

'''simple docstring'''

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





def 				lowercase (					__magic_name__ :    str							):



					'''simple docstring'''







					def remove_articles(__magic_name__ :    str							):
										return re.sub(R"\b(a|an|the)\b"			,   " "			,   __magic_name__							)

					def white_space_fix(__magic_name__ :    Optional[Any]							):
										return " ".join(text.split()							)

					def remove_punc(__magic_name__ :    Tuple							):
										UpperCAmelCase		:							Any        =       set(string.punctuation							)
										return "".join(ch for ch in text if ch not in exclude							)

					def lower(__magic_name__ :    int							):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(__magic_name__							)							)							)							)





def 				lowercase (					__magic_name__ :    str			,   __magic_name__ :    Optional[Any]							):



					'''simple docstring'''







					UpperCAmelCase		:							Union[str, Any]        =       normalize_answer(__magic_name__							).split()
					UpperCAmelCase		:							Tuple        =       normalize_answer(__magic_name__							).split()
					UpperCAmelCase		:							Dict        =       Counter(__magic_name__							) & Counter(__magic_name__							)
					UpperCAmelCase		:							Tuple        =       sum(common.values()							)
					if num_same == 0:
										return 0
					UpperCAmelCase		:							List[Any]        =       1.0 * num_same / len(__magic_name__							)
					UpperCAmelCase		:							Dict        =       1.0 * num_same / len(__magic_name__							)
					UpperCAmelCase		:							List[Any]        =       (2 * precision * recall) / (precision + recall)
					return fa





def 				lowercase (					__magic_name__ :    Dict			,   __magic_name__ :    List[Any]							):



					'''simple docstring'''







					return normalize_answer(__magic_name__							) == normalize_answer(__magic_name__							)





def 				lowercase (					__magic_name__ :    Optional[int]			,   __magic_name__ :    Any			,   __magic_name__ :    int							):



					'''simple docstring'''







					UpperCAmelCase		:							Tuple        =       []
					for ground_truth in ground_truths:
										UpperCAmelCase		:							Dict        =       metric_fn(__magic_name__			,   __magic_name__							)
										scores_for_ground_truths.append(__magic_name__							)
					return max(__magic_name__							)





def 				lowercase (					__magic_name__ :    Dict			,   __magic_name__ :    Optional[int]							):



					'''simple docstring'''







					UpperCAmelCase		:							Optional[int]        =       0
					UpperCAmelCase		:							List[Any]        =       []
					for passage in dataset:
										for qa in passage["qas"]:
															total += 1
															if qa["id"] not in predictions:
																				UpperCAmelCase		:							List[str]        =       F"Unanswered question {qa['id']} will receive score 0."
																				print(__magic_name__			,   file=sys.stderr							)
																				continue

															UpperCAmelCase		:							Optional[int]        =       [x["text"] for x in qa["answers"]]
															UpperCAmelCase		:							List[str]        =       predictions[qa["id"]]

															UpperCAmelCase		:							str        =       metric_max_over_ground_truths(__magic_name__			,   __magic_name__			,   __magic_name__							)
															if int(_exact_match							) == 1:
																				correct_ids.append(qa["id"]							)
															exact_match += _exact_match

															fa += metric_max_over_ground_truths(__magic_name__			,   __magic_name__			,   __magic_name__							)

					UpperCAmelCase		:							Optional[int]        =       exact_match / total
					UpperCAmelCase		:							int        =       fa / total

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


if __name__ == "__main__":
				a       :      int  	=			"1.0"
				a       :      Optional[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.")
				a       :      Any  	=			parser.parse_args()

				with open(args.data_file) as data_file:
								a       :      str  	=			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,
												)
								a       :      str  	=			dataset_json["data"]

				with open(args.pred_file) as pred_file:
								a       :      Optional[Any]  	=			json.load(pred_file)

				a       :      List[str]  	=			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)



'''simple docstring'''

import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Any        =       filter(lambda __magic_name__							: p.requires_grad			,   model.parameters()							)
					UpperCAmelCase		:							Union[str, Any]        =       sum([np.prod(p.size()							) for p in model_parameters]							)
					return params


a       :      int  	=			logging.getLogger(__name__)







class   UpperCamelCase__   (       pl.Callback       ):


		"""simple docstring"""

		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Union[str, Any]        =       {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
							pl_module.logger.log_metrics(snake_case      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case=True      ):


							'''simple docstring'''

							logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"      )
							UpperCAmelCase		:							Optional[Any]        =       trainer.callback_metrics
							trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}      )
							# Log results
							UpperCAmelCase		:							List[str]        =       Path(pl_module.hparams.output_dir      )
							if type_path == "test":
												UpperCAmelCase		:							Dict        =       od / "test_results.txt"
												UpperCAmelCase		:							Dict        =       od / "test_generations.txt"
							else:
												# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
												# If people want this it will be easy enough to add back.
												UpperCAmelCase		:							Union[str, Any]        =       od / f"{type_path}_results/{trainer.global_step:05d}.txt"
												UpperCAmelCase		:							int        =       od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
												results_file.parent.mkdir(exist_ok=snake_case      )
												generations_file.parent.mkdir(exist_ok=snake_case      )
							with open(snake_case   ,      "a+"      ) as writer:
												for key in sorted(snake_case      ):
																	if key in ["log", "progress_bar", "preds"]:
																						continue
																	UpperCAmelCase		:							Optional[Any]        =       metrics[key]
																	if isinstance(snake_case   ,      torch.Tensor      ):
																						UpperCAmelCase		:							Union[str, Any]        =       val.item()
																	UpperCAmelCase		:							List[str]        =       f"{key}: {val:.6f}\n"
																	writer.write(snake_case      )

							if not save_generations:
												return

							if "preds" in metrics:
												UpperCAmelCase		:							List[str]        =       "\n".join(metrics["preds"]      )
												generations_file.open("w+"      ).write(snake_case      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							try:
												UpperCAmelCase		:							Tuple        =       pl_module.model.model.num_parameters()
							except AttributeError:
												UpperCAmelCase		:							int        =       pl_module.model.num_parameters()

							UpperCAmelCase		:							List[Any]        =       count_trainable_parameters(snake_case      )
							# mp stands for million parameters
							trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							save_json(pl_module.metrics   ,      pl_module.metrics_save_path      )
							return self._write_logs(snake_case   ,      snake_case   ,      "test"      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							save_json(pl_module.metrics   ,      pl_module.metrics_save_path      )
							# Uncommenting this will save val generations
							# return self._write_logs(trainer, pl_module, "valid")





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__=1			,   __magic_name__=False							):



					'''simple docstring'''







					if metric == "rouge2":
										UpperCAmelCase		:							Dict        =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										UpperCAmelCase		:							int        =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "loss":
										UpperCAmelCase		:							Tuple        =       "{val_avg_loss:.4f}-{step_count}"
					else:
										raise NotImplementedError(
										    F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
										    " this function."							)

					UpperCAmelCase		:							int        =       ModelCheckpoint(
					    dirpath=__magic_name__			,   filename=__magic_name__			,   monitor=F"val_{metric}"			,   mode="min" if "loss" in metric else "max"			,   save_top_k=__magic_name__			,   )
					return checkpoint_callback





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					return EarlyStopping(
					    monitor=F"val_{metric}"			,   mode="min" if "loss" in metric else "max"			,   patience=__magic_name__			,   verbose=__magic_name__			,   )
