
"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     [
					    'encoder.version',
					    'decoder.version',
					    'model.encoder.version',
					    'model.decoder.version',
					    '_float_tensor',
					    'decoder.output_projection.weight',
					]
					for k in ignore_keys:
										state_dict.pop(_A,					_A      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     emb.weight.shape
					SCREAMING_SNAKE_CASE           =     nn.Linear(_A,					_A,					bias=_A      )
					SCREAMING_SNAKE_CASE           =     emb.weight.data
					return lin_layer

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_="uclanlp/plbart-base",					SCREAMING_SNAKE_CASE_=False,					SCREAMING_SNAKE_CASE_=False      ):
					SCREAMING_SNAKE_CASE           =     torch.load(_A,					map_location='cpu'      )['model']
					remove_ignore_keys_(_A      )
					SCREAMING_SNAKE_CASE           =     state_dict['encoder.embed_tokens.weight'].shape[0]

					SCREAMING_SNAKE_CASE           =     PLBartConfig.from_pretrained(_A,					vocab_size=_A      )

					SCREAMING_SNAKE_CASE           =     state_dict['decoder.embed_tokens.weight']
					if not classification:
										SCREAMING_SNAKE_CASE           =     PLBartForConditionalGeneration(_A      )
										model.model.load_state_dict(_A      )
										if finetuned:
															SCREAMING_SNAKE_CASE           =     make_linear_from_emb(model.model.shared      )

					else:
										SCREAMING_SNAKE_CASE           =     {}
										for key, value in state_dict.copy().items():
															if key.startswith('classification_heads.sentence_classification_head'      ):
																				SCREAMING_SNAKE_CASE           =     value
																				state_dict.pop(_A      )
										SCREAMING_SNAKE_CASE           =     PLBartForSequenceClassification(_A      )
										model.model.load_state_dict(_A      )
										model.classification_head.load_state_dict(_A      )

					return model


if __name__ == "__main__":
	snake_case			       =		argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument(
	    '--hf_config',
	    default='uclanlp/plbart-base',
	    type=str,
	    help='Which huggingface architecture to use: plbart-base',
	)
	parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
	parser.add_argument(
	    '--classification', action='store_true', help='whether the model is a classification checkpoint'
	)
	snake_case			       =		parser.parse_args()
	snake_case			       =		convert_fairseq_plbart_checkpoint_from_disk(
	    args.fairseq_path,
	    hf_config_path=args.hf_config,
	    finetuned=args.finetuned,
	    classification=args.classification,
	)
	model.save_pretrained(args.pytorch_dump_folder_path)


"""simple docstring"""




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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):

					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_      ):
										SCREAMING_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   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
					SCREAMING_SNAKE_CASE           =     normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
					SCREAMING_SNAKE_CASE           =     Counter(SCREAMING_SNAKE_CASE_      ) & Counter(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     sum(common.values()      )
					if num_same == 0:
										return 0
					SCREAMING_SNAKE_CASE           =     1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     (2 * precision * recall) / (precision + recall)
					return fa

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					return normalize_answer(SCREAMING_SNAKE_CASE_      ) == normalize_answer(SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     []
					for ground_truth in ground_truths:
										SCREAMING_SNAKE_CASE           =     metric_fn(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
										scores_for_ground_truths.append(SCREAMING_SNAKE_CASE_      )
					return max(SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     SCREAMING_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:
																									SCREAMING_SNAKE_CASE           =     'Unanswered question ' + qa['id'] + ' will receive score 0.'
																									print(SCREAMING_SNAKE_CASE_,					file=sys.stderr      )
																									continue
																				SCREAMING_SNAKE_CASE           =     [x['text'] for x in qa['answers']]
																				SCREAMING_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_      )

					SCREAMING_SNAKE_CASE           =     100.0 * exact_match / total
					SCREAMING_SNAKE_CASE           =     100.0 * fa / total

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


if __name__ == "__main__":
	snake_case			       =		'1.1'
	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')
	snake_case			       =		parser.parse_args()
	with open(args.dataset_file) as dataset_file:
		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,
			)
		snake_case			       =		dataset_json['data']
	with open(args.prediction_file) as prediction_file:
		snake_case			       =		json.load(prediction_file)
	print(json.dumps(evaluate(dataset, predictions)))
