
"""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     lowerCAmelCase__ (		__magic_name__			)	->List[Any]:
			__lowercase						      =					filter(lambda __magic_name__			: p.requires_grad ,      model.parameters()			)
			__lowercase						      =					sum([np.prod(p.size()			) for p in model_parameters]			)
			return params


_lowercase							 =							logging.getLogger(__name__)

class 				__a    (   pl.Callback					):
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					{f'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
										pl_module.logger.log_metrics(_lowerCAmelCase					)
							@rank_zero_only
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=True					) ->       Any:




										'''simple docstring'''
										logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''					)
										__lowercase						      =					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
										__lowercase						      =					Path(pl_module.hparams.output_dir					)
										if type_path == "test":
													__lowercase						      =					od / """test_results.txt"""
													__lowercase						      =					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.
													__lowercase						      =					od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
													__lowercase						      =					od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
													results_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
													generations_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
										with open(_lowerCAmelCase     ,						"a+"					) as writer:
													for key in sorted(_lowerCAmelCase					):
																if key in ["log", "progress_bar", "preds"]:
																			continue
																__lowercase						      =					metrics[key]
																if isinstance(_lowerCAmelCase     ,						torch.Tensor					):
																			__lowercase						      =					val.item()
																__lowercase						      =					f'''{key}: {val:.6f}\n'''
																writer.write(_lowerCAmelCase					)

										if not save_generations:
													return

										if "preds" in metrics:
													__lowercase						      =					"""\n""".join(metrics["preds"]					)
													generations_file.open("w+"					).write(_lowerCAmelCase					)
							@rank_zero_only
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       List[str]:




										'''simple docstring'''
										try:
													__lowercase						      =					pl_module.model.model.num_parameters()
										except AttributeError:
													__lowercase						      =					pl_module.model.num_parameters()

										__lowercase						      =					count_trainable_parameters(_lowerCAmelCase					)
										# 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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       List[str]:




										'''simple docstring'''
										save_json(pl_module.metrics     ,						pl_module.metrics_save_path					)
										return self._write_logs(_lowerCAmelCase     ,						_lowerCAmelCase     ,						"test"					)





							@rank_zero_only
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       List[Any]:




										'''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     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__=1 ,      __magic_name__=False			)	->Tuple:
			if metric == "rouge2":
						__lowercase						      =					"""{val_avg_rouge2:.4f}-{step_count}"""
			elif metric == "bleu":
						__lowercase						      =					"""{val_avg_bleu:.4f}-{step_count}"""
			elif metric == "loss":
						__lowercase						      =					"""{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."			)

			__lowercase						      =					ModelCheckpoint(
			    dirpath=__lowerCamelCase ,      filename=__lowerCamelCase ,      monitor=F'''val_{metric}''' ,      mode="min" if "loss" in metric else "max" ,      save_top_k=__lowerCamelCase ,      )
			return checkpoint_callback







def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->Tuple:
			return EarlyStopping(
			    monitor=F'''val_{metric}''' ,      mode="min" if "loss" in metric else "max" ,      patience=__lowerCamelCase ,      verbose=__lowerCamelCase ,      )







"""simple docstring"""






import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


_lowercase							 =							'''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

_lowercase							 =							'''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

_lowercase							 =							'''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,      _KWARGS_DESCRIPTION					)
class 				__a    (   datasets.Metric					):



							'''simple docstring'''







							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,						citation=_CITATION     ,						inputs_description=_KWARGS_DESCRIPTION     ,						features=datasets.Features(
										        {
										            "predictions": {
										                "id": datasets.Value("string"					),
										                "prediction_text": datasets.Value("string"					),
										                "no_answer_probability": datasets.Value("float32"					),
										            },
										            "references": {
										                "id": datasets.Value("string"					),
										                "answers": datasets.features.Sequence(
										                    {"text": datasets.Value("string"					), "answer_start": datasets.Value("int32"					)}					),
										            },
										        }					)     ,						codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]     ,						reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]     ,						)





							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=1.0					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					{p["id"]: p["no_answer_probability"] for p in predictions}
										__lowercase						      =					[{"paragraphs": [{"qas": references}]}]
										__lowercase						      =					{p["id"]: p["prediction_text"] for p in predictions}

										__lowercase						      =					make_qid_to_has_ans(_lowerCamelCase					)  # maps qid to True/False
										__lowercase						      =					[k for k, v in qid_to_has_ans.items() if v]
										__lowercase						      =					[k for k, v in qid_to_has_ans.items() if not v]

										__lowercase      ,				__lowercase						      =					get_raw_scores(_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase					)

										if has_ans_qids:
													__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase     ,						qid_list=_lowerCamelCase					)
													merge_eval(_lowerCamelCase     ,						_lowerCamelCase     ,						"HasAns"					)
										if no_ans_qids:
													__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase     ,						qid_list=_lowerCamelCase					)
													merge_eval(_lowerCamelCase     ,						_lowerCamelCase     ,						"NoAns"					)
										find_all_best_thresh(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										return dict(_lowerCamelCase					)





