





'''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


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import BlipImageProcessor



class  lowerCamelCase__				(						unittest.TestCase						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=7   ,     UpperCAmelCase__=3   ,     UpperCAmelCase__=1_8   ,     UpperCAmelCase__=3_0   ,     UpperCAmelCase__=4_0_0   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=False   ,     UpperCAmelCase__=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]   ,     UpperCAmelCase__=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]   ,     UpperCAmelCase__=True   ,     )       ->				int:
					_A :				Any				=  size if size is not None else {'''height''': 2_0, '''width''': 2_0}
					_A :				List[Any]				=  parent
					_A :				Optional[Any]				=  batch_size
					_A :				Dict				=  num_channels
					_A :				int				=  image_size
					_A :				Optional[Any]				=  min_resolution
					_A :				List[Any]				=  max_resolution
					_A :				List[Any]				=  do_resize
					_A :				List[Any]				=  size
					_A :				Union[str, Any]				=  do_normalize
					_A :				Optional[Any]				=  image_mean
					_A :				Optional[Any]				=  image_std
					_A :				Optional[Any]				=  do_pad
					_A :				List[Any]				=  do_convert_rgb




	def 				_lowerCamelCase				(					self			)       ->				Any:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_convert_rgb": self.do_convert_rgb,
					    "do_pad": self.do_pad,
					}




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__=False   ,     UpperCAmelCase__=False   ,     UpperCAmelCase__=False			)       ->				Tuple:
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					if equal_resolution:
									_A :				Dict				=  []
									for i in range(self.batch_size			):
													image_inputs.append(
													    np.random.randint(
													        2_5_5   ,     size=(self.num_channels, self.max_resolution, self.max_resolution)   ,     dtype=np.uinta			)			)
					else:
									_A :				int				=  []
									for i in range(self.batch_size			):
													_A	,       _A :				Optional[int]				=  np.random.choice(np.arange(self.min_resolution   ,     self.max_resolution			)   ,     2			)
													image_inputs.append(np.random.randint(2_5_5   ,     size=(self.num_channels, width, height)   ,     dtype=np.uinta			)			)

					if not numpify and not torchify:
									# PIL expects the channel dimension as last dimension
									_A :				int				=  [Image.fromarray(np.moveaxis(UpperCAmelCase__   ,     0   ,     -1			)			) for x in image_inputs]

					if torchify:
									_A :				List[Any]				=  [torch.from_numpy(UpperCAmelCase__			) for x in image_inputs]

					return image_inputs



@require_torch
@require_vision
class  lowerCamelCase__				(						snake_case_  ,							unittest.TestCase						):


	"""simple docstring"""





	__magic_name__							   =		BlipImageProcessor if is_vision_available() else None




	def 				_lowerCamelCase				(					self			)       ->				Union[str, Any]:
					_A :				Any				=  BlipImageProcessingTester(self			)




	@property
	def 				_lowerCamelCase				(					self			)       ->				Tuple:
					return self.image_processor_tester.prepare_image_processor_dict()




	def 				_lowerCamelCase				(					self			)       ->				List[str]:
					_A :				Optional[Any]				=  self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''do_resize'''			)			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''size'''			)			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''do_normalize'''			)			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''image_mean'''			)			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''image_std'''			)			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''do_convert_rgb'''			)			)




	def 				_lowerCamelCase				(					self			)       ->				Optional[int]:
					pass




	def 				_lowerCamelCase				(					self			)       ->				int:
					# Initialize image_processor
					_A :				str				=  self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					_A :				List[Any]				=  self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase__			)
					for image in image_inputs:
									self.assertIsInstance(UpperCAmelCase__   ,     Image.Image			)

					# Test not batched input
					_A :				int				=  image_processor(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 :				int				=  image_processor(UpperCAmelCase__   ,     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 				_lowerCamelCase				(					self			)       ->				str:
					# Initialize image_processor
					_A :				int				=  self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					_A :				int				=  self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase__   ,     numpify=UpperCAmelCase__			)
					for image in image_inputs:
									self.assertIsInstance(UpperCAmelCase__   ,     np.ndarray			)

					# Test not batched input
					_A :				Dict				=  image_processor(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 :				Optional[int]				=  image_processor(UpperCAmelCase__   ,     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 				_lowerCamelCase				(					self			)       ->				Any:
					# Initialize image_processor
					_A :				Any				=  self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					_A :				Dict				=  self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase__   ,     torchify=UpperCAmelCase__			)
					for image in image_inputs:
									self.assertIsInstance(UpperCAmelCase__   ,     torch.Tensor			)

					# Test not batched input
					_A :				Dict				=  image_processor(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 :				str				=  image_processor(UpperCAmelCase__   ,     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'''],
					    )   ,     )



@require_torch
@require_vision
class  lowerCamelCase__				(						snake_case_  ,							unittest.TestCase						):


	"""simple docstring"""





	__magic_name__							   =		BlipImageProcessor if is_vision_available() else None




	def 				_lowerCamelCase				(					self			)       ->				Dict:
					_A :				int				=  BlipImageProcessingTester(self   ,     num_channels=4			)
					_A :				Optional[int]				=  3




	@property
	def 				_lowerCamelCase				(					self			)       ->				str:
					return self.image_processor_tester.prepare_image_processor_dict()




	def 				_lowerCamelCase				(					self			)       ->				Any:
					_A :				Dict				=  self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''do_resize'''			)			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''size'''			)			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''do_normalize'''			)			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''image_mean'''			)			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''image_std'''			)			)
					self.assertTrue(hasattr(UpperCAmelCase__   ,     '''do_convert_rgb'''			)			)




	def 				_lowerCamelCase				(					self			)       ->				str:
					pass




	def 				_lowerCamelCase				(					self			)       ->				Optional[Any]:
					# Initialize image_processor
					_A :				Tuple				=  self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					_A :				int				=  self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase__			)
					for image in image_inputs:
									self.assertIsInstance(UpperCAmelCase__   ,     Image.Image			)

					# Test not batched input
					_A :				str				=  image_processor(image_inputs[0]   ,     return_tensors='''pt'''			).pixel_values
					self.assertEqual(
					    encoded_images.shape   ,     (
					        1,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )   ,     )

					# Test batched
					_A :				Dict				=  image_processor(UpperCAmelCase__   ,     return_tensors='''pt'''			).pixel_values
					self.assertEqual(
					    encoded_images.shape   ,     (
					        self.image_processor_tester.batch_size,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )   ,     )







'''simple docstring'''






import argparse
import json
import re
import string
import sys
from collections import Counter





def      lowercase  (  lowerCAmelCase :						int):



				"""simple docstring"""


				def remove_articles(lowerCAmelCase :						List[Any]):
								return re.sub(R'''\b(a|an|the)\b'''   , ''' '''   , lowerCAmelCase)

				def white_space_fix(lowerCAmelCase :						Tuple):
								return " ".join(text.split())

				def remove_punc(lowerCAmelCase :						str):
								_A :				List[Any]				=  set(string.punctuation)
								return "".join(ch for ch in text if ch not in exclude)

				def lower(lowerCAmelCase :						Any):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase))))





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Dict):



				"""simple docstring"""


				_A :				str				=  normalize_answer(lowerCAmelCase).split()
				_A :				Dict				=  normalize_answer(lowerCAmelCase).split()
				_A :				str				=  Counter(lowerCAmelCase) & Counter(lowerCAmelCase)
				_A :				List[Any]				=  sum(common.values())
				if num_same == 0:
								return 0
				_A :				Tuple				=  1.0 * num_same / len(lowerCAmelCase)
				_A :				List[Any]				=  1.0 * num_same / len(lowerCAmelCase)
				_A :				int				=  (2 * precision * recall) / (precision + recall)
				return fa





def      lowercase  (  lowerCAmelCase :						Any   , lowerCAmelCase :						str):



				"""simple docstring"""


				return normalize_answer(lowerCAmelCase) == normalize_answer(lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				_A :				Optional[int]				=  []
				for ground_truth in ground_truths:
								_A :				Union[str, Any]				=  metric_fn(lowerCAmelCase   , lowerCAmelCase)
								scores_for_ground_truths.append(lowerCAmelCase)
				return max(lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						str):



				"""simple docstring"""


				_A :				Union[str, Any]				=  0
				_A :				List[Any]				=  []
				for passage in dataset:
								for qa in passage["qas"]:
												total += 1
												if qa["id"] not in predictions:
																_A :				str				=  f"""Unanswered question {qa['id']} will receive score 0."""
																print(lowerCAmelCase   , file=sys.stderr)
																continue

												_A :				Optional[int]				=  [x['''text'''] for x in qa['''answers''']]
												_A :				Union[str, Any]				=  predictions[qa['''id''']]

												_A :				Optional[Any]				=  metric_max_over_ground_truths(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
												if int(_exact_match) == 1:
																correct_ids.append(qa['''id'''])
												exact_match += _exact_match

												fa += metric_max_over_ground_truths(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)

				_A :				Dict				=  exact_match / total
				_A :				Dict				=  fa / total

				return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
	__UpperCamelCase      :   List[str]								=     '''1.0'''
	__UpperCamelCase      :   Union[str, Any]								=     argparse.ArgumentParser('''Official evaluation script for ReCoRD v1.0.''')
	parser.add_argument('''data_file''', help='''The dataset file in JSON format.''')
	parser.add_argument('''pred_file''', help='''The model prediction file in JSON format.''')
	parser.add_argument('''--output_correct_ids''', action='''store_true''', help='''Output the correctly answered query IDs.''')
	__UpperCamelCase      :   Optional[int]								=     parser.parse_args()

	with open(args.data_file) as data_file:
		__UpperCamelCase      :   Optional[int]								=     json.load(data_file)
		if dataset_json["version"] != expected_version:
			print(
			    f'Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}',
			    file=sys.stderr,
			)
		__UpperCamelCase      :   Optional[Any]								=     dataset_json['''data''']

	with open(args.pred_file) as pred_file:
		__UpperCamelCase      :   int								=     json.load(pred_file)

	__UpperCamelCase							,  __UpperCamelCase      :   List[str]								=     evaluate(dataset, predictions)

	if args.output_correct_ids:
		print(f'Output {len(correct_ids)} correctly answered question IDs.')
		with open('''correct_ids.json''', '''w''') as f:
			json.dump(correct_ids, f)
