




"""simple docstring"""




def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	if len(lowerCamelCase_							) < len(lowerCamelCase_							):
		return levenshtein_distance(lowerCamelCase_					,     lowerCamelCase_							)

	if len(lowerCamelCase_							) == 0:
		return len(lowerCamelCase_							)

	snake_case_		   =					list(range(len(lowerCamelCase_							) + 1							)							)

	for i, ca in enumerate(lowerCamelCase_							):
		snake_case_		   =					[i + 1]

		for j, ca in enumerate(lowerCamelCase_							):
			# Calculate insertions, deletions and substitutions
			snake_case_		   =					previous_row[j + 1] + 1
			snake_case_		   =					current_row[j] + 1
			snake_case_		   =					previous_row[j] + (ca != ca)

			# Get the minimum to append to the current row
			current_row.append(min(lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_							)							)

		# Store the previous row
		snake_case_		   =					current_row

	# Returns the last element (distance)
	return previous_row[-1]


if __name__ == "__main__":
		lowercase__    :					int					=						input("Enter the first word:\n").strip()
		lowercase__    :					Optional[Any]					=						input("Enter the second word:\n").strip()

		lowercase__    :					List[Any]					=						levenshtein_distance(first_word, second_word)
		print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')






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


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







def 						lowerCamelCase__   (		_A					,     _A							):







	'''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							)]					,     )
	snake_case_		   =					logging.WARNING
	if model_args.verbose_logging:
		snake_case_		   =					logging.DEBUG
	elif trainer_utils.is_main_process(training_args.local_rank							):
		snake_case_		   =					logging.INFO
	logger.setLevel(_A							)
@dataclass
class 		UpperCAmelCase       :







						'''simple docstring'''





						lowerCAmelCase_							=       field(
						    default=UpperCAmelCase__	,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}      )
						lowerCAmelCase_							=       field(
						    default=UpperCAmelCase__	,       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=UpperCAmelCase__	,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}	,       )
						lowerCAmelCase_							=       field(
						    default=UpperCAmelCase__	,       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=UpperCAmelCase__	,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}      )
						lowerCAmelCase_							=       field(
						    default=UpperCAmelCase__	,       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=UpperCAmelCase__      )
						lowerCAmelCase_							=       field(default_factory=UpperCAmelCase__      )
						lowerCAmelCase_							=       None






						@classmethod
						def 	snake_case__						(						cls							:					Optional[int]	,     __lowercase							:					str						):






							"""simple docstring"""






							if name == "librispeech":
								return cls()
							if name == "timit":
								return cls(
								    do_lower_case=__lowercase	,     translation_table=str.maketrans({"-": " "}						)	,     )
							if name == "buckwalter":
								snake_case_		   =					{
								    "-": " ",  # 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 	snake_case__						(						self							:					str	,     __lowercase							:					str						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Dict	,     __lowercase							:					ModelArguments						):






							"""simple docstring"""






							snake_case_		   =					WavaVecaFeatureExtractor.from_pretrained(
							    model_args.model_name_or_path	,     cache_dir=model_args.cache_dir						)
							if self.vocab_file:
								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:
								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(__lowercase	,     __lowercase						)







@dataclass
class 		UpperCAmelCase       :







						'''simple docstring'''





						lowerCAmelCase_							=       42
						lowerCAmelCase_							=       True
						lowerCAmelCase_							=       None
						lowerCAmelCase_							=       None
						lowerCAmelCase_							=       None
						lowerCAmelCase_							=       None






						def __call__(						self							:					Any	,     __lowercase							:					List[Dict[str, Union[List[int], torch.Tensor]]]						):






							"""simple docstring"""






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

							snake_case_		   =					self.processor.pad(
							    __lowercase	,     padding=self.padding	,     max_length=self.max_length	,     pad_to_multiple_of=self.pad_to_multiple_of	,     return_tensors="pt"	,     )
							snake_case_		   =					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
							snake_case_		   =					labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1						)	,     -1_00						)

							snake_case_		   =					labels

							return batch







class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						def 	snake_case__						(						self							:					Dict	,     __lowercase							:					nn.Module	,     __lowercase							:					Dict[str, Union[torch.Tensor, Any]]						):






							"""simple docstring"""






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

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

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







	'''simple docstring'''





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

	snake_case_   ,				snake_case_   ,				snake_case_		   =					parser.parse_args_into_dataclasses()
	configure_logger(_A					,     _A							)

	snake_case_		   =					Orthography.from_name(data_args.orthography.lower()							)
	snake_case_		   =					orthography.create_processor(_A							)
	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							)					,     )

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

	snake_case_		   =					datasets.load_metric("wer"							)
	snake_case_		   =					processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
	snake_case_		   =					"".join(t for t in processor.tokenizer.get_vocab().keys() if len(_A							) == 1							)
	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					,     )
	snake_case_		   =					[]

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

	snake_case_		   =					train_dataset.map(_A					,     remove_columns=[data_args.speech_file_column]							)
	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(_A							):
			return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

		snake_case_		   =					len(_A							)
		snake_case_		   =					len(_A							)
		snake_case_		   =					train_dataset.filter(_A					,     remove_columns=["duration_in_seconds"]							)
		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}\""							)
	snake_case_		   =					None

	def prepare_dataset(_A							):
		# 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}."

		snake_case_		   =					processor(
		    audio=batch["speech"]					,     text=batch[data_args.target_text_column]					,     sampling_rate=batch["sampling_rate"][0]							)
		batch.update(_A							)
		return batch

	snake_case_		   =					train_dataset.map(
	    _A					,     batch_size=training_args.per_device_train_batch_size					,     batched=_A					,     num_proc=data_args.preprocessing_num_workers					,     )
	snake_case_		   =					val_dataset.map(
	    _A					,     batch_size=training_args.per_device_train_batch_size					,     batched=_A					,     num_proc=data_args.preprocessing_num_workers					,     )

	snake_case_		   =					DataCollatorCTCWithPadding(processor=_A					,     padding=_A							)

	def compute_metrics(_A							):
		snake_case_		   =					pred.predictions
		snake_case_		   =					np.argmax(_A					,     axis=-1							)

		snake_case_		   =					processor.tokenizer.pad_token_id

		snake_case_		   =					processor.batch_decode(_A							)
		# we do not want to group tokens when computing the metrics
		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							)}\""							)

		snake_case_		   =					wer_metric.compute(predictions=_A					,     references=_A							)

		return {"wer": wer}

	if model_args.freeze_feature_extractor:
		model.freeze_feature_extractor()

	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()
