
"""simple docstring"""

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




def 			lowercase__  (    lowerCAmelCase :    List[str]       )       ->					Optional[Any]:






    """simple docstring"""




    def remove_articles(lowerCAmelCase :    Dict       ):
        return re.sub(r'\b(a|an|the)\b'       , ' '       , lowerCAmelCase       )

    def white_space_fix(lowerCAmelCase :    Any       ):
        return " ".join(text.split()       )

    def remove_punc(lowerCAmelCase :    List[Any]       ):
        UpperCAmelCase  	=    set(string.punctuation       )
        return "".join(ch for ch in text if ch not in exclude       )

    def lower(lowerCAmelCase :    Union[str, Any]       ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase       )       )       )       )




def 			lowercase__  (    lowerCAmelCase :    int       , lowerCAmelCase :    List[str]       )       ->					Tuple:






    """simple docstring"""




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




def 			lowercase__  (    lowerCAmelCase :    Optional[int]       , lowerCAmelCase :    List[str]       )       ->					Tuple:






    """simple docstring"""




    return normalize_answer(lowerCAmelCase       ) == normalize_answer(lowerCAmelCase       )




def 			lowercase__  (    lowerCAmelCase :    int       , lowerCAmelCase :    Union[str, Any]       , lowerCAmelCase :    Optional[Any]       )       ->					Dict:






    """simple docstring"""




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




def 			lowercase__  (    lowerCAmelCase :    List[str]       , lowerCAmelCase :    Tuple       )       ->					Any:






    """simple docstring"""




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

    UpperCAmelCase  	=    100.0 * exact_match / total
    UpperCAmelCase  	=    100.0 * fa / total

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


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










import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 			lowerCamelCase_		(					lowerCAmelCase:		Any )->						str:
  if "model" in orig_key:
    _snake_case      : Tuple														=		orig_key.replace('model.'			,							'' )
  if "norm1" in orig_key:
    _snake_case      : List[str]														=		orig_key.replace('norm1'			,							'attention.output.LayerNorm' )
  if "norm2" in orig_key:
    _snake_case      : Tuple														=		orig_key.replace('norm2'			,							'output.LayerNorm' )
  if "norm" in orig_key:
    _snake_case      : Any														=		orig_key.replace('norm'			,							'LayerNorm' )
  if "transformer" in orig_key:
    _snake_case      : Tuple														=		orig_key.split('.' )[0].split('_' )[-1]
    _snake_case      : List[Any]														=		orig_key.replace(F"""transformer_{layer_num}"""			,							F"""encoder.layer.{layer_num}""" )
  if "mha.attn" in orig_key:
    _snake_case      : Union[str, Any]														=		orig_key.replace('mha.attn'			,							'attention.self' )
  if "mha" in orig_key:
    _snake_case      : Dict														=		orig_key.replace('mha'			,							'attention' )
  if "W_q" in orig_key:
    _snake_case      : Optional[Any]														=		orig_key.replace('W_q'			,							'self.query' )
  if "W_k" in orig_key:
    _snake_case      : Union[str, Any]														=		orig_key.replace('W_k'			,							'self.key' )
  if "W_v" in orig_key:
    _snake_case      : Tuple														=		orig_key.replace('W_v'			,							'self.value' )
  if "ff.0" in orig_key:
    _snake_case      : str														=		orig_key.replace('ff.0'			,							'intermediate.dense' )
  if "ff.2" in orig_key:
    _snake_case      : int														=		orig_key.replace('ff.2'			,							'output.dense' )
  if "ff" in orig_key:
    _snake_case      : Tuple														=		orig_key.replace('ff'			,							'output.dense' )
  if "mlm_class" in orig_key:
    _snake_case      : str														=		orig_key.replace('mlm.mlm_class'			,							'cls.predictions.decoder' )
  if "mlm" in orig_key:
    _snake_case      : Dict														=		orig_key.replace('mlm'			,							'cls.predictions.transform' )
  if "backbone.backbone.encoders" in orig_key:
    _snake_case      : Any														=		orig_key.replace('backbone.backbone.encoders'			,							'encoder.layer' )
  if "cls" not in orig_key:
    _snake_case      : Dict														=		'mra.' + orig_key

  return orig_key


def 			lowerCamelCase_		(					lowerCAmelCase:		List[Any]			,							lowerCAmelCase:		Union[str, Any] )->						Any:
  for key in orig_state_dict.copy().keys():
    _snake_case      : Dict														=		orig_state_dict.pop(lowerCAmelCase )

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      _snake_case      : str														=		val

  _snake_case      : Tuple														=		orig_state_dict['cls.predictions.decoder.bias']
  _snake_case      : List[Any]														=		torch.arange(lowerCAmelCase ).expand((1, -1) ) + 2

  return orig_state_dict


def 			lowerCamelCase_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		List[str]			,							lowerCAmelCase:		int )->						List[str]:
  _snake_case      : Optional[Any]														=		torch.load(lowerCAmelCase			,							map_location='cpu' )['model_state_dict']
  _snake_case      : Optional[Any]														=		MraConfig.from_json_file(lowerCAmelCase )
  _snake_case      : Any														=		MraForMaskedLM(lowerCAmelCase )

  _snake_case      : List[Any]														=		convert_checkpoint_helper(config.max_position_embeddings			,							lowerCAmelCase )

  print(model.load_state_dict(lowerCAmelCase ) )
  model.eval()
  model.save_pretrained(lowerCAmelCase )

  print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
      lowerCAmelCase_        =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help="""The json file for Mra model config.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      lowerCAmelCase_        =			parser.parse_args()
      convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




