



import warnings

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





class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							['''image_processor''', '''tokenizer''']
				__lowerCamelCase					=							'''AutoImageProcessor'''
				__lowerCamelCase					=							'''AutoTokenizer'''




				def __init__(				self		,							_snake_case=None		,							_snake_case=None		,							**_snake_case						):



									"""simple docstring"""





									_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."""		,							_snake_case		,							)
														_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 have to specify an image_processor."""						)
									if tokenizer is None:
														raise ValueError("""You have to specify a tokenizer."""						)

									super().__init__(_snake_case		,							_snake_case						)
									_lowerCAmelCase         =       self.image_processor




				def __call__(				self		,							_snake_case=None		,							_snake_case=None		,							_snake_case=None		,							**_snake_case						):



									"""simple docstring"""





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

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

									if images is not None:
														_lowerCAmelCase         =       self.image_processor(_snake_case		,							return_tensors=_snake_case		,							**_snake_case						)

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




				def 				snake_case  (				self		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





									return self.tokenizer.batch_decode(*_snake_case		,							**_snake_case						)




				def 				snake_case  (				self		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





									return self.tokenizer.decode(*_snake_case		,							**_snake_case						)




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.tokenizer.model_input_names
									_lowerCAmelCase         =       self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,							_snake_case		,							)
									return self.image_processor_class




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,							_snake_case		,							)
									return self.image_processor








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


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

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

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

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

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

					return white_space_fix(remove_articles(remove_punc(lower(snake_case     )     )     )     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""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 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					return normalize_answer(snake_case     ) == normalize_answer(snake_case     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case     ):





					"""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 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""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)))



