'''simple docstring'''

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


UpperCamelCase__     :	int				=			'sshleifer/mar_enro_6_3_student'


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         super().setUp()

         UpperCAmelCase__							:				str   				=  cached_path(
             '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''					,extract_compressed_file=lowerCamelCase_					,)
         UpperCAmelCase__							:				Dict   				=  f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''




      @slow
      @require_torch_gpu
      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         MarianMTModel.from_pretrained(lowerCamelCase_							)




      @slow
      @require_torch_gpu
      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  {
             '''$MAX_LEN''': 64,
             '''$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
         UpperCAmelCase__							:				Tuple   				=  (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''							)[1].strip()
         UpperCAmelCase__							:				Tuple   				=  bash_script.replace('''\\\n'''					,''''''							).strip().replace('''"$@"'''					,''''''							)
         for k, v in env_vars_to_replace.items():
            UpperCAmelCase__							:				Any   				=  bash_script.replace(lowerCamelCase_					,str(lowerCamelCase_							)							)
         UpperCAmelCase__							:				str   				=  self.get_auto_remove_tmp_dir()

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

         UpperCAmelCase__							:				List[str]   				=  ['''finetune.py'''] + bash_script.split() + args
         with patch.object(lowerCamelCase_					,'''argv'''					,lowerCamelCase_							):
            UpperCAmelCase__							:				Optional[Any]   				=  argparse.ArgumentParser()
            UpperCAmelCase__							:				Optional[Any]   				=  pl.Trainer.add_argparse_args(lowerCamelCase_							)
            UpperCAmelCase__							:				List[Any]   				=  SummarizationModule.add_model_specific_args(lowerCamelCase_					,os.getcwd()							)
            UpperCAmelCase__							:				Dict   				=  parser.parse_args()
            UpperCAmelCase__							:				Optional[int]   				=  main(lowerCamelCase_							)

         # Check metrics
         UpperCAmelCase__							:				List[Any]   				=  load_json(model.metrics_save_path							)
         UpperCAmelCase__							:				int   				=  metrics['''val'''][0]
         UpperCAmelCase__							:				str   				=  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}''']					,lowerCamelCase_							)

         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
         UpperCAmelCase__							:				Optional[Any]   				=  os.listdir(lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  [x for x in contents if x.endswith('''.ckpt'''							)][0]
         UpperCAmelCase__							:				Optional[Any]   				=  os.path.join(args.output_dir					,lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  torch.load(lowerCamelCase_					,map_location='''cpu'''							)
         UpperCAmelCase__							:				Union[str, Any]   				=  '''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:
            UpperCAmelCase__							:				Any   				=  {os.path.basename(lowerCamelCase_							) 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 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      @timeout_decorator.timeout(600							)
      @slow
      @require_torch_gpu
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
         UpperCAmelCase__							:				int   				=  {
             '''--fp16_opt_level=O1''': '''''',
             '''$MAX_LEN''': 128,
             '''$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
         UpperCAmelCase__							:				List[Any]   				=  (
             (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''							)[1].strip()
         )
         UpperCAmelCase__							:				Tuple   				=  bash_script.replace('''\\\n'''					,''''''							).strip().replace('''"$@"'''					,''''''							)
         UpperCAmelCase__							:				Any   				=  bash_script.replace('''--fp16 '''					,''' '''							)

         for k, v in env_vars_to_replace.items():
            UpperCAmelCase__							:				List[str]   				=  bash_script.replace(lowerCamelCase_					,str(lowerCamelCase_							)							)
         UpperCAmelCase__							:				List[Any]   				=  self.get_auto_remove_tmp_dir()
         UpperCAmelCase__							:				int   				=  bash_script.replace('''--fp16'''					,''''''							)
         UpperCAmelCase__							:				Any   				=  6
         UpperCAmelCase__							:				str   				=  (
             ['''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(lowerCamelCase_					,'''argv'''					,lowerCamelCase_							):
            UpperCAmelCase__							:				List[Any]   				=  argparse.ArgumentParser()
            UpperCAmelCase__							:				Tuple   				=  pl.Trainer.add_argparse_args(lowerCamelCase_							)
            UpperCAmelCase__							:				Tuple   				=  SummarizationDistiller.add_model_specific_args(lowerCamelCase_					,os.getcwd()							)
            UpperCAmelCase__							:				int   				=  parser.parse_args()
            # assert args.gpus == gpus THIS BREAKS for multi_gpu

            UpperCAmelCase__							:				str   				=  distill_main(lowerCamelCase_							)

         # Check metrics
         UpperCAmelCase__							:				Dict   				=  load_json(model.metrics_save_path							)
         UpperCAmelCase__							:				Union[str, Any]   				=  metrics['''val'''][0]
         UpperCAmelCase__							:				Union[str, Any]   				=  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}''']					,lowerCamelCase_							)

         # check lightning ckpt can be loaded and has a reasonable statedict
         UpperCAmelCase__							:				Dict   				=  os.listdir(lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  [x for x in contents if x.endswith('''.ckpt'''							)][0]
         UpperCAmelCase__							:				int   				=  os.path.join(args.output_dir					,lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  torch.load(lowerCamelCase_					,map_location='''cpu'''							)
         UpperCAmelCase__							:				int   				=  '''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:
            UpperCAmelCase__							:				Tuple   				=  {os.path.basename(lowerCamelCase_							) 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





'''simple docstring'''

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

import numpy as np


UpperCamelCase__     :	Union[str, Any]				=			re.compile(r'\b(a|an|the)\b', re.UNICODE)

UpperCamelCase__     :	List[Any]				=			None







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  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=_A  ,			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=_A  ,			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(   _A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            UpperCAmelCase__							:				Union[str, Any]   				=  bool(qa['''answers''']['''text''']    )
   return qid_to_has_ans







def __UpperCamelCase(   _A					:				Dict    ):
   '''simple docstring'''

   def remove_articles(_A					:				Union[str, Any]    ):
      return ARTICLES_REGEX.sub(''' '''  ,			_A    )

   def white_space_fix(_A					:				Optional[int]    ):
      return " ".join(text.split()    )

   def remove_punc(_A					:				Optional[Any]    ):
      UpperCAmelCase__							:				Any   				=  set(string.punctuation    )
      return "".join(ch for ch in text if ch not in exclude    )

   def lower(_A					:				Union[str, Any]    ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_A    )    )    )    )







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   if not s:
      return []
   return normalize_answer(_A    ).split()







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				str    ):
   '''simple docstring'''
   return int(normalize_answer(_A    ) == normalize_answer(_A    )    )







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  get_tokens(_A    )
   UpperCAmelCase__							:				Tuple   				=  get_tokens(_A    )
   UpperCAmelCase__							:				Any   				=  collections.Counter(_A    ) & collections.Counter(_A    )
   UpperCAmelCase__							:				List[Any]   				=  sum(common.values()    )
   if len(_A    ) == 0 or len(_A    ) == 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
   UpperCAmelCase__							:				Optional[Any]   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Tuple   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Optional[int]   				=  (2 * precision * recall) / (precision + recall)
   return fa







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  {}
   UpperCAmelCase__							:				Any   				=  {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            UpperCAmelCase__							:				str   				=  qa['''id''']
            UpperCAmelCase__							:				List[Any]   				=  [t for t in qa['''answers''']['''text'''] if normalize_answer(_A    )]
            if not gold_answers:
               # For unanswerable questions, only correct answer is empty string
               UpperCAmelCase__							:				Tuple   				=  ['''''']
            if qid not in preds:
               print(F'''Missing prediction for {qid}'''    )
               continue
            UpperCAmelCase__							:				Union[str, Any]   				=  preds[qid]
            # Take max over all gold answers
            UpperCAmelCase__							:				List[str]   				=  max(compute_exact(_A  ,			_A    ) for a in gold_answers    )
            UpperCAmelCase__							:				List[str]   				=  max(compute_fa(_A  ,			_A    ) for a in gold_answers    )
   return exact_scores, fa_scores







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Optional[Any]  ,			_A					:				List[str]  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  {}
   for qid, s in scores.items():
      UpperCAmelCase__							:				Dict   				=  na_probs[qid] > na_prob_thresh
      if pred_na:
         UpperCAmelCase__							:				Any   				=  float(not qid_to_has_ans[qid]    )
      else:
         UpperCAmelCase__							:				List[str]   				=  s
   return new_scores







def __UpperCamelCase(   _A					:				str  ,			_A					:				Optional[Any]  ,			_A					:				Any=None    ):
   '''simple docstring'''
   if not qid_list:
      UpperCAmelCase__							:				List[Any]   				=  len(_A    )
      return collections.OrderedDict(
          [
              ('''exact''', 1_0_0.0 * sum(exact_scores.values()    ) / total),
              ('''f1''', 1_0_0.0 * sum(fa_scores.values()    ) / total),
              ('''total''', total),
          ]    )
   else:
      UpperCAmelCase__							:				List[str]   				=  len(_A    )
      return collections.OrderedDict(
          [
              ('''exact''', 1_0_0.0 * sum(exact_scores[k] for k in qid_list    ) / total),
              ('''f1''', 1_0_0.0 * sum(fa_scores[k] for k in qid_list    ) / total),
              ('''total''', total),
          ]    )







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				List[Any]  ,			_A					:				Tuple    ):
   '''simple docstring'''
   for k in new_eval:
      UpperCAmelCase__							:				List[str]   				=  new_eval[k]







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Any  ,			_A					:				Optional[int]  ,			_A					:				int    ):
   '''simple docstring'''
   plt.step(_A  ,			_A  ,			color='''b'''  ,			alpha=0.2  ,			where='''post'''    )
   plt.fill_between(_A  ,			_A  ,			step='''post'''  ,			alpha=0.2  ,			color='''b'''    )
   plt.xlabel('''Recall'''    )
   plt.ylabel('''Precision'''    )
   plt.xlim([0.0, 1.0_5]    )
   plt.ylim([0.0, 1.0_5]    )
   plt.title(_A    )
   plt.savefig(_A    )
   plt.clf()







def __UpperCamelCase(   _A					:				Optional[int]  ,			_A					:				Tuple  ,			_A					:				Any  ,			_A					:				Any  ,			_A					:				Tuple=None  ,			_A					:				Union[str, Any]=None    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  sorted(_A  ,			key=lambda _A    : na_probs[k]    )
   UpperCAmelCase__							:				Tuple   				=  0.0
   UpperCAmelCase__							:				Any   				=  1.0
   UpperCAmelCase__							:				Any   				=  0.0
   UpperCAmelCase__							:				Union[str, Any]   				=  [1.0]
   UpperCAmelCase__							:				int   				=  [0.0]
   UpperCAmelCase__							:				Optional[Any]   				=  0.0
   for i, qid in enumerate(_A    ):
      if qid_to_has_ans[qid]:
         true_pos += scores[qid]
      UpperCAmelCase__							:				Optional[Any]   				=  true_pos / float(i + 1    )
      UpperCAmelCase__							:				int   				=  true_pos / float(_A    )
      if i == len(_A    ) - 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(_A    )
         recalls.append(_A    )
   if out_image:
      plot_pr_curve(_A  ,			_A  ,			_A  ,			_A    )
   return {"ap": 1_0_0.0 * avg_prec}







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Optional[Any]  ,			_A					:				List[Any]  ,			_A					:				Any  ,			_A					:				Dict  ,			_A					:				Any    ):
   '''simple docstring'''
   if out_image_dir and not os.path.exists(_A    ):
      os.makedirs(_A    )
   UpperCAmelCase__							:				List[str]   				=  sum(1 for v in qid_to_has_ans.values() if v    )
   if num_true_pos == 0:
      return
   UpperCAmelCase__							:				Dict   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_exact.png'''    )  ,			title='''Precision-Recall curve for Exact Match score'''  ,			)
   UpperCAmelCase__							:				Any   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_f1.png'''    )  ,			title='''Precision-Recall curve for F1 score'''  ,			)
   UpperCAmelCase__							:				Tuple   				=  {k: float(_A    ) for k, v in qid_to_has_ans.items()}
   UpperCAmelCase__							:				Any   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_oracle.png'''    )  ,			title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'''  ,			)
   merge_eval(_A  ,			_A  ,			'''pr_exact'''    )
   merge_eval(_A  ,			_A  ,			'''pr_f1'''    )
   merge_eval(_A  ,			_A  ,			'''pr_oracle'''    )







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Dict  ,			_A					:				Dict  ,			_A					:				Tuple    ):
   '''simple docstring'''
   if not qid_list:
      return
   UpperCAmelCase__							:				Optional[Any]   				=  [na_probs[k] for k in qid_list]
   UpperCAmelCase__							:				Union[str, Any]   				=  np.ones_like(_A    ) / float(len(_A    )    )
   plt.hist(_A  ,			weights=_A  ,			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(_A  ,			F'''na_prob_hist_{name}.png'''    )    )
   plt.clf()







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				List[str]  ,			_A					:				Optional[int]  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]    )
   UpperCAmelCase__							:				List[str]   				=  num_no_ans
   UpperCAmelCase__							:				Any   				=  cur_score
   UpperCAmelCase__							:				List[str]   				=  0.0
   UpperCAmelCase__							:				Dict   				=  sorted(_A  ,			key=lambda _A    : na_probs[k]    )
   for i, qid in enumerate(_A    ):
      if qid not in scores:
         continue
      if qid_to_has_ans[qid]:
         UpperCAmelCase__							:				int   				=  scores[qid]
      else:
         if preds[qid]:
            UpperCAmelCase__							:				Any   				=  -1
         else:
            UpperCAmelCase__							:				Dict   				=  0
      cur_score += diff
      if cur_score > best_score:
         UpperCAmelCase__							:				Optional[Any]   				=  cur_score
         UpperCAmelCase__							:				Tuple   				=  na_probs[qid]
   return 1_0_0.0 * best_score / len(_A    ), best_thresh







def __UpperCamelCase(   _A					:				str  ,			_A					:				str  ,			_A					:				int  ,			_A					:				int  ,			_A					:				Tuple  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  find_best_thresh(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  find_best_thresh(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__							:				List[str]   				=  best_exact
   UpperCAmelCase__							:				Any   				=  exact_thresh
   UpperCAmelCase__							:				Dict   				=  best_fa
   UpperCAmelCase__							:				Dict   				=  fa_thresh







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


if __name__ == "__main__":
  UpperCamelCase__     :	Tuple				=			parse_args()
  if OPTS.out_image_dir:
    import matplotlib

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




