





'''simple docstring'''


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_lowerCamelCase					:						Optional[int]					     =							logging.getLogger(__name__)



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     field(
		    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
		__lowerCAmelCase								=     field(default=A__		,       metadata={'''help''': '''Set this flag to use fast tokenization.'''}    )
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,       )






def  __a			(  )   ->Tuple:



	"""simple docstring"""







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

	if len(sys.argv     ) == 2 and sys.argv[1].endswith(""".json"""     ):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		A	,     A	,     A					=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
	else:
		A	,     A	,     A					=   parser.parse_args_into_dataclasses()

	if (
	    os.path.exists(training_args.output_dir     )
	    and os.listdir(training_args.output_dir     )
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
		    """ --overwrite_output_dir to overcome."""     )

	# Setup logging
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,      datefmt="""%m/%d/%Y %H:%M:%S"""							,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,      )
	logger.warning(
	    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""							,      training_args.local_rank							,      training_args.device							,      training_args.n_gpu							,      bool(training_args.local_rank != -1     )							,      training_args.fpaa							,      )
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank     ):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info("""Training/evaluation parameters %s"""							,      UpperCAmelCase     )

	# Prepare Question-Answering task
	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	A					=   AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,      cache_dir=model_args.cache_dir							,      )
	A					=   AutoTokenizer.from_pretrained(
	    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,      cache_dir=model_args.cache_dir							,      use_fast=UpperCAmelCase							,      )
	A					=   AutoModelForQuestionAnswering.from_pretrained(
	    model_args.model_name_or_path							,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path     )							,      config=UpperCAmelCase							,      cache_dir=model_args.cache_dir							,      )

	# Get datasets
	A					=   hasattr(model.config							,      """lang2id"""     )
	A					=   (
	    SquadDataset(
	        UpperCAmelCase							,      tokenizer=UpperCAmelCase							,      is_language_sensitive=UpperCAmelCase							,      cache_dir=model_args.cache_dir     )
	    if training_args.do_train
	    else None
	)
	A					=   (
	    SquadDataset(
	        UpperCAmelCase							,      tokenizer=UpperCAmelCase							,      mode="""dev"""							,      is_language_sensitive=UpperCAmelCase							,      cache_dir=model_args.cache_dir							,      )
	    if training_args.do_eval
	    else None
	)

	# Data collator
	A					=   DataCollatorWithPadding(UpperCAmelCase							,      pad_to_multiple_of=8     ) if training_args.fpaa else None

	# Initialize our Trainer
	A					=   Trainer(
	    model=UpperCAmelCase							,      args=UpperCAmelCase							,      train_dataset=UpperCAmelCase							,      eval_dataset=UpperCAmelCase							,      data_collator=UpperCAmelCase							,      )

	# Training
	if training_args.do_train:
		trainer.train(
		    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_master():
			tokenizer.save_pretrained(training_args.output_dir     )




def  __a			(  UpperCAmelCase     )   ->Any:



	"""simple docstring"""







	main()


if __name__ == "__main__":
				main()








'''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'):
				_lowerCamelCase					:						Optional[Any]					     =							True
				from torch.cuda.amp import autocast


_lowerCamelCase					:						List[Any]					     =							logging.getLogger(__name__)



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







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






def  __a			(  UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	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(UpperCAmelCase     )



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       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=A__		,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       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=A__		,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''The number of processes to use for the preprocessing.'''}		,       )






@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     "|"
		__lowerCAmelCase								=     field(default_factory=A__    )
		__lowerCAmelCase								=     field(default_factory=A__    )
		__lowerCAmelCase								=     None







		@classmethod
		def 							A  (cls    :   List[str]   ,   _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    :   Union[str, 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    :   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								=     42
		__lowerCAmelCase								=     True
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None







		def __call__(self    :   List[str]   ,   _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   )   ,   -100   )

			A					=   labels

			return batch






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def 							A  (self    :   str   ,   _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			(  )   ->Union[str, Any]:



	"""simple docstring"""







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

	A	,     A	,     A					=   parser.parse_args_into_dataclasses()
	configure_logger(UpperCAmelCase							,      UpperCAmelCase     )

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

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

	if data_args.max_duration_in_seconds is not None:

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

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

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

	A					=   train_dataset.map(
	    UpperCAmelCase							,      batch_size=training_args.per_device_train_batch_size							,      batched=UpperCAmelCase							,      num_proc=data_args.preprocessing_num_workers							,      )
	A					=   val_dataset.map(
	    UpperCAmelCase							,      batch_size=training_args.per_device_train_batch_size							,      batched=UpperCAmelCase							,      num_proc=data_args.preprocessing_num_workers							,      )

	A					=   DataCollatorCTCWithPadding(processor=UpperCAmelCase							,      padding=UpperCAmelCase     )

	def compute_metrics(UpperCAmelCase     ):
		A					=   pred.predictions
		A					=   np.argmax(UpperCAmelCase							,      axis=-1     )

		A					=   processor.tokenizer.pad_token_id

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

		A					=   wer_metric.compute(predictions=UpperCAmelCase							,      references=UpperCAmelCase     )

		return {"wer": wer}

	if model_args.freeze_feature_extractor:
		model.freeze_feature_extractor()

	A					=   CTCTrainer(
	    model=UpperCAmelCase							,      data_collator=UpperCAmelCase							,      args=UpperCAmelCase							,      compute_metrics=UpperCAmelCase							,      train_dataset=UpperCAmelCase							,      eval_dataset=UpperCAmelCase							,      tokenizer=processor.feature_extractor							,      )

	trainer.train()


if __name__ == "__main__":
				main()

