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 					__snake_case    (     snake_case__       ,snake_case__       ,snake_case__      ):




					@register_to_config
					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase = False       ,			)	->    Dict:



									'''simple docstring'''







									super().__init__()

									snake_case__   :      int   				= nn.Linear(UpperCAmelCase_       ,			UpperCAmelCase_       ,			bias=UpperCAmelCase_						)

									snake_case__   :      str   				= nn.Embedding(UpperCAmelCase_       ,			UpperCAmelCase_						)
									snake_case__   :      str   				= False

									snake_case__   :      Optional[Any]   				= nn.Dropout(p=UpperCAmelCase_						)

									snake_case__   :      int   				= TaConfig(
									    d_model=UpperCAmelCase_       ,			num_heads=UpperCAmelCase_       ,			d_kv=UpperCAmelCase_       ,			d_ff=UpperCAmelCase_       ,			feed_forward_proj=UpperCAmelCase_       ,			dropout_rate=UpperCAmelCase_       ,			is_decoder=UpperCAmelCase_       ,			is_encoder_decoder=UpperCAmelCase_       ,			)
									snake_case__   :      Dict   				= nn.ModuleList()
									for lyr_num in range(UpperCAmelCase_						):
													snake_case__   :      Tuple   				= TaBlock(UpperCAmelCase_						)
													self.encoders.append(UpperCAmelCase_						)

									snake_case__   :      List[str]   				= TaLayerNorm(UpperCAmelCase_						)
									snake_case__   :      Optional[Any]   				= nn.Dropout(p=UpperCAmelCase_						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= self.input_proj(UpperCAmelCase_						)

									# terminal relative positional encodings
									snake_case__   :      Tuple   				= encoder_inputs.shape[1]
									snake_case__   :      Tuple   				= torch.arange(UpperCAmelCase_       ,			device=encoder_inputs.device						)

									snake_case__   :      Tuple   				= encoder_inputs_mask.sum(-1						)
									snake_case__   :      List[str]   				= torch.roll(input_positions.unsqueeze(0						)       ,			tuple(seq_lens.tolist()						)       ,			dims=0						)
									x += self.position_encoding(UpperCAmelCase_						)

									snake_case__   :      Tuple   				= self.dropout_pre(UpperCAmelCase_						)

									# inverted the attention mask
									snake_case__   :      int   				= encoder_inputs.size()
									snake_case__   :      Optional[Any]   				= self.get_extended_attention_mask(UpperCAmelCase_       ,			UpperCAmelCase_						)

									for lyr in self.encoders:
													snake_case__   :      int   				= lyr(UpperCAmelCase_       ,			UpperCAmelCase_						)[0]
									snake_case__   :      List[str]   				= self.layer_norm(UpperCAmelCase_						)

									return self.dropout_post(UpperCAmelCase_						), encoder_inputs_mask






"""simple docstring"""
import argparse
import json
import re
import string
import sys
from collections import Counter







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Any:

							def remove_articles(_SCREAMING_SNAKE_CASE					):
														return re.sub(r"""\b(a|an|the)\b"""					,			""" """					,			_SCREAMING_SNAKE_CASE					)

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

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

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

							return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE					)					)					)					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		normalize_answer(_SCREAMING_SNAKE_CASE					).split()
							snake_case_     							=		normalize_answer(_SCREAMING_SNAKE_CASE					).split()
							snake_case_     							=		Counter(_SCREAMING_SNAKE_CASE					) & Counter(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		sum(common.values()					)
							if num_same == 0:
														return 0
							snake_case_     							=		1.0 * num_same / len(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		1.0 * num_same / len(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		(2 * precision * recall) / (precision + recall)
							return fa







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							return normalize_answer(_SCREAMING_SNAKE_CASE					) == normalize_answer(_SCREAMING_SNAKE_CASE					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		[]
							for ground_truth in ground_truths:
														snake_case_     							=		metric_fn(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)
														scores_for_ground_truths.append(_SCREAMING_SNAKE_CASE					)
							return max(_SCREAMING_SNAKE_CASE					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		snake_case_     							=		snake_case_     							=		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_     							=		"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
																																			print(_SCREAMING_SNAKE_CASE					,			file=sys.stderr					)
																																			continue
																												snake_case_     							=		[x["""text"""] for x in qa["""answers"""]]
																												snake_case_     							=		predictions[qa["""id"""]]
																												exact_match += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)
																												fa += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)

							snake_case_     							=		100.0 * exact_match / total
							snake_case_     							=		100.0 * fa / total

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


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



