





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


_lowerCamelCase				:	Optional[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    )      ->List[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						:      Optional[int]      ,      _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						:      List[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        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						:      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      )      ,      -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 (     )      ->List[str]:



	"""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()













'''simple docstring'''


_lowerCamelCase				:	List[str]				    =       [[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class 							__UpperCAmelCase  :







		'''simple docstring'''







		def __init__(self						:      List[Any]      ,      _lowerCAmelCase						:      List[Any]      ):
			A							  =      name
			A							  =      []
			A							  =      []







		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      str      ):
			self.inbound.append(_lowerCAmelCase      )







		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Any      ):
			self.outbound.append(_lowerCAmelCase      )







		def __repr__(self						:      Tuple      ):
			return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""






def    __a (     UpperCAmelCase       ,			UpperCAmelCase=3       ,			UpperCAmelCase=0.85    )      ->Tuple:



	"""simple docstring"""







	A							  =      {}
	for node in nodes:
		A							  =      1

	A							  =      {}
	for node in nodes:
		A							  =      len(node.outbound    )

	for i in range(UpperCAmelCase    ):
		print(f"""======= Iteration {i + 1} ======="""    )
		for _, node in enumerate(UpperCAmelCase    ):
			A							  =      (1 - d) + d * sum(
			    ranks[ib] / outbounds[ib] for ib in node.inbound    )
		print(UpperCAmelCase    )




def    __a (     )      ->Any:



	"""simple docstring"""







	A							  =      list(input("""Enter Names of the Nodes: """    ).split()    )

	A							  =      [Node(UpperCAmelCase    ) for name in names]

	for ri, row in enumerate(UpperCAmelCase    ):
		for ci, col in enumerate(UpperCAmelCase    ):
			if col == 1:
				nodes[ci].add_inbound(names[ri]    )
				nodes[ri].add_outbound(names[ci]    )

	print("""======= Nodes ======="""    )
	for node in nodes:
		print(UpperCAmelCase    )

	page_rank(UpperCAmelCase    )


if __name__ == "__main__":
				main()

