
"""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'):
			lowercase		:						str =					True
			from torch.cuda.amp import autocast


lowercase		:						int =					logging.getLogger(__name__)




@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Whether to log verbose messages or not.'''} ,				)






def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			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:						List[Any]		    =					logging.WARNING
					if model_args.verbose_logging:
										__a:						Dict		    =					logging.DEBUG
					elif trainer_utils.is_main_process(training_args.local_rank ):
										__a:						Optional[int]		    =					logging.INFO
					logger.setLevel(lowerCamelCase_ )




@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''train''' ,				metadata={
							        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
							    } ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''validation''' ,				metadata={
							        '''help''': (
							            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
							        )
							    } ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''text''' ,				metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''file''' ,				metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''librispeech''' ,				metadata={
							        '''help''': (
							            '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
							            ''' \'buckwalter\'.'''
							        )
							    } ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''The number of processes to use for the preprocessing.'''} ,				)






@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						False
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						"|"
							SCREAMING_SNAKE_CASE		     =						field(default_factory=_lowercase       )
							SCREAMING_SNAKE_CASE		     =						field(default_factory=_lowercase       )
							SCREAMING_SNAKE_CASE		     =						None






							@classmethod
							def 							__lowercase  (					cls						:			List[Any]						, snake_case__						:			str    ):






												"""simple docstring"""



												if name == "librispeech":
																	return cls()
												if name == "timit":
																	return cls(
																	    do_lower_case=snake_case__						, translation_table=str.maketrans({"""-""": """ """}    )						, )
												if name == "buckwalter":
																	__a:						List[Any]		    =					{
																	    """-""": """ """,  # sometimes used to represent pauses
																	    """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
																	}
																	return cls(
																	    vocab_file=pathlib.Path(snake_case__    ).parent.joinpath("""vocab/buckwalter.json"""    )						, word_delimiter_token="""/"""						, translation_table=str.maketrans(snake_case__    )						, words_to_remove={"""sil"""}						, untransliterator=arabic.buckwalter.untransliterate						, )
												raise ValueError(f"""Unsupported orthography: '{name}'."""    )






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






												"""simple docstring"""



												if len(self.translation_table    ) > 0:
																	__a:						Optional[int]		    =					text.translate(self.translation_table    )
												if len(self.words_to_remove    ) == 0:
																	__a:						Tuple		    =					""" """.join(text.split()    )  # clean up whitespaces
												else:
																	__a:						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						:			Union[str, Any]						, snake_case__						:			ModelArguments    ):






												"""simple docstring"""



												__a:						Dict		    =					WavaVecaFeatureExtractor.from_pretrained(
												    model_args.model_name_or_path						, cache_dir=model_args.cache_dir    )
												if self.vocab_file:
																	__a:						Dict		    =					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:						Dict		    =					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(snake_case__						, snake_case__    )






@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						42
							SCREAMING_SNAKE_CASE		     =						True
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None






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






												"""simple docstring"""



												__a:						Optional[int]		    =					[{"""input_values""": feature["""input_values"""]} for feature in features]
												__a:						Optional[Any]		    =					[{"""input_ids""": feature["""labels"""]} for feature in features]

												__a:						Optional[Any]		    =					self.processor.pad(
												    snake_case__						, padding=self.padding						, max_length=self.max_length						, pad_to_multiple_of=self.pad_to_multiple_of						, return_tensors="""pt"""						, )
												__a:						str		    =					self.processor.pad(
												    labels=snake_case__						, 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:						Any		    =					labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1    )						, -1_0_0    )

												__a:						List[Any]		    =					labels

												return batch






class    _snake_case  (						_lowercase       ):






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






												"""simple docstring"""



												model.train()
												__a:						int		    =					self._prepare_inputs(snake_case__    )

												if self.use_amp:
																	with autocast():
																						__a:						List[str]		    =					self.compute_loss(snake_case__						, snake_case__    )
												else:
																	__a:						Tuple		    =					self.compute_loss(snake_case__						, snake_case__    )

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

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

												return loss.detach()






def     lowerCamelCase_    (	)     ->			List[Any]:


					'''simple docstring'''
					# 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:						int		    =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

					__a    ,				__a    ,				__a:						List[str]		    =					parser.parse_args_into_dataclasses()
					configure_logger(lowerCamelCase_	,				lowerCamelCase_ )

					__a:						Union[str, Any]		    =					Orthography.from_name(data_args.orthography.lower() )
					__a:						List[str]		    =					orthography.create_processor(lowerCamelCase_ )
					__a:						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 )	,				)

					__a:						int		    =					datasets.load_dataset(
					    data_args.dataset_name	,				data_args.dataset_config_name	,				split=data_args.train_split_name )
					__a:						Tuple		    =					datasets.load_dataset(
					    data_args.dataset_name	,				data_args.dataset_config_name	,				split=data_args.validation_split_name )

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

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

					__a:						str		    =					train_dataset.map(lowerCamelCase_	,				remove_columns=[data_args.speech_file_column] )
					__a:						Union[str, Any]		    =					val_dataset.map(lowerCamelCase_	,				remove_columns=[data_args.speech_file_column] )

					if data_args.max_duration_in_seconds is not None:

										def filter_by_max_duration(lowerCamelCase_ ):
															return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

										__a:						List[Any]		    =					len(lowerCamelCase_ )
										__a:						List[str]		    =					len(lowerCamelCase_ )
										__a:						int		    =					train_dataset.filter(lowerCamelCase_	,				remove_columns=["""duration_in_seconds"""] )
										__a:						Optional[int]		    =					val_dataset.filter(lowerCamelCase_	,				remove_columns=["""duration_in_seconds"""] )
										if len(lowerCamelCase_ ) > old_train_size:
															logger.warning(
															    f"""Filtered out {len(lowerCamelCase_ ) - old_train_size} train example(s) longer than"""
															    f""" {data_args.max_duration_in_seconds} second(s).""" )
										if len(lowerCamelCase_ ) > old_val_size:
															logger.warning(
															    f"""Filtered out {len(lowerCamelCase_ ) - old_val_size} validation example(s) longer than"""
															    f""" {data_args.max_duration_in_seconds} second(s).""" )
					logger.info(f"""Split sizes: {len(lowerCamelCase_ )} train and {len(lowerCamelCase_ )} validation.""" )

					logger.warning(f"""Updated {len(lowerCamelCase_ )} 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:						List[str]		    =					None

					def prepare_dataset(lowerCamelCase_ ):
										# 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:						Tuple		    =					processor(
										    audio=batch["""speech"""]	,				text=batch[data_args.target_text_column]	,				sampling_rate=batch["""sampling_rate"""][0] )
										batch.update(lowerCamelCase_ )
										return batch

					__a:						Union[str, Any]		    =					train_dataset.map(
					    lowerCamelCase_	,				batch_size=training_args.per_device_train_batch_size	,				batched=lowerCamelCase_	,				num_proc=data_args.preprocessing_num_workers	,				)
					__a:						Tuple		    =					val_dataset.map(
					    lowerCamelCase_	,				batch_size=training_args.per_device_train_batch_size	,				batched=lowerCamelCase_	,				num_proc=data_args.preprocessing_num_workers	,				)

					__a:						Union[str, Any]		    =					DataCollatorCTCWithPadding(processor=lowerCamelCase_	,				padding=lowerCamelCase_ )

					def compute_metrics(lowerCamelCase_ ):
										__a:						Any		    =					pred.predictions
										__a:						List[Any]		    =					np.argmax(lowerCamelCase_	,				axis=-1 )

										__a:						int		    =					processor.tokenizer.pad_token_id

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

										__a:						Optional[int]		    =					wer_metric.compute(predictions=lowerCamelCase_	,				references=lowerCamelCase_ )

										return {"wer": wer}

					if model_args.freeze_feature_extractor:
										model.freeze_feature_extractor()

					__a:						Any		    =					CTCTrainer(
					    model=lowerCamelCase_	,				data_collator=lowerCamelCase_	,				args=lowerCamelCase_	,				compute_metrics=lowerCamelCase_	,				train_dataset=lowerCamelCase_	,				eval_dataset=lowerCamelCase_	,				tokenizer=processor.feature_extractor	,				)

					trainer.train()


if __name__ == "__main__":
			main()


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase		:						int =					logging.get_logger(__name__)

lowercase		:						Any =					{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''regnet'''
							SCREAMING_SNAKE_CASE		     =						['''x''', '''y''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			Any=3						, snake_case__						:			str=3_2						, snake_case__						:			Tuple=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]						, snake_case__						:			Union[str, Any]=[2, 6, 1_2, 2]						, snake_case__						:			str=6_4						, snake_case__						:			Optional[int]="y"						, snake_case__						:			Tuple="relu"						, **snake_case__						:			str						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												if layer_type not in self.layer_types:
																	raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types    )}"""    )
												__a:						Any		    =					num_channels
												__a:						List[Any]		    =					embedding_size
												__a:						Optional[int]		    =					hidden_sizes
												__a:						Optional[int]		    =					depths
												__a:						Optional[Any]		    =					groups_width
												__a:						Tuple		    =					layer_type
												__a:						str		    =					hidden_act
												# always downsample in the first stage
												__a:						List[str]		    =					True
