'''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       :   Dict  		=    True
      from torch.cuda.amp import autocast


__snake_case       :   List[Any]  		=    logging.getLogger(__name__)
@dataclass
class 							lowercase_    :
    a_             =   field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
    a_             =   field(
        default=_A ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,  )
    a_             =   field(
        default=_A ,  metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}						)
    a_             =   field(
        default=_A ,  metadata={"""help""": """Whether to log verbose messages or not."""} ,  )



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


@dataclass
class 							lowercase_    :
    a_             =   False
    a_             =   None
    a_             =   "|"
    a_             =   field(default_factory=_A						)
    a_             =   field(default_factory=_A						)
    a_             =   None







    @classmethod
    def   lowerCamelCase_    (	cls       ,     UpperCamelCase__				)				->       Union[str, Any]:





     """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   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       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   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       WavaVecaProcessor:





     """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 							lowercase_    :
    a_             =   42
    a_             =   True
    a_             =   None
    a_             =   None
    a_             =   None
    a_             =   None







    def __call__(	self       ,     UpperCamelCase__				)				->       Dict[str, 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_0_0				)

     UpperCAmelCase_				       =					labels

     return batch


class 							lowercase_    (     _A						):







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       torch.Tensor:





     """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 lowerCamelCase__						(       ):
 # 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.

 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()

'''simple docstring'''

from ..utils import DummyObject, requires_backends
class 							lowercase_    (     metaclass=_A						):
    a_             =   ["""torch""", """librosa"""]







    def __init__(	self       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       int:





     """simple docstring"""

     requires_backends(self       ,     ["torch", "librosa"]				)







    @classmethod
    def   lowerCamelCase_    (	cls       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       Union[str, Any]:





     """simple docstring"""

     requires_backends(cls       ,     ["torch", "librosa"]				)







    @classmethod
    def   lowerCamelCase_    (	cls       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       Dict:





     """simple docstring"""

     requires_backends(cls       ,     ["torch", "librosa"]				)


class 							lowercase_    (     metaclass=_A						):
    a_             =   ["""torch""", """librosa"""]







    def __init__(	self       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       str:





     """simple docstring"""

     requires_backends(self       ,     ["torch", "librosa"]				)







    @classmethod
    def   lowerCamelCase_    (	cls       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       Union[str, Any]:





     """simple docstring"""

     requires_backends(cls       ,     ["torch", "librosa"]				)







    @classmethod
    def   lowerCamelCase_    (	cls       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       int:





     """simple docstring"""

     requires_backends(cls       ,     ["torch", "librosa"]				)
