'''simple docstring'''






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						:    int     					=					logging.getLogger(__name__)







@dataclass
class lowercase_					:







			"""simple docstring"""

			__lowerCAmelCase    						=      field(
			    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
			__lowerCAmelCase    						=      field(
			    default=_UpperCamelCase						,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}						,						)
			__lowerCAmelCase    						=      field(
			    default=_UpperCamelCase						,						metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
			__lowerCAmelCase    						=      field(
			    default=_UpperCamelCase						,						metadata={"help": "Whether to log verbose messages or not."}						,						)






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







@dataclass
class lowercase_					:







			"""simple docstring"""

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







@dataclass
class lowercase_					:







			"""simple docstring"""

			__lowerCAmelCase    						=      False
			__lowerCAmelCase    						=      None
			__lowerCAmelCase    						=      "|"
			__lowerCAmelCase    						=      field(default_factory=_UpperCamelCase )
			__lowerCAmelCase    						=      field(default_factory=_UpperCamelCase )
			__lowerCAmelCase    						=      None
			@classmethod
			def   __UpperCAmelCase      (			cls			: Tuple,   UpperCamelCase__			: str    )     -> str:
								if name == "librispeech":
													return cls()
								if name == "timit":
													return cls(
													    do_lower_case=UpperCamelCase__,   translation_table=str.maketrans({'-': ' '}    ),   )
								if name == "buckwalter":
													_A								=     {
													    '-': ' ',  # 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   __UpperCAmelCase      (			self			: Optional[int],   UpperCamelCase__			: str    )     -> str:
								# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
								if len(self.translation_table    ) > 0:
													_A								=     text.translate(self.translation_table    )
								if len(self.words_to_remove    ) == 0:
													_A								=     ' '.join(text.split()    )  # clean up whitespaces
								else:
													_A								=     ' '.join(w for w in text.split() if w not in self.words_to_remove    )  # and clean up whilespaces
								return text

			def   __UpperCAmelCase      (			self			: Optional[Any],   UpperCamelCase__			: ModelArguments    )     -> WavaVecaProcessor:
								_A								=     WavaVecaFeatureExtractor.from_pretrained(
								    model_args.model_name_or_path,   cache_dir=model_args.cache_dir    )
								if self.vocab_file:
													_A								=     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:
													_A								=     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 lowercase_					:







			"""simple docstring"""

			__lowerCAmelCase    						=      4_2
			__lowerCAmelCase    						=      True
			__lowerCAmelCase    						=      None
			__lowerCAmelCase    						=      None
			__lowerCAmelCase    						=      None
			__lowerCAmelCase    						=      None
			def __call__(			self			: Tuple,   UpperCamelCase__			: List[Dict[str, Union[List[int], torch.Tensor]]]    )     -> Dict[str, torch.Tensor]:
								# split inputs and labels since they have to be of different lenghts and need
								# different padding methods
								_A								=     [{'input_values': feature['input_values']} for feature in features]
								_A								=     [{'input_ids': feature['labels']} for feature in features]

								_A								=     self.processor.pad(
								    UpperCamelCase__,   padding=self.padding,   max_length=self.max_length,   pad_to_multiple_of=self.pad_to_multiple_of,   return_tensors='pt',   )
								_A								=     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
								_A								=     labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1    ),   -1_00    )

								_A								=     labels

								return batch







class lowercase_					(		_UpperCamelCase ):







			"""simple docstring"""

			def   __UpperCAmelCase      (			self			: Optional[Any],   UpperCamelCase__			: nn.Module,   UpperCamelCase__			: Dict[str, Union[torch.Tensor, Any]]    )     -> torch.Tensor:
								model.train()
								_A								=     self._prepare_inputs(UpperCamelCase__    )

								if self.use_amp:
													with autocast():
																		_A								=     self.compute_loss(UpperCamelCase__,   UpperCamelCase__    )
								else:
													_A								=     self.compute_loss(UpperCamelCase__,   UpperCamelCase__    )

								if self.args.n_gpu > 1:
													if model.module.config.ctc_loss_reduction == "mean":
																		_A								=     loss.mean()
													elif model.module.config.ctc_loss_reduction == "sum":
																		_A								=     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:
													_A								=     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    _SCREAMING_SNAKE_CASE		(			):
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					_A								=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)

					_A		,					_A		,					_A								=     parser.parse_args_into_dataclasses()
					configure_logger(__snake_case      ,		__snake_case							)

					_A								=     Orthography.from_name(data_args.orthography.lower()							)
					_A								=     orthography.create_processor(__snake_case							)
					_A								=     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							)      ,		)

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

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

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

					_A								=     train_dataset.map(__snake_case      ,		remove_columns=[data_args.speech_file_column]							)
					_A								=     val_dataset.map(__snake_case      ,		remove_columns=[data_args.speech_file_column]							)

					if data_args.max_duration_in_seconds is not None:

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

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

					logger.warning(F'Updated {len(__snake_case							)} 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}"'							)
					_A								=     None

					def prepare_dataset(__snake_case       : List[str]							):
										# 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}.'

										_A								=     processor(
										    audio=batch['speech']      ,		text=batch[data_args.target_text_column]      ,		sampling_rate=batch['sampling_rate'][0]							)
										batch.update(__snake_case							)
										return batch

					_A								=     train_dataset.map(
					    __snake_case      ,		batch_size=training_args.per_device_train_batch_size      ,		batched=__snake_case      ,		num_proc=data_args.preprocessing_num_workers      ,		)
					_A								=     val_dataset.map(
					    __snake_case      ,		batch_size=training_args.per_device_train_batch_size      ,		batched=__snake_case      ,		num_proc=data_args.preprocessing_num_workers      ,		)

					_A								=     DataCollatorCTCWithPadding(processor=__snake_case      ,		padding=__snake_case							)

					def compute_metrics(__snake_case       : Any							):
										_A								=     pred.predictions
										_A								=     np.argmax(__snake_case      ,		axis=-1							)

										_A								=     processor.tokenizer.pad_token_id

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

										_A								=     wer_metric.compute(predictions=__snake_case      ,		references=__snake_case							)

										return {"wer": wer}

					if model_args.freeze_feature_extractor:
										model.freeze_feature_extractor()

					_A								=     CTCTrainer(
					    model=__snake_case      ,		data_collator=__snake_case      ,		args=__snake_case      ,		compute_metrics=__snake_case      ,		train_dataset=__snake_case      ,		eval_dataset=__snake_case      ,		tokenizer=processor.feature_extractor      ,		)

					trainer.train()


if __name__ == "__main__":
			main()




'''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)))



