




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






def 					snake_case					(							A			)							->							int:




	'''simple docstring'''


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

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

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

	def lower(A			):
		return text.lower()

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






def 					snake_case					(							A						,   A			)							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							normalize_answer(A			).split()
	lowerCamelCase_							:		Any							   =							normalize_answer(A			).split()
	lowerCamelCase_							:		str							   =							Counter(A			) & Counter(A			)
	lowerCamelCase_							:		Optional[int]							   =							sum(common.values()			)
	if num_same == 0:
		return 0
	lowerCamelCase_							:		Union[str, Any]							   =							1.0 * num_same / len(A			)
	lowerCamelCase_							:		Optional[int]							   =							1.0 * num_same / len(A			)
	lowerCamelCase_							:		int							   =							(2 * precision * recall) / (precision + recall)
	return fa






def 					snake_case					(							A						,   A			)							->							int:




	'''simple docstring'''


	return normalize_answer(A			) == normalize_answer(A			)






def 					snake_case					(							A						,   A						,   A			)							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							[]
	for ground_truth in ground_truths:
		lowerCamelCase_							:		str							   =							metric_fn(A						,   A			)
		scores_for_ground_truths.append(A			)
	return max(A			)






def 					snake_case					(							A						,   A			)							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							0
	for article in dataset:
		for paragraph in article["paragraphs"]:
			for qa in paragraph["qas"]:
				total += 1
				if qa["id"] not in predictions:
					lowerCamelCase_							:		int							   =							'Unanswered question ' + qa['id'] + ' will receive score 0.'
					print(A						,   file=sys.stderr			)
					continue
				lowerCamelCase_							:		Dict							   =							[x['text'] for x in qa['answers']]
				lowerCamelCase_							:		str							   =							predictions[qa['id']]
				exact_match += metric_max_over_ground_truths(A						,   A						,   A			)
				fa += metric_max_over_ground_truths(A						,   A						,   A			)

	lowerCamelCase_							:		str							   =							100.0 * exact_match / total
	lowerCamelCase_							:		int							   =							100.0 * fa / total

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


if __name__ == "__main__":
							UpperCamelCase__              =						'1.1'
							UpperCamelCase__              =						argparse.ArgumentParser(description='Evaluation for SQuAD ' + expected_version)
							parser.add_argument('dataset_file', help='Dataset file')
							parser.add_argument('prediction_file', help='Prediction File')
							UpperCamelCase__              =						parser.parse_args()
							with open(args.dataset_file) as dataset_file:
														UpperCamelCase__              =						json.load(dataset_file)
														if dataset_json["version"] != expected_version:
																					print(
																					    'Evaluation expects v-' + expected_version + ', but got dataset with v-' + dataset_json['version'],
																					    file=sys.stderr,
																					)
														UpperCamelCase__              =						dataset_json['data']
							with open(args.prediction_file) as prediction_file:
														UpperCamelCase__              =						json.load(prediction_file)
							print(json.dumps(evaluate(dataset, predictions)))








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 ConvNextImageProcessor


class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     def __init__( self				:		Optional[Any]    ,       UpperCAmelCase_				:		Optional[int]    ,       UpperCAmelCase_				:		Any=7    ,       UpperCAmelCase_				:		Union[str, Any]=3    ,       UpperCAmelCase_				:		List[str]=18    ,       UpperCAmelCase_				:		Optional[int]=30    ,       UpperCAmelCase_				:		int=400    ,       UpperCAmelCase_				:		int=True    ,       UpperCAmelCase_				:		List[Any]=None    ,       UpperCAmelCase_				:		Tuple=0.875    ,       UpperCAmelCase_				:		str=True    ,       UpperCAmelCase_				:		Optional[Any]=[0.5, 0.5, 0.5]    ,       UpperCAmelCase_				:		List[Any]=[0.5, 0.5, 0.5]    ,       ):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			size if size is not None else {"shortest_edge": 20}
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			parent
       SCREAMING_SNAKE_CASE				:					int		  =			batch_size
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			num_channels
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			image_size
       SCREAMING_SNAKE_CASE				:					Any		  =			min_resolution
       SCREAMING_SNAKE_CASE				:					List[str]		  =			max_resolution
       SCREAMING_SNAKE_CASE				:					int		  =			do_resize
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size
       SCREAMING_SNAKE_CASE				:					Any		  =			crop_pct
       SCREAMING_SNAKE_CASE				:					Dict		  =			do_normalize
       SCREAMING_SNAKE_CASE				:					List[str]		  =			image_mean
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			image_std






     def  _A				( self				:		str					):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_pct": self.crop_pct,
       }





@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						str								=		ConvNextImageProcessor if is_vision_available() else None


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			ConvNextImageProcessingTester(self					)


     @property
     def  _A				( self				:		str					):
       return self.image_processor_tester.prepare_image_processor_dict()


     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_resize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "size"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "crop_pct"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_normalize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_mean"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_std"					)					)


     def  _A				( self				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 20}					)

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 42}					)


     def  _A				( self				:		List[str]					):
       pass


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       Image.Image					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					Dict		  =			image_processing(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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					str		  =			image_processing(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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_    ,       numpify=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       np.ndarray					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					str		  =			image_processing(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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Dict		  =			image_processing(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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )






     def  _A				( self				:		Tuple					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE				:					Tuple		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_    ,       torchify=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       torch.Tensor					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					int		  =			image_processing(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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Any		  =			image_processing(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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )






