




"""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"):
      SCREAMING_SNAKE_CASE       = True
      from torch.cuda.amp import autocast


SCREAMING_SNAKE_CASE       = logging.getLogger(__name__)




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







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	List[Any]:
 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(lowercase_		)




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






@dataclass
class 			UpperCAmelCase_						:
    lowercase__		      =   False
    lowercase__		      =   None
    lowercase__		      =   "|"
    lowercase__		      =   field(default_factory=A_							)
    lowercase__		      =   field(default_factory=A_							)
    lowercase__		      =   None




    @classmethod
    def 							__magic_name__							(					cls		: Optional[int]			,				snake_case_		: str   )   ->							Optional[int]:




     '''simple docstring'''

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




    def 							__magic_name__							(					self		: Dict			,				snake_case_		: str   )   ->							str:




     '''simple docstring'''

     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 							__magic_name__							(					self		: Optional[Any]			,				snake_case_		: ModelArguments   )   ->							WavaVecaProcessor:




     '''simple docstring'''

     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(snake_case_			,				snake_case_   )






@dataclass
class 			UpperCAmelCase_						:
    lowercase__		      =   42
    lowercase__		      =   True
    lowercase__		      =   None
    lowercase__		      =   None
    lowercase__		      =   None
    lowercase__		      =   None




    def __call__(					self		: Optional[int]			,				snake_case_		: List[Dict[str, Union[List[int], torch.Tensor]]]   )   ->							Dict[str, torch.Tensor]:




     '''simple docstring'''

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

     A__ =       self.processor.pad(
         snake_case_			,				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=snake_case_			,				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   )			,				-100   )

     A__ =       labels

     return batch






class 			UpperCAmelCase_						(		A_							):




    def 							__magic_name__							(					self		: Tuple			,				snake_case_		: nn.Module			,				snake_case_		: Dict[str, Union[torch.Tensor, Any]]   )   ->							torch.Tensor:




     '''simple docstring'''

     model.train()
     A__ =       self._prepare_inputs(snake_case_   )

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

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

     return loss.detach()







def 						_SCREAMING_SNAKE_CASE     (							)		->	str:
 # 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(lowercase_    ,				lowercase_		)

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

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

 if data_args.max_duration_in_seconds is not None:

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

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

 logger.warning(f"""Updated {len(lowercase_		)} 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(lowercase_		):
  # 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(lowercase_		)
  return batch

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

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

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

  A__ =       processor.tokenizer.pad_token_id

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

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

  return {"wer": wer}

 if model_args.freeze_feature_extractor:
  model.freeze_feature_extractor()

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

 trainer.train()


if __name__ == "__main__":
      main()











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE       = {
    "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE       = ["DetaImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE       = [
          "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DetaForObjectDetection",
          "DetaModel",
          "DetaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_deta import DetaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deta import (
                DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                DetaForObjectDetection,
                DetaModel,
                DetaPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





