



"""simple docstring"""




import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


_lowerCAmelCase       :      Optional[Any]							=							torch.cuda.is_available()
_lowerCAmelCase       :      str							=							[" Sam ate lunch today.", "Sams lunch ingredients."]
_lowerCAmelCase       :      Dict							=							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_lowerCAmelCase       :      Union[str, Any]							=							{
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1E-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Path		,						SCREAMING_SNAKE_CASE__			:		list       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			Tuple              =					"\n".join(SCREAMING_SNAKE_CASE__       )
     Path(SCREAMING_SNAKE_CASE__       ).open("w"       ).writelines(SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]       )						->     Union[str, Any]:





     '''simple docstring'''

     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.source'       )		,						SCREAMING_SNAKE_CASE__       )
          _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.target'       )		,						SCREAMING_SNAKE_CASE__       )
     return tmp_dir






class 			UpperCAmelCase_						(       _UpperCamelCase		):



      @classmethod
      def 		snake_case_      (  cls  :			Optional[int]		):
           return cls



      @require_torch_multi_gpu
      def 		snake_case_      (  self  :			Union[str, Any]		):
           _UpperCAmelCase    :			Dict              =					{
               "no_teacher": True,
               "freeze_encoder": True,
               "gpus": 2,
               "overwrite_output_dir": True,
               "sortish_sampler": True,
           }
           self._test_distiller_cli_fork(A  ,			check_contents=A		)



      def 		snake_case_      (  self  :			List[str]  ,			A  :			List[Any]  ,			A  :			Optional[Any]=True		):
           _UpperCAmelCase    :			int              =					{
               "label_smoothing": 0.0,
               "early_stopping_patience": -1,
               "train_batch_size": 1,
               "eval_batch_size": 2,
               "max_epochs": 2,
               "alpha_mlm": 0.2,
               "alpha_ce": 0.8,
               "do_predict": True,
               "model_name_or_path": "sshleifer/tinier_bart",
               "teacher": CHEAP_ARGS["model_name_or_path"],
               "val_check_interval": 0.5,
           }
           default_updates.update(A		)
           _UpperCAmelCase    :			dict              =					CHEAP_ARGS.copy()
           _UpperCAmelCase    :			Union[str, Any]              =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
           _UpperCAmelCase    :			List[Any]              =					self.get_auto_remove_tmp_dir()
           args_d.update(data_dir=A  ,			output_dir=A  ,			**A		)

           def convert(A  :			List[Any]  ,			A  :			Optional[int]		):
                if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                     return ""
                if v is False or v is None:
                     return ""
                if v is True:  # or len(str(v))==0:
                     return f'--{k}'
                return f'--{k}={v}'

           _UpperCAmelCase    :			Tuple              =					[x for x in (convert(A  ,			A		) for k, v in args_d.items()) if len(A		)]
           _UpperCAmelCase    :			Union[str, Any]              =					[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
           execute_subprocess_async(A  ,			env=self.get_env()		)

           _UpperCAmelCase    :			int              =					os.listdir(A		)
           _UpperCAmelCase    :			List[Any]              =					{os.path.basename(A		) for p in contents}
           _UpperCAmelCase    :			List[Any]              =					[p for p in contents if p.endswith("ckpt"		)]
           assert len(A		) > 0

           self.assertIn("test_generations.txt"  ,			A		)
           self.assertIn("test_results.txt"  ,			A		)

           # get the following from the module, (we don't have access to `model` here)
           _UpperCAmelCase    :			List[Any]              =					os.path.join(A  ,			"metrics.json"		)
           _UpperCAmelCase    :			str              =					"rouge2"

           _UpperCAmelCase    :			List[Any]              =					load_json(A		)
           # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
           print(A		)
           _UpperCAmelCase    :			List[str]              =					metrics["val"][-1]
           self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]  ,			0.01		)
           self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}']  ,			A		)
           self.assertEqual(len(metrics["test"]		)  ,			1		)
           _UpperCAmelCase    :			Dict              =					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1		)
           self.assertEqual(len(metrics["val"]		)  ,			A		)





"""simple docstring"""




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

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     Union[str, Any]:





     '''simple docstring'''

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

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

     def remove_punc(SCREAMING_SNAKE_CASE__			:		Optional[int]       ):
          _UpperCAmelCase    :			List[str]              =					set(string.punctuation       )
          return "".join(ch for ch in text if ch not in exclude       )

     def lower(SCREAMING_SNAKE_CASE__			:		List[str]       ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__       )       )       )       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		str       )						->     Dict:





     '''simple docstring'''

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

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		Dict       )						->     str:





     '''simple docstring'''

     return normalize_answer(SCREAMING_SNAKE_CASE__       ) == normalize_answer(SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     str:





     '''simple docstring'''

     _UpperCAmelCase    :			int              =					[]
     for ground_truth in ground_truths:
          _UpperCAmelCase    :			Dict              =					metric_fn(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
          scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__       )
     return max(SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Tuple:





     '''simple docstring'''

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

               _UpperCAmelCase    :			Union[str, Any]              =					[x["text"] for x in qa["answers"]]
               _UpperCAmelCase    :			Optional[int]              =					predictions[qa["id"]]

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

               fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )

     _UpperCAmelCase    :			List[str]              =					exact_match / total
     _UpperCAmelCase    :			str              =					fa / total

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


if __name__ == "__main__":
 _lowerCAmelCase       :      Any							=							"1.0"
 _lowerCAmelCase       :      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.")
 _lowerCAmelCase       :      Tuple							=							parser.parse_args()

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

 with open(args.pred_file) as pred_file:
  _lowerCAmelCase       :      str							=							json.load(pred_file)

 _lowerCAmelCase,		_lowerCAmelCase       :      List[Any]							=							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)
