





'''simple docstring'''


import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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 transformers import OneFormerImageProcessor
				from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
				from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
		from PIL import Image







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Optional[int]="shi-labs/oneformer_demo"     )   ->    List[Any]:
			with open(hf_hub_download(__lowerCamelCase					,  __lowerCamelCase					,  repo_type="dataset"     )					,  "r"     ) as f:
						a__     = json.load(__lowerCamelCase     )
			a__     = {}
			a__     = []
			a__     = []
			for key, info in class_info.items():
						a__     = info["name"]
						class_names.append(info["name"]     )
						if info["isthing"]:
									thing_ids.append(int(__lowerCamelCase     )     )
			a__     = thing_ids
			a__     = class_names
			return metadata






class       UpperCamelCase__  (		unittest.TestCase     ):







	def __init__(				self			:   List[Any] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Any=7 , lowerCamelCase			:   List[Any]=3 , lowerCamelCase			:   Dict=3_0 , lowerCamelCase			:   Optional[int]=4_0_0 , lowerCamelCase			:   int=None , lowerCamelCase			:   List[str]=True , lowerCamelCase			:   List[Any]=True , lowerCamelCase			:   Any=[0.5, 0.5, 0.5] , lowerCamelCase			:   Dict=[0.5, 0.5, 0.5] , lowerCamelCase			:   List[Any]=1_0 , lowerCamelCase			:   Tuple=False , lowerCamelCase			:   Tuple=2_5_5 , lowerCamelCase			:   str="shi-labs/oneformer_demo" , lowerCamelCase			:   int="ade20k_panoptic.json" , lowerCamelCase			:   str=1_0 , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = num_channels
				a__     = min_resolution
				a__     = max_resolution
				a__     = do_resize
				a__     = {"shortest_edge": 3_2, "longest_edge": 1_3_3_3} if size is None else size
				a__     = do_normalize
				a__     = image_mean
				a__     = image_std
				a__     = class_info_file
				a__     = prepare_metadata(lowerCamelCase , lowerCamelCase		)
				a__     = num_text
				a__     = repo_path

				# for the post_process_functions
				a__     = 2
				a__     = 1_0
				a__     = 1_0
				a__     = 3
				a__     = 4
				a__     = num_labels
				a__     = do_reduce_labels
				a__     = ignore_index







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "num_labels": self.num_labels,
				    "do_reduce_labels": self.do_reduce_labels,
				    "ignore_index": self.ignore_index,
				    "class_info_file": self.class_info_file,
				    "metadata": self.metadata,
				    "num_text": self.num_text,
				}







	def 						__a   (				self			:   Dict , lowerCamelCase			:   List[str] , lowerCamelCase			:   int=False		):




				'''simple docstring'''
				if not batched:
							a__     = image_inputs[0]
							if isinstance(lowerCamelCase , Image.Image		):
										a__   ,				a__     = image.size
							else:
										a__   ,				a__     = image.shape[1], image.shape[2]
							if w < h:
										a__     = int(self.size["shortest_edge"] * h / w		)
										a__     = self.size["shortest_edge"]
							elif w > h:
										a__     = self.size["shortest_edge"]
										a__     = int(self.size["shortest_edge"] * w / h		)
							else:
										a__     = self.size["shortest_edge"]
										a__     = self.size["shortest_edge"]

				else:
							a__     = []
							for image in image_inputs:
										a__   ,				a__     = self.get_expected_values([image]		)
										expected_values.append((expected_height, expected_width)		)
							a__     = max(lowerCamelCase , key=lambda lowerCamelCase		: item[0]		)[0]
							a__     = max(lowerCamelCase , key=lambda lowerCamelCase		: item[1]		)[1]

				return expected_height, expected_width







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				return OneFormerForUniversalSegmentationOutput(
				    # +1 for null class
				    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)		) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)		) , )






@require_torch
@require_vision
class       UpperCamelCase__  (		__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			Dict								=						OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	lowerCAmelCase__					:			List[str]								=						image_processing_class







	def 						__a   (				self			:   Any		):




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







	@property
	def 						__a   (				self			:   Dict		):




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







	def 						__a   (				self			:   Any		):




				'''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"		)		)
				self.assertTrue(hasattr(lowerCamelCase , "ignore_index"		)		)
				self.assertTrue(hasattr(lowerCamelCase , "class_info_file"		)		)
				self.assertTrue(hasattr(lowerCamelCase , "num_text"		)		)
				self.assertTrue(hasattr(lowerCamelCase , "repo_path"		)		)
				self.assertTrue(hasattr(lowerCamelCase , "metadata"		)		)
				self.assertTrue(hasattr(lowerCamelCase , "do_reduce_labels"		)		)







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




				'''simple docstring'''
				pass







	def 						__a   (				self			:   List[Any]		):




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

				# Test not batched input
				a__     = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt"		).pixel_values

				a__   ,				a__     = self.image_processing_tester.get_expected_values(lowerCamelCase		)

				self.assertEqual(
				    encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )

				# Test batched
				a__   ,				a__     = self.image_processing_tester.get_expected_values(lowerCamelCase , batched=lowerCamelCase		)

				a__     = image_processor(
				    lowerCamelCase , ["semantic"] * len(lowerCamelCase		) , return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape , (
				        self.image_processing_tester.batch_size,
				        self.image_processing_tester.num_channels,
				        expected_height,
				        expected_width,
				    ) , )







	def 						__a   (				self			:   Optional[int]		):




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

				# Test not batched input
				a__     = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt"		).pixel_values

				a__   ,				a__     = self.image_processing_tester.get_expected_values(lowerCamelCase		)

				self.assertEqual(
				    encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )

				# Test batched
				a__   ,				a__     = self.image_processing_tester.get_expected_values(lowerCamelCase , batched=lowerCamelCase		)

				a__     = image_processor(
				    lowerCamelCase , ["semantic"] * len(lowerCamelCase		) , return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape , (
				        self.image_processing_tester.batch_size,
				        self.image_processing_tester.num_channels,
				        expected_height,
				        expected_width,
				    ) , )







	def 						__a   (				self			:   Optional[Any]		):




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

				# Test not batched input
				a__     = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt"		).pixel_values

				a__   ,				a__     = self.image_processing_tester.get_expected_values(lowerCamelCase		)

				self.assertEqual(
				    encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )

				# Test batched
				a__   ,				a__     = self.image_processing_tester.get_expected_values(lowerCamelCase , batched=lowerCamelCase		)

				a__     = image_processor(
				    lowerCamelCase , ["semantic"] * len(lowerCamelCase		) , return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape , (
				        self.image_processing_tester.batch_size,
				        self.image_processing_tester.num_channels,
				        expected_height,
				        expected_width,
				    ) , )







	def 						__a   (				self			:   Any , lowerCamelCase			:   List[str]=False , lowerCamelCase			:   Any=False , lowerCamelCase			:   List[Any]="np"		):




				'''simple docstring'''
				a__     = self.image_processing_class(**self.image_processor_dict		)
				# prepare image and target
				a__     = self.image_processing_tester.num_labels
				a__     = None
				a__     = None
				a__     = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCamelCase		)
				if with_segmentation_maps:
							a__     = num_labels
							if is_instance_map:
										a__     = list(range(lowerCamelCase		)		) * 2
										a__     = dict(enumerate(lowerCamelCase		)		)
							a__     = [
							    np.random.randint(0 , high * 2 , (img.size[1], img.size[0])		).astype(np.uinta		) for img in image_inputs
							]
							if segmentation_type == "pil":
										a__     = [Image.fromarray(lowerCamelCase		) for annotation in annotations]

				a__     = image_processor(
				    lowerCamelCase , ["semantic"] * len(lowerCamelCase		) , lowerCamelCase , return_tensors="pt" , instance_id_to_semantic_id=lowerCamelCase , pad_and_return_pixel_mask=lowerCamelCase , )

				return inputs







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				pass







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				def common(lowerCamelCase			:   int=False , lowerCamelCase			:   Dict=None		):
							a__     = self.comm_get_image_processor_inputs(
							    with_segmentation_maps=lowerCamelCase , is_instance_map=lowerCamelCase , segmentation_type=lowerCamelCase		)

							a__     = inputs["mask_labels"]
							a__     = inputs["class_labels"]
							a__     = inputs["pixel_values"]
							a__     = inputs["text_inputs"]

							# check the batch_size
							for mask_label, class_label, text_input in zip(lowerCamelCase , lowerCamelCase , lowerCamelCase		):
										self.assertEqual(mask_label.shape[0] , class_label.shape[0]		)
										# this ensure padding has happened
										self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:]		)
										self.assertEqual(len(lowerCamelCase		) , self.image_processing_tester.num_text		)

				common()
				common(is_instance_map=lowerCamelCase		)
				common(is_instance_map=lowerCamelCase , segmentation_type="pil"		)
				common(is_instance_map=lowerCamelCase , segmentation_type="pil"		)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				a__     = np.zeros((2_0, 5_0)		)
				a__     = 1
				a__     = 1
				a__     = 1

				a__     = binary_mask_to_rle(lowerCamelCase		)
				self.assertEqual(len(lowerCamelCase		) , 4		)
				self.assertEqual(rle[0] , 2_1		)
				self.assertEqual(rle[1] , 4_5		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes , max_seq_length=7_7 , task_seq_length=7_7 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
				a__     = self.image_processing_tester.get_fake_oneformer_outputs()

				a__     = fature_extractor.post_process_semantic_segmentation(lowerCamelCase		)

				self.assertEqual(len(lowerCamelCase		) , self.image_processing_tester.batch_size		)
				self.assertEqual(
				    segmentation[0].shape , (
				        self.image_processing_tester.height,
				        self.image_processing_tester.width,
				    ) , )

				a__     = [(1, 4) for i in range(self.image_processing_tester.batch_size		)]
				a__     = fature_extractor.post_process_semantic_segmentation(lowerCamelCase , target_sizes=lowerCamelCase		)

				self.assertEqual(segmentation[0].shape , target_sizes[0]		)







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes , max_seq_length=7_7 , task_seq_length=7_7 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
				a__     = self.image_processing_tester.get_fake_oneformer_outputs()
				a__     = image_processor.post_process_instance_segmentation(lowerCamelCase , threshold=0		)

				self.assertTrue(len(lowerCamelCase		) == self.image_processing_tester.batch_size		)
				for el in segmentation:
							self.assertTrue("segmentation" in el		)
							self.assertTrue("segments_info" in el		)
							self.assertEqual(type(el["segments_info"]		) , lowerCamelCase		)
							self.assertEqual(
							    el["segmentation"].shape , (self.image_processing_tester.height, self.image_processing_tester.width)		)







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes , max_seq_length=7_7 , task_seq_length=7_7 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
				a__     = self.image_processing_tester.get_fake_oneformer_outputs()
				a__     = image_processor.post_process_panoptic_segmentation(lowerCamelCase , threshold=0		)

				self.assertTrue(len(lowerCamelCase		) == self.image_processing_tester.batch_size		)
				for el in segmentation:
							self.assertTrue("segmentation" in el		)
							self.assertTrue("segments_info" in el		)
							self.assertEqual(type(el["segments_info"]		) , lowerCamelCase		)
							self.assertEqual(
							    el["segmentation"].shape , (self.image_processing_tester.height, self.image_processing_tester.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()



