code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import torch
  import torch.nn as nn
  from transformers.modeling_utils import ModuleUtilsMixin
  from transformers.models.ta.modeling_ta import (
      TaBlock,
      TaConfig,
      TaLayerNorm,
  )
  
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...models import ModelMixin
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       a__ ,    a__ ,    a__		):
  
  
  
        @register_to_config
        def __init__(  self  :			Any  ,			A  :			str  ,			A  :			Tuple  ,			A  :			str  ,			A  :			List[Any]  ,			A  :			Any  ,			A  :			Optional[int]  ,			A  :			Any  ,			A  :			int  ,			A  :			Dict  ,			A  :			Tuple = False  ,			):
             super().__init__()
  
             _UpperCAmelCase    :			Optional[Any]              =					nn.Linear(SCREAMING_SNAKE_CASE_  ,			SCREAMING_SNAKE_CASE_  ,			bias=SCREAMING_SNAKE_CASE_		)
  
             _UpperCAmelCase    :			Optional[Any]              =					nn.Embedding(SCREAMING_SNAKE_CASE_  ,			SCREAMING_SNAKE_CASE_		)
             _UpperCAmelCase    :			Any              =					False
  
             _UpperCAmelCase    :			str              =					nn.Dropout(p=SCREAMING_SNAKE_CASE_		)
  
             _UpperCAmelCase    :			Optional[Any]              =					TaConfig(
                 d_model=SCREAMING_SNAKE_CASE_  ,			num_heads=SCREAMING_SNAKE_CASE_  ,			d_kv=SCREAMING_SNAKE_CASE_  ,			d_ff=SCREAMING_SNAKE_CASE_  ,			feed_forward_proj=SCREAMING_SNAKE_CASE_  ,			dropout_rate=SCREAMING_SNAKE_CASE_  ,			is_decoder=SCREAMING_SNAKE_CASE_  ,			is_encoder_decoder=SCREAMING_SNAKE_CASE_  ,			)
             _UpperCAmelCase    :			List[Any]              =					nn.ModuleList()
             for lyr_num in range(SCREAMING_SNAKE_CASE_		):
                  _UpperCAmelCase    :			Tuple              =					TaBlock(SCREAMING_SNAKE_CASE_		)
                  self.encoders.append(SCREAMING_SNAKE_CASE_		)
  
             _UpperCAmelCase    :			int              =					TaLayerNorm(SCREAMING_SNAKE_CASE_		)
             _UpperCAmelCase    :			Dict              =					nn.Dropout(p=SCREAMING_SNAKE_CASE_		)
  
  
  
        def 		snake_case_      (  self  :			Optional[int]  ,			A  :			Dict  ,			A  :			Dict		):
             _UpperCAmelCase    :			str              =					self.input_proj(SCREAMING_SNAKE_CASE_		)
  
             # terminal relative positional encodings
             _UpperCAmelCase    :			int              =					encoder_inputs.shape[1]
             _UpperCAmelCase    :			Optional[Any]              =					torch.arange(SCREAMING_SNAKE_CASE_  ,			device=encoder_inputs.device		)
  
             _UpperCAmelCase    :			Tuple              =					encoder_inputs_mask.sum(-1		)
             _UpperCAmelCase    :			Any              =					torch.roll(input_positions.unsqueeze(0		)  ,			tuple(seq_lens.tolist()		)  ,			dims=0		)
             x += self.position_encoding(SCREAMING_SNAKE_CASE_		)
  
             _UpperCAmelCase    :			Any              =					self.dropout_pre(SCREAMING_SNAKE_CASE_		)
  
             # inverted the attention mask
             _UpperCAmelCase    :			Optional[int]              =					encoder_inputs.size()
             _UpperCAmelCase    :			List[Any]              =					self.get_extended_attention_mask(SCREAMING_SNAKE_CASE_  ,			SCREAMING_SNAKE_CASE_		)
  
             for lyr in self.encoders:
                  _UpperCAmelCase    :			List[Any]              =					lyr(SCREAMING_SNAKE_CASE_  ,			SCREAMING_SNAKE_CASE_		)[0]
             _UpperCAmelCase    :			Any              =					self.layer_norm(SCREAMING_SNAKE_CASE_		)
  
             return self.dropout_post(SCREAMING_SNAKE_CASE_		), encoder_inputs_mask
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       def remove_articles(SCREAMING_SNAKE_CASE__			:		Tuple       ):
            return re.sub(R"\b(a|an|the)\b"		,						" "		,						SCREAMING_SNAKE_CASE__       )
  
       def white_space_fix(SCREAMING_SNAKE_CASE__			:		Any       ):
            return " ".join(text.split()       )
  
       def remove_punc(SCREAMING_SNAKE_CASE__			:		Optional[int]       ):
            _UpperCAmelCase    :			List[str]              =					set(string.punctuation       )
            return "".join(ch for ch in text if ch not in exclude       )
  
       def lower(SCREAMING_SNAKE_CASE__			:		List[str]       ):
            return text.lower()
  
       return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__       )       )       )       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		str       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			List[Any]              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
       _UpperCAmelCase    :			Tuple              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
       _UpperCAmelCase    :			List[Any]              =					Counter(SCREAMING_SNAKE_CASE__       ) & Counter(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			List[str]              =					sum(common.values()       )
       if num_same == 0:
            return 0
       _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Union[str, Any]              =					(2 * precision * recall) / (precision + recall)
       return fa
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		Dict       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       return normalize_answer(SCREAMING_SNAKE_CASE__       ) == normalize_answer(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			int              =					[]
       for ground_truth in ground_truths:
            _UpperCAmelCase    :			Dict              =					metric_fn(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__       )
       return max(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Tuple:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			str              =					0
       _UpperCAmelCase    :			int              =					[]
       for passage in dataset:
            for qa in passage["qas"]:
                 total += 1
                 if qa["id"] not in predictions:
                      _UpperCAmelCase    :			List[Any]              =					f'Unanswered question {qa["id"]} will receive score 0.'
                      print(SCREAMING_SNAKE_CASE__		,						file=sys.stderr       )
                      continue
  
                 _UpperCAmelCase    :			Union[str, Any]              =					[x["text"] for x in qa["answers"]]
                 _UpperCAmelCase    :			Optional[int]              =					predictions[qa["id"]]
  
                 _UpperCAmelCase    :			int              =					metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
                 if int(_exact_match       ) == 1:
                      correct_ids.append(qa["id"]       )
                 exact_match += _exact_match
  
                 fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
       _UpperCAmelCase    :			List[str]              =					exact_match / total
       _UpperCAmelCase    :			str              =					fa / total
  
       return {"exact_match": exact_match, "f1": fa}, correct_ids
  
  
  if __name__ == "__main__":
   _lowerCAmelCase       :      Any							=							"1.0"
   _lowerCAmelCase       :      Any							=							argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
   parser.add_argument("data_file", help="The dataset file in JSON format.")
   parser.add_argument("pred_file", help="The model prediction file in JSON format.")
   parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
   _lowerCAmelCase       :      Tuple							=							parser.parse_args()
  
   with open(args.data_file) as data_file:
    _lowerCAmelCase       :      int							=							json.load(data_file)
    if dataset_json["version"] != expected_version:
     print(
         F"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
         file=sys.stderr,
     )
    _lowerCAmelCase       :      List[str]							=							dataset_json["data"]
  
   with open(args.pred_file) as pred_file:
    _lowerCAmelCase       :      str							=							json.load(pred_file)
  
   _lowerCAmelCase,		_lowerCAmelCase       :      List[Any]							=							evaluate(dataset, predictions)
  
   if args.output_correct_ids:
    print(F"Output {len(correct_ids)} correctly answered question IDs.")
    with open("correct_ids.json", "w") as f:
     json.dump(correct_ids, f)
  