




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




def      __magic_name__							(					A						:   Dict  ):






			'''simple docstring'''







			def remove_articles(A						:   Any  ):
						return re.sub(R"\b(a|an|the)\b",    " ",    A  )

			def white_space_fix(A						:   int  ):
						return " ".join(text.split()  )

			def remove_punc(A						:   int  ):
						a       =     set(string.punctuation  )
						return "".join(ch for ch in text if ch not in exclude  )

			def lower(A						:   List[Any]  ):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(A  )  )  )  )




def      __magic_name__							(					A						:   List[str],    A						:   str  ):






			'''simple docstring'''







			a       =     normalize_answer(A  ).split()
			a       =     normalize_answer(A  ).split()
			a       =     Counter(A  ) & Counter(A  )
			a       =     sum(common.values()  )
			if num_same == 0:
						return 0
			a       =     1.0 * num_same / len(A  )
			a       =     1.0 * num_same / len(A  )
			a       =     (2 * precision * recall) / (precision + recall)
			return fa




def      __magic_name__							(					A						:   Optional[int],    A						:   Optional[int]  ):






			'''simple docstring'''







			return normalize_answer(A  ) == normalize_answer(A  )




def      __magic_name__							(					A						:   List[Any],    A						:   Optional[int],    A						:   Optional[int]  ):






			'''simple docstring'''







			a       =     []
			for ground_truth in ground_truths:
						a       =     metric_fn(A,    A  )
						scores_for_ground_truths.append(A  )
			return max(A  )




def      __magic_name__							(					A						:   str,    A						:   List[Any]  ):






			'''simple docstring'''







			a       =     a       =     a       =     0
			a       =     []
			for passage in dataset:
						for qa in passage["qas"]:
									total += 1
									if qa["id"] not in predictions:
												a       =     F"""Unanswered question {qa["id"]} will receive score 0."""
												print(A,    file=sys.stderr  )
												continue

									a       =     [x["text"] for x in qa["answers"]]
									a       =     predictions[qa["id"]]

									a       =     metric_max_over_ground_truths(A,    A,    A  )
									if int(_exact_match  ) == 1:
												correct_ids.append(qa["id"]  )
									exact_match += _exact_match

									fa += metric_max_over_ground_truths(A,    A,    A  )

			a       =     exact_match / total
			a       =     fa / total

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


if __name__ == "__main__":
		__lowerCAmelCase						:							int			     =						'1.0'
		__lowerCAmelCase						:							List[str]			     =						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.')
		__lowerCAmelCase						:							Dict			     =						parser.parse_args()

		with open(args.data_file) as data_file:
				__lowerCAmelCase						:							List[Any]			     =						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,
						)
				__lowerCAmelCase						:							Dict			     =						dataset_json['data']

		with open(args.pred_file) as pred_file:
				__lowerCAmelCase						:							List[Any]			     =						json.load(pred_file)

		__lowerCAmelCase     ,	__lowerCAmelCase						:							Tuple			     =						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)












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 SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				def __init__( self							:	Union[str, Any]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	List[str]=7   , __lowerCamelCase							:	int=3   , __lowerCamelCase							:	Any=18   , __lowerCamelCase							:	Optional[int]=30   , __lowerCamelCase							:	Union[str, Any]=4_00   , __lowerCamelCase							:	Optional[int]=True   , __lowerCamelCase							:	List[str]=1 / 2_55   , __lowerCamelCase							:	Any=True   , __lowerCamelCase							:	Optional[int]=8   , )   ->	Optional[Any]:
							a       =     parent
							a       =     batch_size
							a       =     num_channels
							a       =     image_size
							a       =     min_resolution
							a       =     max_resolution
							a       =     do_rescale
							a       =     rescale_factor
							a       =     do_pad
							a       =     pad_size





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Optional[int]:
							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	Any=False   , __lowerCamelCase							:	List[Any]=False				)   ->	List[Any]:
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
										a       =     []
										for i in range(self.batch_size				):
													image_inputs.append(
													    np.random.randint(
													        2_55   , size=(self.num_channels, self.max_resolution, self.max_resolution)   , dtype=np.uinta				)				)
							else:
										a       =     []
										for i in range(self.batch_size				):
													a       ,						a       =     np.random.choice(np.arange(self.min_resolution   , self.max_resolution				)   , 2				)
													image_inputs.append(np.random.randint(2_55   , size=(self.num_channels, width, height)   , dtype=np.uinta				)				)

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

							if torchify:
										a       =     [torch.from_numpy(__lowerCamelCase				) for x in image_inputs]

							return image_inputs




@require_torch
@require_vision
class        snake_case__				(_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   SwinaSRImageProcessor if is_vision_available() else None





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     SwinaSRImageProcessingTester(self				)





				@property
				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	List[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_rescale"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "rescale_factor"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_pad"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "pad_size"				)				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Union[str, Any]:
							pass





				def 				__UpperCAmelCase				( self							:	int   , __lowerCamelCase							:	int				)   ->	Dict:
							a       ,						a       =     get_image_size(__lowerCamelCase				)
							a       =     self.image_processor_tester.pad_size

							a       =     (old_height // size + 1) * size - old_height
							a       =     (old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , Image.Image				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(np.array(image_inputs[0]				)				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Union[str, Any]:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							a       =     self.image_processor_tester.prepare_inputs(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]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	int:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							a       =     self.image_processor_tester.prepare_inputs(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]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )






