






import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__snake_case      :      Tuple             =       """sshleifer/mar_enro_6_3_student"""


class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().setUp()

												lowerCAmelCase__        =  cached_path(
												    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'       ,  extract_compressed_file=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"






						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												MarianMTModel.from_pretrained(_UpperCamelCase				)







						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  {
												    '$MAX_LEN': 64,
												    '$BS': 64,
												    '$GAS': 1,
												    '$ENRO_DIR': self.data_dir,
												    'facebook/mbart-large-cc25': MARIAN_MODEL,
												    # "val_check_interval=0.25": "val_check_interval=1.0",
												    '--learning_rate=3e-5': '--learning_rate 3e-4',
												    '--num_train_epochs 6': '--num_train_epochs 1',
												}

												# Clean up bash script
												lowerCAmelCase__        =  (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'				)[1].strip()
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()

												# bash_script = bash_script.replace("--fp16 ", "")
												lowerCAmelCase__        =  F"\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        ".split()
												# XXX: args.gpus > 1 : handle multi_gpu in the future

												lowerCAmelCase__        =  ['finetune.py'] + bash_script.split() + args
												with patch.object(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationModule.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		lowerCAmelCase__        =  main(_UpperCamelCase				)

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  metrics['val'][-1]
												self.assertEqual(len(metrics['val']				)       ,  (args.max_epochs / args.val_check_interval)				)
												assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]       ,  _UpperCamelCase				)

												self.assertGreater(last_step_stats['val_avg_gen_time']       ,  0.01				)
												# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
												self.assertLessEqual(last_step_stats['val_avg_gen_time']       ,  1.0				)

												# test learning requirements:

												# 1. BLEU improves over the course of training by more than 2 pts
												self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu']       ,  2				)

												# 2. BLEU finishes above 17
												self.assertGreater(last_step_stats['val_avg_bleu']       ,  17				)

												# 3. test BLEU and val BLEU within ~1.1 pt.
												self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu']				)       ,  1.1				)

												# check lightning ckpt can be loaded and has a reasonable statedict
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
												assert expected_key in ckpt["state_dict"]
												assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

												# TODO: turn on args.do_predict when PL bug fixed.
												if args.do_predict:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) for p in contents}
																		assert "test_generations.txt" in contents
																		assert "test_results.txt" in contents
																		# assert len(metrics["val"]) ==  desired_n_evals
																		assert len(metrics['test']				) == 1




class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						@timeout_decorator.timeout(6_00				)
						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
												lowerCAmelCase__        =  {
												    '--fp16_opt_level=O1': '',
												    '$MAX_LEN': 1_28,
												    '$BS': 16,
												    '$GAS': 1,
												    '$ENRO_DIR': data_dir,
												    '$m': 'sshleifer/student_marian_en_ro_6_1',
												    'val_check_interval=0.25': 'val_check_interval=1.0',
												}

												# Clean up bash script
												lowerCAmelCase__        =  (
												    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'				)[1].strip()
												)
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												lowerCAmelCase__        =  bash_script.replace('--fp16 '       ,  ' '				)

												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()
												lowerCAmelCase__        =  bash_script.replace('--fp16'       ,  ''				)
												lowerCAmelCase__        =  6
												lowerCAmelCase__        =  (
												    ['distillation.py']
												    + bash_script.split()
												    + [
												        F"--output_dir={output_dir}",
												        '--gpus=1',
												        '--learning_rate=1e-3',
												        F"--num_train_epochs={epochs}",
												        '--warmup_steps=10',
												        '--val_check_interval=1.0',
												        '--do_predict',
												    ]
												)
												with patch.object(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationDistiller.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		lowerCAmelCase__        =  distill_main(_UpperCamelCase				)

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  metrics['val'][-1]
												assert len(metrics['val']				) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

												assert last_step_stats["val_avg_gen_time"] >= 0.01

												assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
												assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
												assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]       ,  _UpperCamelCase				)

												# check lightning ckpt can be loaded and has a reasonable statedict
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
												assert expected_key in ckpt["state_dict"]
												assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

												# TODO: turn on args.do_predict when PL bug fixed.
												if args.do_predict:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) for p in contents}
																		assert "test_generations.txt" in contents
																		assert "test_results.txt" in contents
																		# assert len(metrics["val"]) ==  desired_n_evals
																		assert len(metrics['test']				) == 1













import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


__snake_case      :      Any             =       re.compile(R"""\b(a|an|the)\b""", re.UNICODE)

__snake_case      :      List[Any]             =       None







def 		_UpperCamelCase				(      )					->				Tuple:

						"""simple docstring"""



						lowerCAmelCase__        =  argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.'						)
						parser.add_argument('data_file'      ,    metavar='data.json'      ,    help='Input data JSON file.'						)
						parser.add_argument('pred_file'      ,    metavar='pred.json'      ,    help='Model predictions.'						)
						parser.add_argument(
						    '--out-file'      ,    '-o'      ,    metavar='eval.json'      ,    help='Write accuracy metrics to file (default is stdout).'						)
						parser.add_argument(
						    '--na-prob-file'      ,    '-n'      ,    metavar='na_prob.json'      ,    help='Model estimates of probability of no answer.'						)
						parser.add_argument(
						    '--na-prob-thresh'      ,    '-t'      ,    type=UpperCamelCase_      ,    default=1.0      ,    help='Predict "" if no-answer probability exceeds this (default = 1.0).'      ,    )
						parser.add_argument(
						    '--out-image-dir'      ,    '-p'      ,    metavar='out_images'      ,    default=UpperCamelCase_      ,    help='Save precision-recall curves to directory.'						)
						parser.add_argument('--verbose'      ,    '-v'      ,    action='store_true'						)
						if len(sys.argv						) == 1:
												parser.print_help()
												sys.exit(1						)
						return parser.parse_args()







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict						)					->				List[str]:

						"""simple docstring"""



						lowerCAmelCase__        =  {}
						for article in dataset:
												for p in article["paragraphs"]:
																		for qa in p["qas"]:
																								lowerCAmelCase__        =  bool(qa['answers']['text']						)
						return qid_to_has_ans







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]						)					->				Any:

						"""simple docstring"""



						def remove_articles(UpperCamelCase_    :   Optional[int]						):
												return ARTICLES_REGEX.sub(' '      ,    UpperCamelCase_						)

						def white_space_fix(UpperCamelCase_    :   Any						):
												return " ".join(text.split()						)

						def remove_punc(UpperCamelCase_    :   Union[str, Any]						):
												lowerCAmelCase__        =  set(string.punctuation						)
												return "".join(ch for ch in text if ch not in exclude						)

						def lower(UpperCamelCase_    :   str						):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_						)						)						)						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]						)					->				Union[str, Any]:

						"""simple docstring"""



						if not s:
												return []
						return normalize_answer(UpperCamelCase_						).split()







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   List[str]						)					->				Tuple:

						"""simple docstring"""



						return int(normalize_answer(UpperCamelCase_						) == normalize_answer(UpperCamelCase_						)						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   List[Any]						)					->				Tuple:

						"""simple docstring"""



						lowerCAmelCase__        =  get_tokens(UpperCamelCase_						)
						lowerCAmelCase__        =  get_tokens(UpperCamelCase_						)
						lowerCAmelCase__        =  collections.Counter(UpperCamelCase_						) & collections.Counter(UpperCamelCase_						)
						lowerCAmelCase__        =  sum(common.values()						)
						if len(UpperCamelCase_						) == 0 or len(UpperCamelCase_						) == 0:
												# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
												return int(gold_toks == pred_toks						)
						if num_same == 0:
												return 0
						lowerCAmelCase__        =  1.0 * num_same / len(UpperCamelCase_						)
						lowerCAmelCase__        =  1.0 * num_same / len(UpperCamelCase_						)
						lowerCAmelCase__        =  (2 * precision * recall) / (precision + recall)
						return fa







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Optional[int]						)					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  {}
						lowerCAmelCase__        =  {}
						for article in dataset:
												for p in article["paragraphs"]:
																		for qa in p["qas"]:
																								lowerCAmelCase__        =  qa['id']
																								lowerCAmelCase__        =  [t for t in qa['answers']['text'] if normalize_answer(UpperCamelCase_						)]
																								if not gold_answers:
																														# For unanswerable questions, only correct answer is empty string
																														lowerCAmelCase__        =  ['']
																								if qid not in preds:
																														print(F"Missing prediction for {qid}"						)
																														continue
																								lowerCAmelCase__        =  preds[qid]
																								# Take max over all gold answers
																								lowerCAmelCase__        =  max(compute_exact(UpperCamelCase_      ,    UpperCamelCase_						) for a in gold_answers						)
																								lowerCAmelCase__        =  max(compute_fa(UpperCamelCase_      ,    UpperCamelCase_						) for a in gold_answers						)
						return exact_scores, fa_scores







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   List[Any]						)					->				str:

						"""simple docstring"""



						lowerCAmelCase__        =  {}
						for qid, s in scores.items():
												lowerCAmelCase__        =  na_probs[qid] > na_prob_thresh
												if pred_na:
																		lowerCAmelCase__        =  float(not qid_to_has_ans[qid]						)
												else:
																		lowerCAmelCase__        =  s
						return new_scores







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   str=None						)					->				Union[str, Any]:

						"""simple docstring"""



						if not qid_list:
												lowerCAmelCase__        =  len(UpperCamelCase_						)
												return collections.OrderedDict(
												    [
												        ('exact', 100.0 * sum(exact_scores.values()						) / total),
												        ('f1', 100.0 * sum(fa_scores.values()						) / total),
												        ('total', total),
												    ]						)
						else:
												lowerCAmelCase__        =  len(UpperCamelCase_						)
												return collections.OrderedDict(
												    [
												        ('exact', 100.0 * sum(exact_scores[k] for k in qid_list						) / total),
												        ('f1', 100.0 * sum(fa_scores[k] for k in qid_list						) / total),
												        ('total', total),
												    ]						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Tuple						)					->				Tuple:

						"""simple docstring"""



						for k in new_eval:
												lowerCAmelCase__        =  new_eval[k]







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Optional[int]						)					->				int:

						"""simple docstring"""



						plt.step(UpperCamelCase_      ,    UpperCamelCase_      ,    color='b'      ,    alpha=0.2      ,    where='post'						)
						plt.fill_between(UpperCamelCase_      ,    UpperCamelCase_      ,    step='post'      ,    alpha=0.2      ,    color='b'						)
						plt.xlabel('Recall'						)
						plt.ylabel('Precision'						)
						plt.xlim([0.0, 1.05]						)
						plt.ylim([0.0, 1.05]						)
						plt.title(UpperCamelCase_						)
						plt.savefig(UpperCamelCase_						)
						plt.clf()







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   List[str]=None      ,    UpperCamelCase_    :   Any=None						)					->				List[str]:

						"""simple docstring"""



						lowerCAmelCase__        =  sorted(UpperCamelCase_      ,    key=lambda UpperCamelCase_						: na_probs[k]						)
						lowerCAmelCase__        =  0.0
						lowerCAmelCase__        =  1.0
						lowerCAmelCase__        =  0.0
						lowerCAmelCase__        =  [1.0]
						lowerCAmelCase__        =  [0.0]
						lowerCAmelCase__        =  0.0
						for i, qid in enumerate(UpperCamelCase_						):
												if qid_to_has_ans[qid]:
																		true_pos += scores[qid]
												lowerCAmelCase__        =  true_pos / float(i + 1						)
												lowerCAmelCase__        =  true_pos / float(UpperCamelCase_						)
												if i == len(UpperCamelCase_						) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
																		# i.e., if we can put a threshold after this point
																		avg_prec += cur_p * (cur_r - recalls[-1])
																		precisions.append(UpperCamelCase_						)
																		recalls.append(UpperCamelCase_						)
						if out_image:
												plot_pr_curve(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
						return {"ap": 100.0 * avg_prec}







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Tuple						)					->				Tuple:

						"""simple docstring"""



						if out_image_dir and not os.path.exists(UpperCamelCase_						):
												os.makedirs(UpperCamelCase_						)
						lowerCAmelCase__        =  sum(1 for v in qid_to_has_ans.values() if v						)
						if num_true_pos == 0:
												return
						lowerCAmelCase__        =  make_precision_recall_eval(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    out_image=os.path.join(UpperCamelCase_      ,    'pr_exact.png'						)      ,    title='Precision-Recall curve for Exact Match score'      ,    )
						lowerCAmelCase__        =  make_precision_recall_eval(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    out_image=os.path.join(UpperCamelCase_      ,    'pr_f1.png'						)      ,    title='Precision-Recall curve for F1 score'      ,    )
						lowerCAmelCase__        =  {k: float(UpperCamelCase_						) for k, v in qid_to_has_ans.items()}
						lowerCAmelCase__        =  make_precision_recall_eval(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    out_image=os.path.join(UpperCamelCase_      ,    'pr_oracle.png'						)      ,    title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'      ,    )
						merge_eval(UpperCamelCase_      ,    UpperCamelCase_      ,    'pr_exact'						)
						merge_eval(UpperCamelCase_      ,    UpperCamelCase_      ,    'pr_f1'						)
						merge_eval(UpperCamelCase_      ,    UpperCamelCase_      ,    'pr_oracle'						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Dict						)					->				int:

						"""simple docstring"""



						if not qid_list:
												return
						lowerCAmelCase__        =  [na_probs[k] for k in qid_list]
						lowerCAmelCase__        =  np.ones_like(UpperCamelCase_						) / float(len(UpperCamelCase_						)						)
						plt.hist(UpperCamelCase_      ,    weights=UpperCamelCase_      ,    bins=20      ,    range=(0.0, 1.0)						)
						plt.xlabel('Model probability of no-answer'						)
						plt.ylabel('Proportion of dataset'						)
						plt.title(F"Histogram of no-answer probability: {name}"						)
						plt.savefig(os.path.join(UpperCamelCase_      ,    F"na_prob_hist_{name}.png"						)						)
						plt.clf()







def 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   str						)					->				int:

						"""simple docstring"""



						lowerCAmelCase__        =  sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]						)
						lowerCAmelCase__        =  num_no_ans
						lowerCAmelCase__        =  cur_score
						lowerCAmelCase__        =  0.0
						lowerCAmelCase__        =  sorted(UpperCamelCase_      ,    key=lambda UpperCamelCase_						: na_probs[k]						)
						for i, qid in enumerate(UpperCamelCase_						):
												if qid not in scores:
																		continue
												if qid_to_has_ans[qid]:
																		lowerCAmelCase__        =  scores[qid]
												else:
																		if preds[qid]:
																								lowerCAmelCase__        =  -1
																		else:
																								lowerCAmelCase__        =  0
												cur_score += diff
												if cur_score > best_score:
																		lowerCAmelCase__        =  cur_score
																		lowerCAmelCase__        =  na_probs[qid]
						return 100.0 * best_score / len(UpperCamelCase_						), best_thresh







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   List[str]						)					->				str:

						"""simple docstring"""



						lowerCAmelCase__  ,    lowerCAmelCase__        =  find_best_thresh(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
						lowerCAmelCase__  ,    lowerCAmelCase__        =  find_best_thresh(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
						lowerCAmelCase__        =  best_exact
						lowerCAmelCase__        =  exact_thresh
						lowerCAmelCase__        =  best_fa
						lowerCAmelCase__        =  fa_thresh







def 		_UpperCamelCase				(      )					->				Dict:

						"""simple docstring"""



						with open(OPTS.data_file						) as f:
												lowerCAmelCase__        =  json.load(UpperCamelCase_						)
												lowerCAmelCase__        =  dataset_json['data']
						with open(OPTS.pred_file						) as f:
												lowerCAmelCase__        =  json.load(UpperCamelCase_						)
						if OPTS.na_prob_file:
												with open(OPTS.na_prob_file						) as f:
																		lowerCAmelCase__        =  json.load(UpperCamelCase_						)
						else:
												lowerCAmelCase__        =  {k: 0.0 for k in preds}
						lowerCAmelCase__        =  make_qid_to_has_ans(UpperCamelCase_						)  # maps qid to True/False
						lowerCAmelCase__        =  [k for k, v in qid_to_has_ans.items() if v]
						lowerCAmelCase__        =  [k for k, v in qid_to_has_ans.items() if not v]
						lowerCAmelCase__  ,    lowerCAmelCase__        =  get_raw_scores(UpperCamelCase_      ,    UpperCamelCase_						)
						lowerCAmelCase__        =  apply_no_ans_threshold(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    OPTS.na_prob_thresh						)
						lowerCAmelCase__        =  apply_no_ans_threshold(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    OPTS.na_prob_thresh						)
						lowerCAmelCase__        =  make_eval_dict(UpperCamelCase_      ,    UpperCamelCase_						)
						if has_ans_qids:
												lowerCAmelCase__        =  make_eval_dict(UpperCamelCase_      ,    UpperCamelCase_      ,    qid_list=UpperCamelCase_						)
												merge_eval(UpperCamelCase_      ,    UpperCamelCase_      ,    'HasAns'						)
						if no_ans_qids:
												lowerCAmelCase__        =  make_eval_dict(UpperCamelCase_      ,    UpperCamelCase_      ,    qid_list=UpperCamelCase_						)
												merge_eval(UpperCamelCase_      ,    UpperCamelCase_      ,    'NoAns'						)
						if OPTS.na_prob_file:
												find_all_best_thresh(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
						if OPTS.na_prob_file and OPTS.out_image_dir:
												run_precision_recall_analysis(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    OPTS.out_image_dir						)
												histogram_na_prob(UpperCamelCase_      ,    UpperCamelCase_      ,    OPTS.out_image_dir      ,    'hasAns'						)
												histogram_na_prob(UpperCamelCase_      ,    UpperCamelCase_      ,    OPTS.out_image_dir      ,    'noAns'						)
						if OPTS.out_file:
												with open(OPTS.out_file      ,    'w'						) as f:
																		json.dump(UpperCamelCase_      ,    UpperCamelCase_						)
						else:
												print(json.dumps(UpperCamelCase_      ,    indent=2						)						)


if __name__ == "__main__":
					__snake_case      :      int             =       parse_args()
					if OPTS.out_image_dir:
										import matplotlib

										matplotlib.use("""Agg""")
										import matplotlib.pyplot as plt
					main()





