






"""simple docstring"""







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





def 						lowercase__(A						)				->Dict:




						"""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						):
												lowercase__				:      Union[str, Any]=       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 						lowercase__(A					,		A						)				->Optional[Any]:




						"""simple docstring"""



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





def 						lowercase__(A					,		A						)				->Any:




						"""simple docstring"""



						return normalize_answer(A						) == normalize_answer(A						)





def 						lowercase__(A					,		A					,		A						)				->List[Any]:




						"""simple docstring"""



						lowercase__				:      Optional[int]=       []
						for ground_truth in ground_truths:
												lowercase__				:      Any=       metric_fn(A					,		A						)
												scores_for_ground_truths.append(A						)
						return max(A						)





def 						lowercase__(A					,		A						)				->Optional[int]:




						"""simple docstring"""



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

						lowercase__				:      List[str]=       100.0 * exact_match / total
						lowercase__				:      str=       100.0 * fa / total

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


if __name__ == "__main__":
			a :				Any      					=		"""1.1"""
			a :				str      					=		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 :				Optional[Any]      					=		parser.parse_args()
			with open(args.dataset_file) as dataset_file:
						a :				Any      					=		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 :				Union[str, Any]      					=		dataset_json["""data"""]
			with open(args.prediction_file) as prediction_file:
						a :				Union[str, Any]      					=		json.load(prediction_file)
			print(json.dumps(evaluate(dataset, predictions)))














"""simple docstring"""







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





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




						"""simple docstring"""



						lowercase__				:      List[str]=       RoFormerConfig.from_json_file(A						)
						print(f'''Building PyTorch model from configuration: {config}'''						)
						lowercase__				:      Tuple=       RoFormerForMaskedLM(A						)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(A					,		A					,		A						)

						# Save pytorch-model
						print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
						torch.save(model.state_dict()					,		A					,		_use_new_zipfile_serialization=A						)


if __name__ == "__main__":
			a :				List[str]      					=		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
			)
			parser.add_argument(
			    """--bert_config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        """The config json file corresponding to the pre-trained BERT model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			a :				str      					=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






