'''simple docstring'''






import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_UpperCAmelCase						:    Tuple     					=					logging.getLogger()
_UpperCAmelCase						:    List[str]     					=					torch.cuda.is_available()
_UpperCAmelCase						:    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-0_8,
    '''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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Path      ,		__snake_case       : list							):
					_A								=     '\n'.join(__snake_case							)
					Path(__snake_case							).open('w'							).writelines(__snake_case							)


_UpperCAmelCase						:    Tuple     					=					[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_UpperCAmelCase						:    List[str]     					=					['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_UpperCAmelCase						:    List[Any]     					=					'''patrickvonplaten/t5-tiny-random'''
_UpperCAmelCase						:    Tuple     					=					'''sshleifer/t5-tinier-random'''
_UpperCAmelCase						:    Optional[int]     					=					'''sshleifer/bart-tiny-random'''
_UpperCAmelCase						:    Optional[int]     					=					'''sshleifer/tiny-mbart'''
_UpperCAmelCase						:    int     					=					'''sshleifer/tiny-marian-en-de'''
_UpperCAmelCase						:    Optional[int]     					=					'''stas/tiny-wmt19-en-de'''


_UpperCAmelCase						:    Dict     					=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple							):
					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(__snake_case      ,		F'{split}.source'							)      ,		__snake_case							)
										_dump_articles(os.path.join(__snake_case      ,		F'{split}.target'							)      ,		__snake_case							)
					return tmp_dir







class lowercase_					(		_UpperCamelCase ):







			"""simple docstring"""

			@classmethod
			def   __UpperCAmelCase      (			cls			: int    )     -> List[Any]:
								logging.disable(logging.CRITICAL    )  # remove noisy download output from tracebacks
								return cls
			@slow
			@require_torch_gpu
			def   __UpperCAmelCase      (			self			: str    )     -> List[str]:
								_A								=     list_models()
								_A								=     'sshleifer'
								_A								=     [x.modelId for x in model_list if x.modelId.startswith(UpperCamelCase__    )]
								_A								=     ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
								_A								=     []
								for m in model_ids:
													if m in allowed_to_be_broken:
																		continue
													try:
																		AutoConfig.from_pretrained(UpperCamelCase__    )
													except Exception:
																		failures.append(UpperCamelCase__    )
								assert not failures, f'The following models could not be loaded through AutoConfig: {failures}'
			def   __UpperCAmelCase      (			self			: str    )     -> Tuple:
								_A								=     {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
								self._test_distiller_cli(UpperCamelCase__    )
			def   __UpperCAmelCase      (			self			: Optional[int]    )     -> Dict:
								_A								=     {
								    'student_encoder_layers': 2,
								    'student_decoder_layers': 1,
								    'max_epochs': 4,
								    'val_check_interval': 0.25,
								    'alpha_hid': 2.0,
								    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
								}
								_A								=     self._test_distiller_cli(UpperCamelCase__,   check_contents=UpperCamelCase__    )

								_A								=     list(Path(model.output_dir    ).glob('*.ckpt'    )    )
								self.assertEqual(1,   len(UpperCamelCase__    )    )
								_A								=     list(Path(model.output_dir    ).glob('**/*.bin'    )    )
								self.assertEqual(len(UpperCamelCase__    ),   2    )
								_A								=     lmap(str.strip,   Path(model.hparams.data_dir    ).joinpath('test.source'    ).open().readlines()    )
								_A								=     tempfile.mktemp()  # XXX: not being cleaned up
								generate_summaries_or_translations(UpperCamelCase__,   UpperCamelCase__,   str(model.output_dir / 'best_tfmr'    )    )
								self.assertTrue(Path(UpperCamelCase__    ).exists()    )

								_A								=     self.get_auto_remove_tmp_dir()
								convert_pl_to_hf(ckpts[0],   transformer_ckpts[0].parent,   UpperCamelCase__    )
								assert os.path.exists(os.path.join(UpperCamelCase__,   'pytorch_model.bin'    )    )
			def   __UpperCAmelCase      (			self			: str    )     -> List[Any]:
								_A								=     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__    )
								_A		,					_A								=     model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
								_A								=     torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]],   dtype=torch.long,   device=model.device    )
								_A								=     target_ids[:, :-1].contiguous()  # Why this line?
								_A								=     target_ids[:, 1:].clone()  # why clone?
								_A								=     model(
								    UpperCamelCase__,   attention_mask=UpperCamelCase__,   decoder_input_ids=UpperCamelCase__,   labels=UpperCamelCase__,   use_cache=UpperCamelCase__    ).loss

								_A								=     model(UpperCamelCase__,   attention_mask=UpperCamelCase__,   decoder_input_ids=UpperCamelCase__,   use_cache=UpperCamelCase__    ).logits

								_A								=     nn.functional.log_softmax(UpperCamelCase__,   dim=-1    )
								_A		,					_A								=     label_smoothed_nll_loss(
								    UpperCamelCase__,   UpperCamelCase__,   0.1,   ignore_index=model.config.pad_token_id    )
								with self.assertRaises(UpperCamelCase__    ):
													# TODO: understand why this breaks
													self.assertEqual(UpperCamelCase__,   UpperCamelCase__    )
			def   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> int:
								_A								=     {
								    'student_encoder_layers': 2,
								    'student_decoder_layers': 1,
								    'num_train_epochs': 4,
								    'val_check_interval': 0.25,
								    'alpha_hid': 2.0,
								    'task': 'translation',
								    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
								    'tokenizer_name': MBART_TINY,
								    'teacher': MBART_TINY,
								    'src_lang': 'en_XX',
								    'tgt_lang': 'ro_RO',
								}
								_A								=     self._test_distiller_cli(UpperCamelCase__,   check_contents=UpperCamelCase__    )
								assert model.model.config.model_type == "mbart"

								_A								=     list(Path(model.output_dir    ).glob('*.ckpt'    )    )
								self.assertEqual(1,   len(UpperCamelCase__    )    )
								_A								=     list(Path(model.output_dir    ).glob('**/*.bin'    )    )
								_A								=     list(Path(model.output_dir    ).glob('best_tfmr/*'    )    )
								assert len(UpperCamelCase__    ) > 2
								self.assertEqual(len(UpperCamelCase__    ),   2    )
			def   __UpperCAmelCase      (			self			: str    )     -> Tuple:
								_A								=     {
								    'student_encoder_layers': 1,
								    'student_decoder_layers': 1,
								    'alpha_hid': 2.0,
								    'teacher': T5_TINY,
								    'model_name_or_path': T5_TINY,
								    'tokenizer_name': T5_TINY,
								}
								self._test_distiller_cli(UpperCamelCase__    )
			def   __UpperCAmelCase      (			self			: Optional[int]    )     -> Optional[int]:
								_A								=     {
								    'teacher': T5_TINY,
								    'student': T5_TINIER,
								    'model_name_or_path': T5_TINIER,
								    'tokenizer_name': T5_TINIER,
								}
								self._test_distiller_cli(UpperCamelCase__    )

			def   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: List[Any],   UpperCamelCase__			: Any=True    )     -> Dict:
								_A								=     {
								    '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(UpperCamelCase__    )
								_A								=     CHEAP_ARGS.copy()
								_A								=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
								_A								=     self.get_auto_remove_tmp_dir()

								args_d.update(data_dir=UpperCamelCase__,   output_dir=UpperCamelCase__,   **UpperCamelCase__    )
								_A								=     distill_main(argparse.Namespace(**UpperCamelCase__    )    )
								if not check_contents:
													return model
								_A								=     os.listdir(UpperCamelCase__    )
								_A								=     {os.path.basename(UpperCamelCase__    ) for p in contents}
								_A								=     [p for p in contents if p.endswith('ckpt'    )]
								assert len(UpperCamelCase__    ) > 0

								self.assertIn('test_generations.txt',   UpperCamelCase__    )
								self.assertIn('test_results.txt',   UpperCamelCase__    )

								_A								=     load_json(model.metrics_save_path    )
								_A								=     metrics['val'][-1]
								self.assertGreaterEqual(last_step_stats['val_avg_gen_time'],   0.01    )
								self.assertGreaterEqual(1.0,   last_step_stats['val_avg_gen_time']    )
								self.assertIsInstance(last_step_stats[f'val_avg_{model.val_metric}'],   UpperCamelCase__    )
								_A								=     int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1    )
								self.assertEqual(len(metrics['val']    ),   UpperCamelCase__    )
								self.assertEqual(len(metrics['test']    ),   1    )
								return model







class lowercase_					(		_UpperCamelCase ):







			"""simple docstring"""

			@parameterized.expand(
			    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY],   )
			def   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: Any    )     -> Optional[int]:
								_A								=     CHEAP_ARGS.copy()
								_A								=     'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
								_A								=     0.1 if task == 'translation' else 0

								_A								=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
								_A								=     self.get_auto_remove_tmp_dir()
								args_d.update(
								    data_dir=UpperCamelCase__,   model_name_or_path=UpperCamelCase__,   tokenizer_name=UpperCamelCase__,   train_batch_size=2,   eval_batch_size=2,   output_dir=UpperCamelCase__,   do_predict=UpperCamelCase__,   task=UpperCamelCase__,   src_lang='en_XX',   tgt_lang='ro_RO',   freeze_encoder=UpperCamelCase__,   freeze_embeds=UpperCamelCase__,   )
								assert "n_train" in args_d
								_A								=     argparse.Namespace(**UpperCamelCase__    )
								_A								=     main(UpperCamelCase__    )

								_A								=     module.model.get_input_embeddings()
								assert not input_embeds.weight.requires_grad
								if model == T5_TINY:
													_A								=     module.model.lm_head
													assert not lm_head.weight.requires_grad
													assert (lm_head.weight == input_embeds.weight).all().item()
								elif model == FSMT_TINY:
													_A								=     module.model.model
													_A								=     fsmt.decoder.embed_positions
													assert not embed_pos.weight.requires_grad
													assert not fsmt.decoder.embed_tokens.weight.requires_grad
													# check that embeds are not the same
													assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
								else:
													_A								=     module.model.model
													_A								=     bart.decoder.embed_positions
													assert not embed_pos.weight.requires_grad
													assert not bart.shared.weight.requires_grad
													# check that embeds are the same
													assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
													assert bart.decoder.embed_tokens == bart.shared

								_A								=     load_json(module.output_dir / 'text_batch.json'    )
								assert isinstance(UpperCamelCase__,   UpperCamelCase__    )
								assert len(UpperCamelCase__    ) >= 4
			def   __UpperCAmelCase      (			self			: int    )     -> Union[str, Any]:
								_A								=     CHEAP_ARGS.copy()

								_A								=     'summarization'
								_A								=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )

								args_d.update(
								    data_dir=UpperCamelCase__,   tokenizer_name=UpperCamelCase__,   train_batch_size=2,   eval_batch_size=2,   do_predict=UpperCamelCase__,   task=UpperCamelCase__,   src_lang='en_XX',   tgt_lang='ro_RO',   freeze_encoder=UpperCamelCase__,   freeze_embeds=UpperCamelCase__,   )

								# test models whose config includes the extra_model_args
								_A								=     BART_TINY
								_A								=     self.get_auto_remove_tmp_dir()
								_A								=     args_d.copy()
								args_da.update(
								    model_name_or_path=UpperCamelCase__,   output_dir=UpperCamelCase__,   )
								_A								=     ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
								for p in extra_model_params:
													_A								=     0.5
								_A								=     argparse.Namespace(**UpperCamelCase__    )
								_A								=     main(UpperCamelCase__    )
								for p in extra_model_params:
													assert getattr(model.config,   UpperCamelCase__    ) == 0.5, f'failed to override the model config for param {p}'

								# test models whose config doesn't include the extra_model_args
								_A								=     T5_TINY
								_A								=     self.get_auto_remove_tmp_dir()
								_A								=     args_d.copy()
								args_da.update(
								    model_name_or_path=UpperCamelCase__,   output_dir=UpperCamelCase__,   )
								_A								=     'encoder_layerdrop'
								_A								=     0.5
								_A								=     argparse.Namespace(**UpperCamelCase__    )
								with pytest.raises(UpperCamelCase__    ) as excinfo:
													_A								=     main(UpperCamelCase__    )
								assert str(excinfo.value    ) == f'model config doesn\'t have a `{unsupported_param}` attribute'

			def   __UpperCAmelCase      (			self			: Tuple    )     -> str:
								_A								=     CHEAP_ARGS.copy()

								_A								=     'summarization'
								_A								=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )

								_A								=     BART_TINY
								_A								=     self.get_auto_remove_tmp_dir()

								args_d.update(
								    data_dir=UpperCamelCase__,   model_name_or_path=UpperCamelCase__,   output_dir=UpperCamelCase__,   tokenizer_name=UpperCamelCase__,   train_batch_size=2,   eval_batch_size=2,   do_predict=UpperCamelCase__,   task=UpperCamelCase__,   src_lang='en_XX',   tgt_lang='ro_RO',   freeze_encoder=UpperCamelCase__,   freeze_embeds=UpperCamelCase__,   )

								# emulate finetune.py
								_A								=     argparse.ArgumentParser()
								_A								=     pl.Trainer.add_argparse_args(UpperCamelCase__    )
								_A								=     SummarizationModule.add_model_specific_args(UpperCamelCase__,   os.getcwd()    )
								_A								=     {'--help': True}

								# --help test
								with pytest.raises(UpperCamelCase__    ) as excinfo:
													with CaptureStdout() as cs:
																		_A								=     parser.parse_args(UpperCamelCase__    )
													assert False, "--help is expected to sys.exit"
								assert excinfo.type == SystemExit
								_A								=     lightning_base.arg_to_scheduler_metavar
								assert expected in cs.out, "--help is expected to list the supported schedulers"

								# --lr_scheduler=non_existing_scheduler test
								_A								=     'non_existing_scheduler'
								_A								=     {f'--lr_scheduler={unsupported_param}'}
								with pytest.raises(UpperCamelCase__    ) as excinfo:
													with CaptureStderr() as cs:
																		_A								=     parser.parse_args(UpperCamelCase__    )
													assert False, "invalid argument is expected to sys.exit"
								assert excinfo.type == SystemExit
								_A								=     f'invalid choice: \'{unsupported_param}\''
								assert expected in cs.err, f'should have bailed on invalid choice of scheduler {unsupported_param}'

								# --lr_scheduler=existing_scheduler test
								_A								=     'cosine'
								_A								=     args_d.copy()
								_A								=     supported_param
								_A								=     argparse.Namespace(**UpperCamelCase__    )
								_A								=     main(UpperCamelCase__    )
								assert (
								    getattr(model.hparams,   'lr_scheduler'    ) == supported_param
								), f'lr_scheduler={supported_param} shouldn\'t fail'




'''simple docstring'''






import argparse
import json
import re
import string
import sys

import numpy as np


_UpperCAmelCase						:    Dict     					=					0.5





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : List[Any]							):
     _A								=     ['.', ',', ';', ':']

     for token in remove_tokens:
          _A								=     ground_truth.replace(__snake_case      ,		''							)
          _A								=     prediction.replace(__snake_case      ,		''							)

     _A		,					_A								=     ground_truth.lower(), prediction.lower()
     _A		,					_A								=     ground_truth.replace('/'      ,		' '							), prediction.replace('/'      ,		' '							)
     _A		,					_A								=     set(ground_truth.split(' '							)							), set(prediction.split(' '							)							)

     _A								=     ground_truth.intersection(__snake_case							)
     _A								=     ground_truth.union(__snake_case							)
     _A								=     len(__snake_case							) / len(__snake_case							)
     return jaccard





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any							):

     def remove_articles(__snake_case       : Dict							):
          return re.sub(R'\b(a|an|the)\b'      ,		' '      ,		__snake_case							)

     def white_space_fix(__snake_case       : int							):
          return " ".join(text.split()							)

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

     def lower(__snake_case       : Optional[int]							):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(__snake_case							)							)							)							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : Tuple      ,		__snake_case       : List[str]							):
     _A		,					_A		,					_A								=     0, 0, 0

     _A								=     'Parties' in qa_id

     # first check if ground truth is empty
     if len(__snake_case							) == 0:
          if len(__snake_case							) > 0:
               fp += len(__snake_case							)  # false positive for each one
     else:
          for ground_truth in ground_truths:
               assert len(__snake_case							) > 0
               # check if there is a match
               _A								=     False
               for pred in predictions:
                    if substr_ok:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH or ground_truth in pred
                    else:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH
                    if is_match:
                         _A								=     True

               if match_found:
                    tp += 1
               else:
                    fn += 1

        # now also get any fps by looping through preds
          for pred in predictions:
               # Check if there's a match. if so, don't count (don't want to double count based on the above)
               # but if there's no match, then this is a false positive.
               # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
               # multiple predictions that are matched with the same answer.)
               _A								=     False
               for ground_truth in ground_truths:
                    assert len(__snake_case							) > 0
                    if substr_ok:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH or ground_truth in pred
                    else:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH
                    if is_match:
                         _A								=     True

               if not match_found:
                    fp += 1

     _A								=     tp / (tp + fp) if tp + fp > 0 else np.nan
     _A								=     tp / (tp + fn) if tp + fn > 0 else np.nan

     return precision, recall





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     _A								=     precisions[::-1]
     for i in range(1      ,		len(__snake_case							)							):
          _A								=     max(precision_best[i - 1]      ,		precision_best[i]							)
     _A								=     precision_best[::-1]
     return precisions





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Any							):
     _A								=     process_precisions(__snake_case							)
     _A								=     np.trapz(__snake_case      ,		__snake_case							)
     if np.isnan(__snake_case							):
          return 0
     return aupr





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]							):
     _A								=     process_precisions(__snake_case							)
     _A								=     0
     for prec, recall in zip(__snake_case      ,		__snake_case							):
          if recall >= recall_thresh:
               _A								=     prec
               break
     return prec_at_recall





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Dict							):
     return normalize_answer(__snake_case							) == normalize_answer(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Tuple      ,		__snake_case       : List[Any]							):
     _A								=     0
     for pred in predictions:
          for ground_truth in ground_truths:
               _A								=     metric_fn(__snake_case      ,		__snake_case							)
               if score == 1:  # break the loop when one prediction matches the ground truth
                    break
          if score == 1:
               break
     return score





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : Tuple							):
     _A								=     _A								=     _A								=     0
     _A								=     []
     _A								=     []
     for article in dataset:
          for paragraph in article["paragraphs"]:
               for qa in paragraph["qas"]:
                    total += 1
                    if qa["id"] not in predictions:
                         _A								=     'Unanswered question ' + qa['id'] + ' will receive score 0.'
                         print(__snake_case      ,		file=sys.stderr							)
                         continue
                    _A								=     [x['text'] for x in qa['answers']]
                    _A								=     predictions[qa['id']]
                    _A		,					_A								=     compute_precision_recall(__snake_case      ,		__snake_case      ,		qa['id']							)

                    precisions.append(__snake_case							)
                    recalls.append(__snake_case							)

                    if precision == 0 and recall == 0:
                         fa += 0
                    else:
                         fa += 2 * (precision * recall) / (precision + recall)

                    exact_match += metric_max_over_ground_truths(__snake_case      ,		__snake_case      ,		__snake_case							)

     _A								=     [x for _, x in sorted(zip(__snake_case      ,		__snake_case							)							)]
     recalls.sort()

     _A								=     1_00.0 * fa / total
     _A								=     1_00.0 * exact_match / total
     _A								=     get_aupr(__snake_case      ,		__snake_case							)

     _A								=     get_prec_at_recall(__snake_case      ,		__snake_case      ,		recall_thresh=0.9							)
     _A								=     get_prec_at_recall(__snake_case      ,		__snake_case      ,		recall_thresh=0.8							)

     return {
         "exact_match": exact_match,
         "f1": fa,
         "aupr": aupr,
         "prec_at_80_recall": prec_at_aa_recall,
         "prec_at_90_recall": prec_at_aa_recall,
     }


if __name__ == "__main__":
   _UpperCAmelCase						:    Union[str, Any]     					=					argparse.ArgumentParser(description='''Evaluation for CUAD''')
   parser.add_argument('''dataset_file''', help='''Dataset file''')
   parser.add_argument('''prediction_file''', help='''Prediction File''')
   _UpperCAmelCase						:    List[Any]     					=					parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      _UpperCAmelCase						:    Tuple     					=					json.load(dataset_file)
      _UpperCAmelCase						:    Dict     					=					dataset_json['''data''']
   with open(args.prediction_file) as prediction_file:
      _UpperCAmelCase						:    Tuple     					=					json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))



