




"""simple docstring"""







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





def 							lowercase   (snake_case__				:	int	)       ->					List[Any]:


				'''simple docstring'''






				def remove_articles(snake_case__				:	Optional[int]	):
								return re.sub(R"""\b(a|an|the)\b""" ,    """ """ ,    snake_case__	)

				def white_space_fix(snake_case__				:	str	):
								return " ".join(text.split()	)

				def remove_punc(snake_case__				:	Optional[int]	):
								lowerCAmelCase		=			set(string.punctuation	)
								return "".join(ch for ch in text if ch not in exclude	)

				def lower(snake_case__				:	Dict	):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(snake_case__	)	)	)	)





def 							lowercase   (snake_case__				:	Dict ,    snake_case__				:	Optional[Any]	)       ->					List[str]:


				'''simple docstring'''






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





def 							lowercase   (snake_case__				:	Tuple ,    snake_case__				:	List[Any]	)       ->					List[str]:


				'''simple docstring'''






				return normalize_answer(snake_case__	) == normalize_answer(snake_case__	)





def 							lowercase   (snake_case__				:	Union[str, Any] ,    snake_case__				:	List[str] ,    snake_case__				:	List[str]	)       ->					str:


				'''simple docstring'''






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





def 							lowercase   (snake_case__				:	Dict ,    snake_case__				:	Optional[Any]	)       ->					Dict:


				'''simple docstring'''






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

				lowerCAmelCase		=			100.0 * exact_match / total
				lowerCAmelCase		=			100.0 * fa / total

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


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











"""simple docstring"""







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  ['image_processor', 'tokenizer']
						_a        =  'VideoMAEImageProcessor'
						_a        =  ('CLIPTokenizer', 'CLIPTokenizerFast')


						def __init__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     Optional[Any]=None  ,  lowerCAmelCase			:     Tuple=None  ,  **lowerCAmelCase			:     List[str]   ):
										lowerCAmelCase		=			None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
														    """ instead."""  ,  lowerCAmelCase  ,  )
														lowerCAmelCase		=			kwargs.pop("""feature_extractor"""   )

										lowerCAmelCase		=			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("""You need to specify an `image_processor`."""   )
										if tokenizer is None:
														raise ValueError("""You need to specify a `tokenizer`."""   )

										super().__init__(lowerCAmelCase  ,  lowerCAmelCase   )
										lowerCAmelCase		=			self.image_processor


						def __call__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     str=None  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     int=None  ,  **lowerCAmelCase			:     str   ):

										if text is None and videos is None:
														raise ValueError("""You have to specify either text or videos. Both cannot be none."""   )

										if text is not None:
														lowerCAmelCase		=			self.tokenizer(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if videos is not None:
														lowerCAmelCase		=			self.image_processor(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if text is not None and videos is not None:
														lowerCAmelCase		=			image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**lowerCAmelCase   )  ,  tensor_type=lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  *lowerCAmelCase			:     List[str]  ,  **lowerCAmelCase			:     int   ):
										return self.tokenizer.batch_decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     List[str]  ,  *lowerCAmelCase			:     Any  ,  **lowerCAmelCase			:     List[Any]   ):
										return self.tokenizer.decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						@property
						def 					__lowercase	(							self			:     Dict   ):
										return ["input_ids", "attention_mask", "position_ids", "pixel_values"]


						@property
						def 					__lowercase	(							self			:     List[str]   ):
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,  lowerCAmelCase  ,  )
										return self.image_processor_class




						@property
						def 					__lowercase	(							self			:     Tuple   ):
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,  lowerCAmelCase  ,  )
										return self.image_processor





