





import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
				from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
				UpperCamelCase__ :			str       	=			True
				from torch.cuda.amp import autocast


UpperCamelCase__ :			str       	=			logging.getLogger(__name__)







@dataclass
class 	_UpperCamelCase	:







					'''simple docstring'''

					lowerCamelCase  :  str           =							field(
					    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}	)
					lowerCamelCase  :  Optional[str]           =							field(
					    default=__lowerCamelCase    ,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}    ,							)
					lowerCamelCase  :  Optional[bool]           =							field(
					    default=__lowerCamelCase    ,							metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}	)
					lowerCamelCase  :  Optional[bool]           =							field(
					    default=__lowerCamelCase    ,							metadata={'help': 'Whether to log verbose messages or not.'}    ,							)




def  A_(      A					,   A ):
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					,   datefmt="""%m/%d/%Y %H:%M:%S"""					,   handlers=[logging.StreamHandler(sys.stdout )]					,   )
	UpperCAmelCase_		    =  logging.WARNING
	if model_args.verbose_logging:
		UpperCAmelCase_		    =  logging.DEBUG
	elif trainer_utils.is_main_process(training_args.local_rank ):
		UpperCAmelCase_		    =  logging.INFO
	logger.setLevel(A__ )







@dataclass
class 	_UpperCamelCase	:







					'''simple docstring'''

					lowerCamelCase  :  str           =							field(
					    default=__lowerCamelCase    ,							metadata={'help': 'The name of the dataset to use (via the datasets library).'}	)
					lowerCamelCase  :  Optional[str]           =							field(
					    default=__lowerCamelCase    ,							metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}	)
					lowerCamelCase  :  Optional[str]           =							field(
					    default='train'    ,							metadata={
					        'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
					    }    ,							)
					lowerCamelCase  :  Optional[str]           =							field(
					    default='validation'    ,							metadata={
					        'help': (
					            'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
					        )
					    }    ,							)
					lowerCamelCase  :  Optional[str]           =							field(
					    default='text'    ,							metadata={'help': 'Column in the dataset that contains label (target text). Defaults to \'text\''}    ,							)
					lowerCamelCase  :  Optional[str]           =							field(
					    default='file'    ,							metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}    ,							)
					lowerCamelCase  :  Optional[bool]           =							field(
					    default=__lowerCamelCase    ,							metadata={'help': 'Resample loaded audio to target feature extractor\'s sampling rate or not.'}    ,							)
					lowerCamelCase  :  Optional[float]           =							field(
					    default=__lowerCamelCase    ,							metadata={'help': 'Filters out examples longer than specified. Defaults to no filtering.'}    ,							)
					lowerCamelCase  :  Optional[str]           =							field(
					    default='librispeech'    ,							metadata={
					        'help': (
					            'Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'
					            ' \'buckwalter\'.'
					        )
					    }    ,							)
					lowerCamelCase  :  bool           =							field(
					    default=__lowerCamelCase    ,							metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}	)
					lowerCamelCase  :  Optional[int]           =							field(
					    default=__lowerCamelCase    ,							metadata={'help': 'The number of processes to use for the preprocessing.'}    ,							)







@dataclass
class 	_UpperCamelCase	:







					'''simple docstring'''

					lowerCamelCase  :  bool           =							False
					lowerCamelCase  :  Optional[str]           =							None
					lowerCamelCase  :  Optional[str]           =							"|"
					lowerCamelCase  :  Optional[Dict[str, str]]           =							field(default_factory=__lowerCamelCase	)
					lowerCamelCase  :  Optional[Set[str]]           =							field(default_factory=__lowerCamelCase	)
					lowerCamelCase  :  Optional[Callable[[str], str]]           =							None





					@classmethod
					def 							SCREAMING_SNAKE_CASE				(    cls     :	Dict			,       __lowercase     :	str		):






						'''simple docstring'''







						if name == "librispeech":
							return cls()
						if name == "timit":
							return cls(
							    do_lower_case=UpperCamelCase_			,       translation_table=str.maketrans({"""-""": """ """}		)			,       )
						if name == "buckwalter":
							UpperCAmelCase_		    =  {
							    """-""": """ """,  # sometimes used to represent pauses
							    """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
							}
							return cls(
							    vocab_file=pathlib.Path(UpperCamelCase_		).parent.joinpath("""vocab/buckwalter.json"""		)			,       word_delimiter_token="""/"""			,       translation_table=str.maketrans(UpperCamelCase_		)			,       words_to_remove={"""sil"""}			,       untransliterator=arabic.buckwalter.untransliterate			,       )
						raise ValueError(F"""Unsupported orthography: \'{name}\'."""		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Any			,       __lowercase     :	str		):






						'''simple docstring'''







						if len(self.translation_table		) > 0:
							UpperCAmelCase_		    =  text.translate(self.translation_table		)
						if len(self.words_to_remove		) == 0:
							UpperCAmelCase_		    =  """ """.join(text.split()		)  # clean up whitespaces
						else:
							UpperCAmelCase_		    =  """ """.join(w for w in text.split() if w not in self.words_to_remove		)  # and clean up whilespaces
						return text





					def 							SCREAMING_SNAKE_CASE				(    self     :	Any			,       __lowercase     :	ModelArguments		):






						'''simple docstring'''







						UpperCAmelCase_		    =  WavaVecaFeatureExtractor.from_pretrained(
						    model_args.model_name_or_path			,       cache_dir=model_args.cache_dir		)
						if self.vocab_file:
							UpperCAmelCase_		    =  WavaVecaCTCTokenizer(
							    self.vocab_file			,       cache_dir=model_args.cache_dir			,       do_lower_case=self.do_lower_case			,       word_delimiter_token=self.word_delimiter_token			,       )
						else:
							UpperCAmelCase_		    =  WavaVecaCTCTokenizer.from_pretrained(
							    model_args.model_name_or_path			,       cache_dir=model_args.cache_dir			,       do_lower_case=self.do_lower_case			,       word_delimiter_token=self.word_delimiter_token			,       )
						return WavaVecaProcessor(UpperCamelCase_			,       UpperCamelCase_		)







@dataclass
class 	_UpperCamelCase	:







					'''simple docstring'''

					lowerCamelCase  :  WavaVecaProcessor
					lowerCamelCase  :  Union[bool, str]           =							True
					lowerCamelCase  :  Optional[int]           =							None
					lowerCamelCase  :  Optional[int]           =							None
					lowerCamelCase  :  Optional[int]           =							None
					lowerCamelCase  :  Optional[int]           =							None





					def __call__(    self     :	Union[str, Any]			,       __lowercase     :	List[Dict[str, Union[List[int], torch.Tensor]]]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  [{"""input_values""": feature["""input_values"""]} for feature in features]
						UpperCAmelCase_		    =  [{"""input_ids""": feature["""labels"""]} for feature in features]

						UpperCAmelCase_		    =  self.processor.pad(
						    UpperCamelCase_			,       padding=self.padding			,       max_length=self.max_length			,       pad_to_multiple_of=self.pad_to_multiple_of			,       return_tensors="""pt"""			,       )
						UpperCAmelCase_		    =  self.processor.pad(
						    labels=UpperCamelCase_			,       padding=self.padding			,       max_length=self.max_length_labels			,       pad_to_multiple_of=self.pad_to_multiple_of_labels			,       return_tensors="""pt"""			,       )

						# replace padding with -100 to ignore loss correctly
						UpperCAmelCase_		    =  labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1		)			,       -1_00		)

						UpperCAmelCase_		    =  labels

						return batch







class 	_UpperCamelCase	(							__lowerCamelCase	):







					'''simple docstring'''

					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]			,       __lowercase     :	nn.Module			,       __lowercase     :	Dict[str, Union[torch.Tensor, Any]]		):






						'''simple docstring'''







						model.train()
						UpperCAmelCase_		    =  self._prepare_inputs(UpperCamelCase_		)

						if self.use_amp:
							with autocast():
								UpperCAmelCase_		    =  self.compute_loss(UpperCamelCase_			,       UpperCamelCase_		)
						else:
							UpperCAmelCase_		    =  self.compute_loss(UpperCamelCase_			,       UpperCamelCase_		)

						if self.args.n_gpu > 1:
							if model.module.config.ctc_loss_reduction == "mean":
								UpperCAmelCase_		    =  loss.mean()
							elif model.module.config.ctc_loss_reduction == "sum":
								UpperCAmelCase_		    =  loss.sum() / (inputs["""labels"""] >= 0).sum()
							else:
								raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']"""		)

						if self.args.gradient_accumulation_steps > 1:
							UpperCAmelCase_		    =  loss / self.args.gradient_accumulation_steps

						if self.use_amp:
							self.scaler.scale(UpperCamelCase_		).backward()
						elif self.use_apex:
							with amp.scale_loss(UpperCamelCase_			,       self.optimizer		) as scaled_loss:
								scaled_loss.backward()
						elif self.deepspeed:
							self.deepspeed.backward(UpperCamelCase_		)
						else:
							loss.backward()

						return loss.detach()




def  A_(      ):
	UpperCAmelCase_		    =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

	UpperCAmelCase_    ,						UpperCAmelCase_    ,						UpperCAmelCase_		    =  parser.parse_args_into_dataclasses()
	configure_logger(A__					,   A__ )

	UpperCAmelCase_		    =  Orthography.from_name(data_args.orthography.lower() )
	UpperCAmelCase_		    =  orthography.create_processor(A__ )
	UpperCAmelCase_		    =  WavaVecaForCTC.from_pretrained(
	    model_args.model_name_or_path					,   cache_dir=model_args.cache_dir					,   gradient_checkpointing=training_args.gradient_checkpointing					,   vocab_size=len(processor.tokenizer )					,   )

	UpperCAmelCase_		    =  datasets.load_dataset(
	    data_args.dataset_name					,   data_args.dataset_config_name					,   split=data_args.train_split_name )
	UpperCAmelCase_		    =  datasets.load_dataset(
	    data_args.dataset_name					,   data_args.dataset_config_name					,   split=data_args.validation_split_name )

	UpperCAmelCase_		    =  datasets.load_metric("""wer""" )
	UpperCAmelCase_		    =  processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
	UpperCAmelCase_		    =  """""".join(t for t in processor.tokenizer.get_vocab().keys() if len(A__ ) == 1 )
	UpperCAmelCase_		    =  re.compile(  # remove characters not in vocabulary
	    Rf"""[^\s{re.escape(A__ )}]"""					,   flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0					,   )
	UpperCAmelCase_		    =  []

	def prepare_example(A ):  # TODO(elgeish) make use of multiprocessing?
		UpperCAmelCase_    ,						UpperCAmelCase_		    =  librosa.load(example[data_args.speech_file_column]					,   sr=A__ )
		if data_args.max_duration_in_seconds is not None:
			UpperCAmelCase_		    =  len(example["""speech"""] ) / example["""sampling_rate"""]
		# Normalize and clean up text; order matters!
		UpperCAmelCase_		    =  orthography.preprocess_for_training(example[data_args.target_text_column] )
		UpperCAmelCase_		    =  vocabulary_text_cleaner.sub(""""""					,   A__ )
		if updated_text != example[data_args.target_text_column]:
			text_updates.append((example[data_args.target_text_column], updated_text) )
			UpperCAmelCase_		    =  updated_text
		return example

	UpperCAmelCase_		    =  train_dataset.map(A__					,   remove_columns=[data_args.speech_file_column] )
	UpperCAmelCase_		    =  val_dataset.map(A__					,   remove_columns=[data_args.speech_file_column] )

	if data_args.max_duration_in_seconds is not None:

		def filter_by_max_duration(A ):
			return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

		UpperCAmelCase_		    =  len(A__ )
		UpperCAmelCase_		    =  len(A__ )
		UpperCAmelCase_		    =  train_dataset.filter(A__					,   remove_columns=["""duration_in_seconds"""] )
		UpperCAmelCase_		    =  val_dataset.filter(A__					,   remove_columns=["""duration_in_seconds"""] )
		if len(A__ ) > old_train_size:
			logger.warning(
			    f"""Filtered out {len(A__ ) - old_train_size} train example(s) longer than"""
			    f""" {data_args.max_duration_in_seconds} second(s).""" )
		if len(A__ ) > old_val_size:
			logger.warning(
			    f"""Filtered out {len(A__ ) - old_val_size} validation example(s) longer than"""
			    f""" {data_args.max_duration_in_seconds} second(s).""" )
	logger.info(f"""Split sizes: {len(A__ )} train and {len(A__ )} validation.""" )

	logger.warning(f"""Updated {len(A__ )} transcript(s) using \'{data_args.orthography}\' orthography rules.""" )
	if logger.isEnabledFor(logging.DEBUG ):
		for original_text, updated_text in text_updates:
			logger.debug(f"""Updated text: \"{original_text}\" -> \"{updated_text}\"""" )
	UpperCAmelCase_		    =  None

	def prepare_dataset(A ):
		# check that all files have the correct sampling rate
		assert (
		    len(set(batch["""sampling_rate"""] ) ) == 1
		), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""

		UpperCAmelCase_		    =  processor(
		    audio=batch["""speech"""]					,   text=batch[data_args.target_text_column]					,   sampling_rate=batch["""sampling_rate"""][0] )
		batch.update(A__ )
		return batch

	UpperCAmelCase_		    =  train_dataset.map(
	    A__					,   batch_size=training_args.per_device_train_batch_size					,   batched=A__					,   num_proc=data_args.preprocessing_num_workers					,   )
	UpperCAmelCase_		    =  val_dataset.map(
	    A__					,   batch_size=training_args.per_device_train_batch_size					,   batched=A__					,   num_proc=data_args.preprocessing_num_workers					,   )

	UpperCAmelCase_		    =  DataCollatorCTCWithPadding(processor=A__					,   padding=A__ )

	def compute_metrics(A ):
		UpperCAmelCase_		    =  pred.predictions
		UpperCAmelCase_		    =  np.argmax(A__					,   axis=-1 )

		UpperCAmelCase_		    =  processor.tokenizer.pad_token_id

		UpperCAmelCase_		    =  processor.batch_decode(A__ )
		# we do not want to group tokens when computing the metrics
		UpperCAmelCase_		    =  processor.batch_decode(pred.label_ids					,   group_tokens=A__ )
		if logger.isEnabledFor(logging.DEBUG ):
			for reference, predicted in zip(A__					,   A__ ):
				logger.debug(f"""reference: \"{reference}\"""" )
				logger.debug(f"""predicted: \"{predicted}\"""" )
				if orthography.untransliterator is not None:
					logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(A__ )}\"""" )
					logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(A__ )}\"""" )

		UpperCAmelCase_		    =  wer_metric.compute(predictions=A__					,   references=A__ )

		return {"wer": wer}

	if model_args.freeze_feature_extractor:
		model.freeze_feature_extractor()

	UpperCAmelCase_		    =  CTCTrainer(
	    model=A__					,   data_collator=A__					,   args=A__					,   compute_metrics=A__					,   train_dataset=A__					,   eval_dataset=A__					,   tokenizer=processor.feature_extractor					,   )

	trainer.train()


if __name__ == "__main__":
				main()








import argparse
import json
import re
import string
import sys

import numpy as np


UpperCAmelCase_   							=      0.5




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Union[str, Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		[""".""", """,""", """;""", """:"""]

       for token in remove_tokens:
              __lowerCamelCase  =		ground_truth.replace(A__    ,     """"""      )
              __lowerCamelCase  =		prediction.replace(A__    ,     """"""      )

       __lowerCamelCase,  __lowerCamelCase  =		ground_truth.lower(), prediction.lower()
       __lowerCamelCase,  __lowerCamelCase  =		ground_truth.replace("""/"""    ,     """ """      ), prediction.replace("""/"""    ,     """ """      )
       __lowerCamelCase,  __lowerCamelCase  =		set(ground_truth.split(""" """      )      ), set(prediction.split(""" """      )      )

       __lowerCamelCase  =		ground_truth.intersection(A__      )
       __lowerCamelCase  =		ground_truth.union(A__      )
       __lowerCamelCase  =		len(A__      ) / len(A__      )
       return jaccard




def    lowerCamelCase__			(		A__			:   Optional[Any]      ):





       '''simple docstring'''



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

       def white_space_fix(A__			:   Optional[Any]      ):
              return " ".join(text.split()      )

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

       def lower(A__			:   Tuple      ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(A__      )      )      )      )




def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Optional[Any]    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		0, 0, 0

       __lowerCamelCase  =		"""Parties""" in qa_id

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

                     if not match_found:
                            fp += 1

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

       return precision, recall




def    lowerCamelCase__			(		A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		precisions[::-1]
       for i in range(1    ,     len(A__      )      ):
              __lowerCamelCase  =		max(precision_best[i - 1]    ,     precision_best[i]      )
       __lowerCamelCase  =		precision_best[::-1]
       return precisions




def    lowerCamelCase__			(		A__			:   str    ,     A__			:   Union[str, Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		process_precisions(A__      )
       __lowerCamelCase  =		np.trapz(A__    ,     A__      )
       if np.isnan(A__      ):
              return 0
       return aupr




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   List[str]    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		process_precisions(A__      )
       __lowerCamelCase  =		0
       for prec, recall in zip(A__    ,     A__      ):
              if recall >= recall_thresh:
                     __lowerCamelCase  =		prec
                     break
       return prec_at_recall




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   List[Any]      ):





       '''simple docstring'''



       return normalize_answer(A__      ) == normalize_answer(A__      )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   int    ,     A__			:   Dict      ):





       '''simple docstring'''



       __lowerCamelCase  =		0
       for pred in predictions:
              for ground_truth in ground_truths:
                     __lowerCamelCase  =		metric_fn(A__    ,     A__      )
                     if score == 1:  # break the loop when one prediction matches the ground truth
                            break
              if score == 1:
                     break
       return score




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



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

                            precisions.append(A__      )
                            recalls.append(A__      )

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

                            exact_match += metric_max_over_ground_truths(A__    ,     A__    ,     A__      )

       __lowerCamelCase  =		[x for _, x in sorted(zip(A__    ,     A__      )      )]
       recalls.sort()

       __lowerCamelCase  =		100.0 * fa / total
       __lowerCamelCase  =		100.0 * exact_match / total
       __lowerCamelCase  =		get_aupr(A__    ,     A__      )

       __lowerCamelCase  =		get_prec_at_recall(A__    ,     A__    ,     recall_thresh=0.9      )
       __lowerCamelCase  =		get_prec_at_recall(A__    ,     A__    ,     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_   							=      argparse.ArgumentParser(description='Evaluation for CUAD')
       parser.add_argument('dataset_file', help='Dataset file')
       parser.add_argument('prediction_file', help='Prediction File')
       UpperCAmelCase_   							=      parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              UpperCAmelCase_   							=      json.load(dataset_file)
              UpperCAmelCase_   							=      dataset_json['data']
       with open(args.prediction_file) as prediction_file:
              UpperCAmelCase_   							=      json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))

