding.input_ids.shape , (1, 1_0_2_4)		)

				a__     = [3_0_6, 1_9_1, 1_9_1]
				self.assertEqual(encoding.input_ids[0, :3].tolist() , lowerCamelCase		)

				# test batched
				a__     = image_processing(lowerCamelCase , return_tensors="pt"		)

				self.assertIsInstance(encoding.input_ids , torch.LongTensor		)
				self.assertEqual(encoding.input_ids.shape , (2, 1_0_2_4)		)

				a__     = [3_0_3, 1_3, 1_3]
				self.assertEqual(encoding.input_ids[1, -3:].tolist() , lowerCamelCase		)










'''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()



