


'''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				:							List[Any]		       =		True
							from torch.cuda.amp import autocast


__SCREAMING_SNAKE_CASE				:							Optional[Any]		       =		logging.getLogger(__name__)






@dataclass
class 				__lowerCAmelCase      :

						"""simple docstring"""






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







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							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 				__lowerCAmelCase      :

						"""simple docstring"""






						_UpperCAmelCase		:					str      	=field(
						    default=lowercase							, metadata={"help": "The name of the dataset to use (via the datasets library)."}	)
						_UpperCAmelCase		:					Optional[str]      	=field(
						    default=lowercase							, 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=lowercase							, metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."}							, )
						_UpperCAmelCase		:					Optional[float]      	=field(
						    default=lowercase							, 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=lowercase							, metadata={"help": "Overwrite the cached preprocessed datasets or not."}	)
						_UpperCAmelCase		:					Optional[int]      	=field(
						    default=lowercase							, metadata={"help": "The number of processes to use for the preprocessing."}							, )






@dataclass
class 				__lowerCAmelCase      :

						"""simple docstring"""






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






						@classmethod
						def 							_UpperCAmelCase					(     cls       :			Optional[Any]   ,     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 							_UpperCAmelCase					(     self       :			str   ,     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 							_UpperCAmelCase					(     self       :			Any   ,     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 				__lowerCAmelCase      :

						"""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       :			Optional[Any]   ,     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  )   ,     -1_00  )

													A_ 						=		labels

													return batch






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[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_							(  ):
							# 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(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()









'''simple docstring'''
def 			a_							(  UpperCamelCase_   ):
							if not isinstance(UpperCamelCase_	,     UpperCamelCase_   ):
														A_ 						=		f"Input value of [number={number}] must be an integer"
														raise TypeError(UpperCamelCase_   )
							if number < 0 or number % 2 == 1:
														return False
							A_ 						=		int(number**0.5   )
							return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





