




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_		=							True
				from torch.cuda.amp import autocast


lowercase_		=							logging.getLogger(__name__)





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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->Dict:






					"""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       )],	)
					__magic_name__					:							Optional[Any]		   =							logging.WARNING
					if model_args.verbose_logging:
										__magic_name__					:							str		   =							logging.DEBUG
					elif trainer_utils.is_main_process(training_args.local_rank       ):
										__magic_name__					:							Optional[Any]		   =							logging.INFO
					logger.setLevel(UpperCAmelCase       )





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





@dataclass
class 		A__				:
	lowerCamelCase__		: bool				    =False
	lowerCamelCase__		: Optional[str]				    =None
	lowerCamelCase__		: Optional[str]				    ="|"
	lowerCamelCase__		: Optional[Dict[str, str]]				    =field(default_factory=__SCREAMING_SNAKE_CASE )
	lowerCamelCase__		: Optional[Set[str]]				    =field(default_factory=__SCREAMING_SNAKE_CASE )
	lowerCamelCase__		: Optional[Callable[[str], str]]				    =None






	@classmethod
	def  lowercase      (	cls				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






						if name == "librispeech":
											return cls()
						if name == "timit":
											return cls(
											    do_lower_case=lowerCamelCase				,     translation_table=str.maketrans({'''-''': ''' '''}						)				,     )
						if name == "buckwalter":
											__magic_name__					:							List[Any]		   =							{
											    '''-''': ''' ''',  # 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  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






						if len(self.translation_table						) > 0:
											__magic_name__					:							Any		   =							text.translate(self.translation_table						)
						if len(self.words_to_remove						) == 0:
											__magic_name__					:							int		   =							''' '''.join(text.split()						)  # clean up whitespaces
						else:
											__magic_name__					:							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				,     lowerCamelCase						)      ->   WavaVecaProcessor:




						"""simple docstring"""






						__magic_name__					:							int		   =							WavaVecaFeatureExtractor.from_pretrained(
						    model_args.model_name_or_path				,     cache_dir=model_args.cache_dir						)
						if self.vocab_file:
											__magic_name__					:							Optional[Any]		   =							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:
											__magic_name__					:							Tuple		   =							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 		A__				:
	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				,     lowerCamelCase						)      ->   Dict[str, torch.Tensor]:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							[{'''input_values''': feature['''input_values''']} for feature in features]
						__magic_name__					:							str		   =							[{'''input_ids''': feature['''labels''']} for feature in features]

						__magic_name__					:							List[Any]		   =							self.processor.pad(
						    lowerCamelCase				,     padding=self.padding				,     max_length=self.max_length				,     pad_to_multiple_of=self.pad_to_multiple_of				,     return_tensors='''pt'''				,     )
						__magic_name__					:							Optional[int]		   =							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
						__magic_name__					:							Dict		   =							labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1						)				,     -100						)

						__magic_name__					:							Optional[int]		   =							labels

						return batch





class 		A__				(   __SCREAMING_SNAKE_CASE ):






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase						)      ->   torch.Tensor:




						"""simple docstring"""






						model.train()
						__magic_name__					:							List[str]		   =							self._prepare_inputs(lowerCamelCase						)

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

						if self.args.n_gpu > 1:
											if model.module.config.ctc_loss_reduction == "mean":
																__magic_name__					:							str		   =							loss.mean()
											elif model.module.config.ctc_loss_reduction == "sum":
																__magic_name__					:							Optional[Any]		   =							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:
											__magic_name__					:							str		   =							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  lowerCAmelCase  (	)  ->Tuple:






					"""simple docstring"""






					__magic_name__					:							List[str]		   =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

					__magic_name__   , __magic_name__   , __magic_name__					:							Optional[int]		   =							parser.parse_args_into_dataclasses()
					configure_logger(UpperCAmelCase,	UpperCAmelCase       )

					__magic_name__					:							int		   =							Orthography.from_name(data_args.orthography.lower()       )
					__magic_name__					:							Tuple		   =							orthography.create_processor(UpperCAmelCase       )
					__magic_name__					:							int		   =							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       ),	)

					__magic_name__					:							Dict		   =							datasets.load_dataset(
					    data_args.dataset_name,	data_args.dataset_config_name,	split=data_args.train_split_name       )
					__magic_name__					:							int		   =							datasets.load_dataset(
					    data_args.dataset_name,	data_args.dataset_config_name,	split=data_args.validation_split_name       )

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

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

					__magic_name__					:							Optional[int]		   =							train_dataset.map(UpperCAmelCase,	remove_columns=[data_args.speech_file_column]       )
					__magic_name__					:							Optional[Any]		   =							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

										__magic_name__					:							Optional[int]		   =							len(UpperCAmelCase       )
										__magic_name__					:							Optional[int]		   =							len(UpperCAmelCase       )
										__magic_name__					:							Tuple		   =							train_dataset.filter(UpperCAmelCase,	remove_columns=['''duration_in_seconds''']       )
										__magic_name__					:							List[str]		   =							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}"'''       )
					__magic_name__					:							List[str]		   =							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}.'''

										__magic_name__					:							Optional[int]		   =							processor(
										    audio=batch['''speech'''],	text=batch[data_args.target_text_column],	sampling_rate=batch['''sampling_rate'''][0]       )
										batch.update(UpperCAmelCase       )
										return batch

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

					__magic_name__					:							str		   =							DataCollatorCTCWithPadding(processor=UpperCAmelCase,	padding=UpperCAmelCase       )

					def compute_metrics(UpperCAmelCase       ):
										__magic_name__					:							Union[str, Any]		   =							pred.predictions
										__magic_name__					:							List[Any]		   =							np.argmax(UpperCAmelCase,	axis=-1       )

										__magic_name__					:							int		   =							processor.tokenizer.pad_token_id

										__magic_name__					:							Optional[Any]		   =							processor.batch_decode(UpperCAmelCase       )
										# we do not want to group tokens when computing the metrics
										__magic_name__					:							Dict		   =							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       )}"'''       )

										__magic_name__					:							Dict		   =							wer_metric.compute(predictions=UpperCAmelCase,	references=UpperCAmelCase       )

										return {"wer": wer}

					if model_args.freeze_feature_extractor:
										model.freeze_feature_extractor()

					__magic_name__					:							Optional[int]		   =							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()









import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.electra.modeling_flax_electra import (
							    FlaxElectraForCausalLM,
							    FlaxElectraForMaskedLM,
							    FlaxElectraForMultipleChoice,
							    FlaxElectraForPreTraining,
							    FlaxElectraForQuestionAnswering,
							    FlaxElectraForSequenceClassification,
							    FlaxElectraForTokenClassification,
							    FlaxElectraModel,
							)


class 				__SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase=13 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=99 ,       __lowercase=24 ,       __lowercase=32 ,       __lowercase=5 ,       __lowercase=4 ,       __lowercase=37 ,       __lowercase="gelu" ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=512 ,       __lowercase=16 ,       __lowercase=2 ,       __lowercase=0.02 ,       __lowercase=4 ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							parent
						lowerCamelCase_							:		Any							   =							batch_size
						lowerCamelCase_							:		Union[str, Any]							   =							seq_length
						lowerCamelCase_							:		Dict							   =							is_training
						lowerCamelCase_							:		Any							   =							use_attention_mask
						lowerCamelCase_							:		Any							   =							use_token_type_ids
						lowerCamelCase_							:		Any							   =							use_labels
						lowerCamelCase_							:		Any							   =							vocab_size
						lowerCamelCase_							:		Any							   =							hidden_size
						lowerCamelCase_							:		Optional[Any]							   =							embedding_size
						lowerCamelCase_							:		Dict							   =							num_hidden_layers
						lowerCamelCase_							:		Optional[Any]							   =							num_attention_heads
						lowerCamelCase_							:		Optional[Any]							   =							intermediate_size
						lowerCamelCase_							:		List[str]							   =							hidden_act
						lowerCamelCase_							:		Optional[Any]							   =							hidden_dropout_prob
						lowerCamelCase_							:		int							   =							attention_probs_dropout_prob
						lowerCamelCase_							:		Optional[int]							   =							max_position_embeddings
						lowerCamelCase_							:		List[str]							   =							type_vocab_size
						lowerCamelCase_							:		int							   =							type_sequence_label_size
						lowerCamelCase_							:		Tuple							   =							initializer_range
						lowerCamelCase_							:		int							   =							num_choices

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size					)

						lowerCamelCase_							:		List[Any]							   =							None
						if self.use_attention_mask:
							lowerCamelCase_							:		Tuple							   =							random_attention_mask([self.batch_size, self.seq_length]					)

						lowerCamelCase_							:		int							   =							None
						if self.use_token_type_ids:
							lowerCamelCase_							:		List[Any]							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size					)

						lowerCamelCase_							:		List[str]							   =							ElectraConfig(
						    vocab_size=self.vocab_size ,       hidden_size=self.hidden_size ,       embedding_size=self.embedding_size ,       num_hidden_layers=self.num_hidden_layers ,       num_attention_heads=self.num_attention_heads ,       intermediate_size=self.intermediate_size ,       hidden_act=self.hidden_act ,       hidden_dropout_prob=self.hidden_dropout_prob ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob ,       max_position_embeddings=self.max_position_embeddings ,       type_vocab_size=self.type_vocab_size ,       initializer_range=self.initializer_range ,       )

						return config, input_ids, token_type_ids, attention_mask





					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							self.prepare_config_and_inputs()
						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							config_and_inputs
						lowerCamelCase_							:		Union[str, Any]							   =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict


@require_flax
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = True

					_lowerCamelCase		  = (
					    (
					        FlaxElectraModel,
					        FlaxElectraForCausalLM,
					        FlaxElectraForMaskedLM,
					        FlaxElectraForPreTraining,
					        FlaxElectraForTokenClassification,
					        FlaxElectraForQuestionAnswering,
					        FlaxElectraForMultipleChoice,
					        FlaxElectraForSequenceClassification,
					    )
					    if is_flax_available()
					    else ()
					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							FlaxElectraModelTester(self					)





					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							if model_class_name == FlaxElectraForMaskedLM:
								lowerCamelCase_							:		Union[str, Any]							   =							model_class_name.from_pretrained('google/electra-small-generator'					)
							else:
								lowerCamelCase_							:		List[Any]							   =							model_class_name.from_pretrained('google/electra-small-discriminator'					)
							lowerCamelCase_							:		Dict							   =							model(np.ones((1, 1)					)					)
							self.assertIsNotNone(__lowercase					)
