





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


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

  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] ):
    _snake_case      : List[str]														=		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 			lowerCamelCase_		(					lowerCAmelCase:		int			,							lowerCAmelCase:		List[str] )->						Tuple:
  _snake_case      : Dict														=		normalize_answer(lowerCAmelCase ).split()
  _snake_case      : Optional[int]														=		normalize_answer(lowerCAmelCase ).split()
  _snake_case      : Optional[Any]														=		Counter(lowerCAmelCase ) & Counter(lowerCAmelCase )
  _snake_case      : Tuple														=		sum(common.values() )
  if num_same == 0:
    return 0
  _snake_case      : Dict														=		1.0 * num_same / len(lowerCAmelCase )
  _snake_case      : List[str]														=		1.0 * num_same / len(lowerCAmelCase )
  _snake_case      : List[Any]														=		(2 * precision * recall) / (precision + recall)
  return fa


def 			lowerCamelCase_		(					lowerCAmelCase:		Optional[int]			,							lowerCAmelCase:		List[str] )->						Tuple:
  return normalize_answer(lowerCAmelCase ) == normalize_answer(lowerCAmelCase )


def 			lowerCamelCase_		(					lowerCAmelCase:		int			,							lowerCAmelCase:		Union[str, Any]			,							lowerCAmelCase:		Optional[Any] )->						Dict:
  _snake_case      : Union[str, Any]														=		[]
  for ground_truth in ground_truths:
    _snake_case      : List[str]														=		metric_fn(lowerCAmelCase			,							lowerCAmelCase )
    scores_for_ground_truths.append(lowerCAmelCase )
  return max(lowerCAmelCase )


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

  _snake_case      : List[Any]														=		1_0_0.0 * exact_match / total
  _snake_case      : Optional[Any]														=		1_0_0.0 * fa / total

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


if __name__ == "__main__":
      lowerCAmelCase_        =			"""1.1"""
      lowerCAmelCase_        =			argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
      parser.add_argument("""dataset_file""", help="""Dataset file""")
      parser.add_argument("""prediction_file""", help="""Prediction File""")
      lowerCAmelCase_        =			parser.parse_args()
      with open(args.dataset_file) as dataset_file:
            lowerCAmelCase_        =			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,
                  )
            lowerCAmelCase_        =			dataset_json["""data"""]
      with open(args.prediction_file) as prediction_file:
            lowerCAmelCase_        =			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)




