



"""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'):
 __snake_case	:				Optional[int]  					=			True
 from torch.cuda.amp import autocast


__snake_case	:				Any  					=			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=a      , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      , )
     _lowerCamelCase					: Optional[bool]				   =field(
         default=a      , metadata={"help": "Whether to freeze the feature extractor layers of the model."}				)
     _lowerCamelCase					: Optional[bool]				   =field(
         default=a      , 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      )]		,							)
     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(__a      )

@dataclass
class       UpperCamelCase  :





     """simple docstring"""






     _lowerCamelCase					: str				   =field(
         default=a      , metadata={"help": "The name of the dataset to use (via the datasets library)."}				)
     _lowerCamelCase					: Optional[str]				   =field(
         default=a      , 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=a      , metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."}      , )
     _lowerCamelCase					: Optional[float]				   =field(
         default=a      , 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=a      , metadata={"help": "Overwrite the cached preprocessed datasets or not."}				)
     _lowerCamelCase					: Optional[int]				   =field(
         default=a      , 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=a				)
     _lowerCamelCase					: Optional[Set[str]]				   =field(default_factory=a				)
     _lowerCamelCase					: Optional[Callable[[str], str]]				   =None





     @classmethod
     def 						A__  (					cls :							int	,      _lowerCamelCase :							str ):
          if name == "librispeech":
               return cls()
          if name == "timit":
               return cls(
                   do_lower_case=_lowerCamelCase	,      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(_lowerCamelCase ).parent.joinpath('''vocab/buckwalter.json''' )	,      word_delimiter_token='''/'''	,      translation_table=str.maketrans(_lowerCamelCase )	,      words_to_remove={'''sil'''}	,      untransliterator=arabic.buckwalter.untransliterate	,      )
          raise ValueError(F'''Unsupported orthography: \'{name}\'.''' )





     def 						A__  (					self :							Any	,      _lowerCamelCase :							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 						A__  (					self :							List[str]	,      _lowerCamelCase :							ModelArguments ):
          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(_lowerCamelCase	,      _lowerCamelCase )


@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 :							Dict	,      _lowerCamelCase :							List[Dict[str, Union[List[int], 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(
              _lowerCamelCase	,      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=_lowerCamelCase	,      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_0_0 )

          A__				=   labels

          return batch


class       UpperCamelCase  (					a				):





     """simple docstring"""






     def 						A__  (					self :							int	,      _lowerCamelCase :							nn.Module	,      _lowerCamelCase :							Dict[str, Union[torch.Tensor, Any]] ):
          model.train()
          A__				=   self._prepare_inputs(_lowerCamelCase )

          if self.use_amp:
               with autocast():
                    A__				=   self.compute_loss(_lowerCamelCase	,      _lowerCamelCase )
          else:
               A__				=   self.compute_loss(_lowerCamelCase	,      _lowerCamelCase )

          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(_lowerCamelCase ).backward()
          elif self.use_apex:
               with amp.scale_loss(_lowerCamelCase	,      self.optimizer ) as scaled_loss:
                    scaled_loss.backward()
          elif self.deepspeed:
               self.deepspeed.backward(_lowerCamelCase )
          else:
               loss.backward()

          return loss.detach()



def   a_	(			):
     # 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(__a		,							__a      )

     A__				=   Orthography.from_name(data_args.orthography.lower()      )
     A__				=   orthography.create_processor(__a      )
     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(__a      ) == 1      )
     A__				=   re.compile(  # remove characters not in vocabulary
         rf'''[^\s{re.escape(__a      )}]'''		,							flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0		,							)
     A__				=   []

     def prepare_example(__a      ):  # TODO(elgeish) make use of multiprocessing?
          A__	,     A__				=   librosa.load(example[data_args.speech_file_column]		,							sr=__a      )
          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(''''''		,							__a      )
          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(__a		,							remove_columns=[data_args.speech_file_column]      )
     A__				=   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

          A__				=   len(__a      )
          A__				=   len(__a      )
          A__				=   train_dataset.filter(__a		,							remove_columns=['''duration_in_seconds''']      )
          A__				=   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}"'''      )
     A__				=   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}.'''

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

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

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

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

          A__				=   processor.tokenizer.pad_token_id

          A__				=   processor.batch_decode(__a      )
          # we do not want to group tokens when computing the metrics
          A__				=   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      )}"'''      )

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

          return {"wer": wer}

     if model_args.freeze_feature_extractor:
          model.freeze_feature_extractor()

     A__				=   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()








"""simple docstring"""







import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__snake_case	:				List[str]  					=			logging.getLogger()



def   a_	(			):
     A__				=   argparse.ArgumentParser()
     parser.add_argument('''-f'''      )
     A__				=   parser.parse_args()
     return args.f

class       UpperCamelCase  (					a				):





     """simple docstring"""






     def 						A__  (					self :							Optional[int] ):
          A__				=   logging.StreamHandler(sys.stdout )
          logger.addHandler(_lowerCamelCase )

          A__				=   self.get_auto_remove_tmp_dir()
          A__				=   F'''
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

          with patch.object(_lowerCamelCase	,      '''argv'''	,      _lowerCamelCase ):
               A__				=   run_glue_with_pabee.main()
               for value in result.values():
                    self.assertGreaterEqual(_lowerCamelCase	,      0.75 )



