





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 							snake_case_		:
		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__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  str:
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''				,							datefmt='''%m/%d/%Y %H:%M:%S'''				,							handlers=[logging.StreamHandler(sys.stdout		)]				,							)
		lowerCamelCase :			Dict    		=logging.WARNING
		if model_args.verbose_logging:
				lowerCamelCase :			Dict    		=logging.DEBUG
		elif trainer_utils.is_main_process(training_args.local_rank		):
				lowerCamelCase :			List[Any]    		=logging.INFO
		logger.setLevel(SCREAMING_SNAKE_CASE_		)
@dataclass
class 							snake_case_		:
		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 							snake_case_		:
		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       __lowercase (		cls      ,  __lowercase )					-> Optional[int]:
				if name == "librispeech":
						return cls()
				if name == "timit":
						return cls(
						    do_lower_case=__lowercase      ,  translation_table=str.maketrans({'''-''': ''' '''} )      ,  )
				if name == "buckwalter":
						lowerCamelCase :			Tuple    		={
						    '''-''': ''' ''',  # sometimes used to represent pauses
						    '''^''': '''v''',  # fixing "tha" in arabic_speech_corpus dataset
						}
						return cls(
						    vocab_file=pathlib.Path(__lowercase ).parent.joinpath('''vocab/buckwalter.json''' )      ,  word_delimiter_token='''/'''      ,  translation_table=str.maketrans(__lowercase )      ,  words_to_remove={'''sil'''}      ,  untransliterator=arabic.buckwalter.untransliterate      ,  )
				raise ValueError(F"Unsupported orthography: '{name}'." )







		def       __lowercase (		self      ,  __lowercase )					-> str:
				# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
				if len(self.translation_table ) > 0:
						lowerCamelCase :			Optional[int]    		=text.translate(self.translation_table )
				if len(self.words_to_remove ) == 0:
						lowerCamelCase :			List[str]    		=''' '''.join(text.split() )  # clean up whitespaces
				else:
						lowerCamelCase :			Optional[int]    		=''' '''.join(w for w in text.split() if w not in self.words_to_remove )  # and clean up whilespaces
				return text







		def       __lowercase (		self      ,  __lowercase )					-> WavaVecaProcessor:
				lowerCamelCase :			List[str]    		=WavaVecaFeatureExtractor.from_pretrained(
				    model_args.model_name_or_path      ,  cache_dir=model_args.cache_dir )
				if self.vocab_file:
						lowerCamelCase :			int    		=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:
						lowerCamelCase :			List[Any]    		=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(__lowercase      ,  __lowercase )

@dataclass
class 							snake_case_		:
		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      ,  __lowercase )					-> Dict[str, torch.Tensor]:
				# split inputs and labels since they have to be of different lenghts and need
				# different padding methods
				lowerCamelCase :			Optional[Any]    		=[{'''input_values''': feature['''input_values''']} for feature in features]
				lowerCamelCase :			Optional[Any]    		=[{'''input_ids''': feature['''labels''']} for feature in features]

				lowerCamelCase :			List[Any]    		=self.processor.pad(
				    __lowercase      ,  padding=self.padding      ,  max_length=self.max_length      ,  pad_to_multiple_of=self.pad_to_multiple_of      ,  return_tensors='''pt'''      ,  )
				lowerCamelCase :			int    		=self.processor.pad(
				    labels=__lowercase      ,  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
				lowerCamelCase :			int    		=labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 )      ,  -1_0_0 )

				lowerCamelCase :			Optional[Any]    		=labels

				return batch

class 							snake_case_		(      _A):







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase )					-> torch.Tensor:
				model.train()
				lowerCamelCase :			List[str]    		=self._prepare_inputs(__lowercase )

				if self.use_amp:
						with autocast():
								lowerCamelCase :			Optional[int]    		=self.compute_loss(__lowercase      ,  __lowercase )
				else:
						lowerCamelCase :			Optional[int]    		=self.compute_loss(__lowercase      ,  __lowercase )

				if self.args.n_gpu > 1:
						if model.module.config.ctc_loss_reduction == "mean":
								lowerCamelCase :			Union[str, Any]    		=loss.mean()
						elif model.module.config.ctc_loss_reduction == "sum":
								lowerCamelCase :			int    		=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:
						lowerCamelCase :			Tuple    		=loss / self.args.gradient_accumulation_steps

				if self.use_amp:
						self.scaler.scale(__lowercase ).backward()
				elif self.use_apex:
						with amp.scale_loss(__lowercase      ,  self.optimizer ) as scaled_loss:
								scaled_loss.backward()
				elif self.deepspeed:
						self.deepspeed.backward(__lowercase )
				else:
						loss.backward()

				return loss.detach()


def        A__						(							) ->  Union[str, Any]:
		# 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.

		lowerCamelCase :			Dict    		=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)

		lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Optional[int]    		=parser.parse_args_into_dataclasses()
		configure_logger(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[str]    		=Orthography.from_name(data_args.orthography.lower()		)
		lowerCamelCase :			List[str]    		=orthography.create_processor(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[Any]    		=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		)				,							)

		lowerCamelCase :			Tuple    		=datasets.load_dataset(
		    data_args.dataset_name				,							data_args.dataset_config_name				,							split=data_args.train_split_name		)
		lowerCamelCase :			str    		=datasets.load_dataset(
		    data_args.dataset_name				,							data_args.dataset_config_name				,							split=data_args.validation_split_name		)

		lowerCamelCase :			Dict    		=datasets.load_metric('''wer'''		)
		lowerCamelCase :			str    		=processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
		lowerCamelCase :			str    		=''''''.join(t for t in processor.tokenizer.get_vocab().keys() if len(SCREAMING_SNAKE_CASE_		) == 1		)
		lowerCamelCase :			Any    		=re.compile(  # remove characters not in vocabulary
		    RF"[^\s{re.escape(SCREAMING_SNAKE_CASE_		)}]"				,							flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0				,							)
		lowerCamelCase :			Optional[Any]    		=[]

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

		lowerCamelCase :			Optional[Any]    		=train_dataset.map(SCREAMING_SNAKE_CASE_				,							remove_columns=[data_args.speech_file_column]		)
		lowerCamelCase :			List[Any]    		=val_dataset.map(SCREAMING_SNAKE_CASE_				,							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

				lowerCamelCase :			Any    		=len(SCREAMING_SNAKE_CASE_		)
				lowerCamelCase :			Tuple    		=len(SCREAMING_SNAKE_CASE_		)
				lowerCamelCase :			Dict    		=train_dataset.filter(SCREAMING_SNAKE_CASE_				,							remove_columns=['''duration_in_seconds''']		)
				lowerCamelCase :			int    		=val_dataset.filter(SCREAMING_SNAKE_CASE_				,							remove_columns=['''duration_in_seconds''']		)
				if len(SCREAMING_SNAKE_CASE_		) > old_train_size:
						logger.warning(
						    F"Filtered out {len(SCREAMING_SNAKE_CASE_		) - old_train_size} train example(s) longer than"
						    F" {data_args.max_duration_in_seconds} second(s)."		)
				if len(SCREAMING_SNAKE_CASE_		) > old_val_size:
						logger.warning(
						    F"Filtered out {len(SCREAMING_SNAKE_CASE_		) - old_val_size} validation example(s) longer than"
						    F" {data_args.max_duration_in_seconds} second(s)."		)
		logger.info(F"Split sizes: {len(SCREAMING_SNAKE_CASE_		)} train and {len(SCREAMING_SNAKE_CASE_		)} validation."		)

		logger.warning(F"Updated {len(SCREAMING_SNAKE_CASE_		)} 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}\""		)
		lowerCamelCase :			Dict    		=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}."

				lowerCamelCase :			Dict    		=processor(
				    audio=batch['''speech''']				,							text=batch[data_args.target_text_column]				,							sampling_rate=batch['''sampling_rate'''][0]		)
				batch.update(SCREAMING_SNAKE_CASE_		)
				return batch

		lowerCamelCase :			List[Any]    		=train_dataset.map(
		    SCREAMING_SNAKE_CASE_				,							batch_size=training_args.per_device_train_batch_size				,							batched=SCREAMING_SNAKE_CASE_				,							num_proc=data_args.preprocessing_num_workers				,							)
		lowerCamelCase :			Union[str, Any]    		=val_dataset.map(
		    SCREAMING_SNAKE_CASE_				,							batch_size=training_args.per_device_train_batch_size				,							batched=SCREAMING_SNAKE_CASE_				,							num_proc=data_args.preprocessing_num_workers				,							)

		lowerCamelCase :			str    		=DataCollatorCTCWithPadding(processor=SCREAMING_SNAKE_CASE_				,							padding=SCREAMING_SNAKE_CASE_		)

		def compute_metrics(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase :			Union[str, Any]    		=pred.predictions
				lowerCamelCase :			Optional[Any]    		=np.argmax(SCREAMING_SNAKE_CASE_				,							axis=-1		)

				lowerCamelCase :			Optional[Any]    		=processor.tokenizer.pad_token_id

				lowerCamelCase :			Optional[Any]    		=processor.batch_decode(SCREAMING_SNAKE_CASE_		)
				# we do not want to group tokens when computing the metrics
				lowerCamelCase :			Optional[int]    		=processor.batch_decode(pred.label_ids				,							group_tokens=SCREAMING_SNAKE_CASE_		)
				if logger.isEnabledFor(logging.DEBUG		):
						for reference, predicted in zip(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		):
								logger.debug(F"reference: \"{reference}\""		)
								logger.debug(F"predicted: \"{predicted}\""		)
								if orthography.untransliterator is not None:
										logger.debug(F"reference (untransliterated): \"{orthography.untransliterator(SCREAMING_SNAKE_CASE_		)}\""		)
										logger.debug(F"predicted (untransliterated): \"{orthography.untransliterator(SCREAMING_SNAKE_CASE_		)}\""		)

				lowerCamelCase :			Dict    		=wer_metric.compute(predictions=SCREAMING_SNAKE_CASE_				,							references=SCREAMING_SNAKE_CASE_		)

				return {"wer": wer}

		if model_args.freeze_feature_extractor:
				model.freeze_feature_extractor()

		lowerCamelCase :			Union[str, Any]    		=CTCTrainer(
		    model=SCREAMING_SNAKE_CASE_				,							data_collator=SCREAMING_SNAKE_CASE_				,							args=SCREAMING_SNAKE_CASE_				,							compute_metrics=SCREAMING_SNAKE_CASE_				,							train_dataset=SCREAMING_SNAKE_CASE_				,							eval_dataset=SCREAMING_SNAKE_CASE_				,							tokenizer=processor.feature_extractor				,							)

		trainer.train()


if __name__ == "__main__":
				main()








import sys

from transformers import AutoTokenizer


snake_case_				=      sys.argv[1]
snake_case_				=      sys.argv[2]
snake_case_				=      int(sys.argv[3])

snake_case_				=      0

snake_case_				=      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
				for line in f_p:
								snake_case_				=      line.rstrip()

								if not line:
												print(line)
												snake_case_				=      0
												continue

								snake_case_				=      line.split()[0]

								snake_case_				=      len(tokenizer.tokenize(token))

								# Token contains strange control characters like \x96 or \x95
								# Just filter out the complete line
								if current_subwords_len == 0:
												continue

								if (subword_len_counter + current_subwords_len) > max_len:
												print('''''')
												print(line)
												snake_case_				=      current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

