"""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 		_lowercase (						_SCREAMING_SNAKE_CASE					,					_SCREAMING_SNAKE_CASE					,					_SCREAMING_SNAKE_CASE      ):



						"""simple docstring"""




						@register_to_config
						def __init__(				self							:    str     , UpperCamelCase__							:    Tuple     , UpperCamelCase__							:    str     , UpperCamelCase__							:    int     , UpperCamelCase__							:    Union[str, Any]     , UpperCamelCase__							:    Optional[int]     , UpperCamelCase__							:    Union[str, Any]     , UpperCamelCase__							:    Optional[Any]     , UpperCamelCase__							:    List[Any]     , UpperCamelCase__							:    int     , UpperCamelCase__							:    Dict = False     , )				->				Optional[Any]:
													'''simple docstring'''



													super().__init__()

													__UpperCamelCase							=nn.Linear(__A     , __A     , bias=__A				)

													__UpperCamelCase							=nn.Embedding(__A     , __A				)
													__UpperCamelCase							=False

													__UpperCamelCase							=nn.Dropout(p=__A				)

													__UpperCamelCase							=TaConfig(
													    d_model=__A     , num_heads=__A     , d_kv=__A     , d_ff=__A     , feed_forward_proj=__A     , dropout_rate=__A     , is_decoder=__A     , is_encoder_decoder=__A     , )
													__UpperCamelCase							=nn.ModuleList()
													for lyr_num in range(__A				):
																				__UpperCamelCase							=TaBlock(__A				)
																				self.encoders.append(__A				)

													__UpperCamelCase							=TaLayerNorm(__A				)
													__UpperCamelCase							=nn.Dropout(p=__A				)





						def   UpperCAmelCase_ (				self							:    int     , UpperCamelCase__							:    List[Any]     , UpperCamelCase__							:    int				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=self.input_proj(__A				)

													# terminal relative positional encodings
													__UpperCamelCase							=encoder_inputs.shape[1]
													__UpperCamelCase							=torch.arange(__A     , device=encoder_inputs.device				)

													__UpperCamelCase							=encoder_inputs_mask.sum(-1				)
													__UpperCamelCase							=torch.roll(input_positions.unsqueeze(0				)     , tuple(seq_lens.tolist()				)     , dims=0				)
													x += self.position_encoding(__A				)

													__UpperCamelCase							=self.dropout_pre(__A				)

													# inverted the attention mask
													__UpperCamelCase							=encoder_inputs.size()
													__UpperCamelCase							=self.get_extended_attention_mask(__A     , __A				)

													for lyr in self.encoders:
																				__UpperCamelCase							=lyr(__A     , __A				)[0]
													__UpperCamelCase							=self.layer_norm(__A				)

													return self.dropout_post(__A				), encoder_inputs_mask







"""simple docstring"""







import argparse
import json
import re
import string
import sys
from collections import Counter
def _A		(		lowercase						):







    """simple docstring"""

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

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

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

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

    return white_space_fix(remove_articles(remove_punc(lower(lowercase						)						)						)						)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=normalize_answer(lowercase						).split()
    a											=normalize_answer(lowercase						).split()
    a											=Counter(lowercase						) & Counter(lowercase						)
    a											=sum(common.values()						)
    if num_same == 0:
        return 0
    a											=1.0 * num_same / len(lowercase						)
    a											=1.0 * num_same / len(lowercase						)
    a											=(2 * precision * recall) / (precision + recall)
    return fa
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    return normalize_answer(lowercase						) == normalize_answer(lowercase						)
def _A		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    a											=[]
    for ground_truth in ground_truths:
        a											=metric_fn(lowercase			,       lowercase						)
        scores_for_ground_truths.append(lowercase						)
    return max(lowercase						)
def _A		(		lowercase			,       lowercase						):







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

    a											=100.0 * exact_match / total
    a											=100.0 * fa / total

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


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