
"""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					  =						True
	from torch.cuda.amp import autocast


snake_case					  =						logging.getLogger(__name__)




@dataclass
class 	UpperCamelCase      :

				"""simple docstring"""



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






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




@dataclass
class 	UpperCamelCase      :

				"""simple docstring"""



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






@dataclass
class 	UpperCamelCase      :

				"""simple docstring"""



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


				@classmethod
				def 				A   (							cls						,      lowercase__     )       ->       Optional[int]:
									"""simple docstring"""







									if name == "librispeech":
														return cls()
									if name == "timit":
														return cls(
														    do_lower_case=lowerCamelCase_						,      translation_table=str.maketrans({'-': ' '}     )						,      )
									if name == "buckwalter":
														SCREAMING_SNAKE_CASE           =      {
														    '-': ' ',  # 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						,      lowercase__     )       ->       Any:
									"""simple docstring"""







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




				def 				A   (							self						,      lowercase__     )       ->       Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      WavaVecaFeatureExtractor.from_pretrained(
									    model_args.model_name_or_path						,      cache_dir=model_args.cache_dir     )
									if self.vocab_file:
														SCREAMING_SNAKE_CASE           =      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:
														SCREAMING_SNAKE_CASE           =      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"""



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


				def __call__(							self						,      lowercase__     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      [{'input_values': feature['input_values']} for feature in features]
									SCREAMING_SNAKE_CASE           =      [{'input_ids': feature['labels']} for feature in features]

									SCREAMING_SNAKE_CASE           =      self.processor.pad(
									    lowerCamelCase_						,      padding=self.padding						,      max_length=self.max_length						,      pad_to_multiple_of=self.pad_to_multiple_of						,      return_tensors='pt'						,      )
									SCREAMING_SNAKE_CASE           =      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
									SCREAMING_SNAKE_CASE           =      labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1     )						,      -100     )

									SCREAMING_SNAKE_CASE           =      labels

									return batch






class 	UpperCamelCase      (			lowerCAmelCase_ ):

				"""simple docstring"""



				def 				A   (							self						,      lowercase__						,      lowercase__     )       ->       Tuple:
									"""simple docstring"""







									model.train()
									SCREAMING_SNAKE_CASE           =      self._prepare_inputs(lowerCamelCase_     )

									if self.use_amp:
														with autocast():
																			SCREAMING_SNAKE_CASE           =      self.compute_loss(lowerCamelCase_						,      lowerCamelCase_     )
									else:
														SCREAMING_SNAKE_CASE           =      self.compute_loss(lowerCamelCase_						,      lowerCamelCase_     )

									if self.args.n_gpu > 1:
														if model.module.config.ctc_loss_reduction == "mean":
																			SCREAMING_SNAKE_CASE           =      loss.mean()
														elif model.module.config.ctc_loss_reduction == "sum":
																			SCREAMING_SNAKE_CASE           =      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:
														SCREAMING_SNAKE_CASE           =      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     UpperCamelCase_    (						):
					# 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.

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

					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      parser.parse_args_into_dataclasses()
					configure_logger(a_,	a_     )

					SCREAMING_SNAKE_CASE           =      Orthography.from_name(data_args.orthography.lower()     )
					SCREAMING_SNAKE_CASE           =      orthography.create_processor(a_     )
					SCREAMING_SNAKE_CASE           =      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     ),	)

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

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

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

					SCREAMING_SNAKE_CASE           =      train_dataset.map(a_,	remove_columns=[data_args.speech_file_column]     )
					SCREAMING_SNAKE_CASE           =      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(SCREAMING_SNAKE_CASE_     ):
															return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

										SCREAMING_SNAKE_CASE           =      len(a_     )
										SCREAMING_SNAKE_CASE           =      len(a_     )
										SCREAMING_SNAKE_CASE           =      train_dataset.filter(a_,	remove_columns=['duration_in_seconds']     )
										SCREAMING_SNAKE_CASE           =      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}"'''     )
					SCREAMING_SNAKE_CASE           =      None

					def prepare_dataset(SCREAMING_SNAKE_CASE_     ):
										# 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}.'''

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

					SCREAMING_SNAKE_CASE           =      train_dataset.map(
					    a_,	batch_size=training_args.per_device_train_batch_size,	batched=a_,	num_proc=data_args.preprocessing_num_workers,	)
					SCREAMING_SNAKE_CASE           =      val_dataset.map(
					    a_,	batch_size=training_args.per_device_train_batch_size,	batched=a_,	num_proc=data_args.preprocessing_num_workers,	)

					SCREAMING_SNAKE_CASE           =      DataCollatorCTCWithPadding(processor=a_,	padding=a_     )

					def compute_metrics(SCREAMING_SNAKE_CASE_     ):
										SCREAMING_SNAKE_CASE           =      pred.predictions
										SCREAMING_SNAKE_CASE           =      np.argmax(a_,	axis=-1     )

										SCREAMING_SNAKE_CASE           =      processor.tokenizer.pad_token_id

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

										SCREAMING_SNAKE_CASE           =      wer_metric.compute(predictions=a_,	references=a_     )

										return {"wer": wer}

					if model_args.freeze_feature_extractor:
										model.freeze_feature_extractor()

					SCREAMING_SNAKE_CASE           =      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 typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_  					= {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= ['DetrFeatureExtractor']
  SCREAMING_SNAKE_CASE_  					= ['DetrImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= [
      'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DetrForObjectDetection',
      'DetrForSegmentation',
      'DetrModel',
      'DetrPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_detr import DetrFeatureExtractor
    from .image_processing_detr import DetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_detr import (
        DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        DetrForObjectDetection,
        DetrForSegmentation,
        DetrModel,
        DetrPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE_  					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

