



'''simple docstring'''

import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
							from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
							__lowerCAmelCase						      =       True
							from torch.cuda.amp import autocast


__lowerCAmelCase						      =       logging.getLogger(__name__)

@dataclass
class      __magic_name__						:
							lowerCAmelCase	:   Tuple           = field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
							lowerCAmelCase	:   Any           = field(
							    default=_lowerCamelCase      ,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,						)
							lowerCAmelCase	:   int           = field(
							    default=_lowerCamelCase      ,						metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}       )
							lowerCAmelCase	:   Dict           = field(
							    default=_lowerCamelCase      ,						metadata={'help': 'Whether to log verbose messages or not.'}      ,						)
							lowerCAmelCase	:   int           = field(
							    default=2.0      ,						metadata={'help': 'Maximum temperature for gumbel softmax.'}       )
							lowerCAmelCase	:   List[Any]           = field(
							    default=0.5      ,						metadata={'help': 'Minimum temperature for gumbel softmax.'}       )
							lowerCAmelCase	:   Optional[Any]           = field(
							    default=0.999_995      ,						metadata={'help': 'Decay of gumbel temperature during training.'}       )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'						,      datefmt='%m/%d/%Y %H:%M:%S'						,      handlers=[logging.StreamHandler(sys.stdout  )]						,      )
		_a					:				Tuple				=							logging.WARNING
		if model_args.verbose_logging:
				_a					:				Tuple				=							logging.DEBUG
		elif trainer_utils.is_main_process(training_args.local_rank  ):
				_a					:				str				=							logging.INFO
		logger.setLevel(__UpperCAmelCase  )

@dataclass
class      __magic_name__						:
							lowerCAmelCase	:   Dict           = field(
							    default=_lowerCamelCase      ,						metadata={'help': 'The name of the dataset to use (via the datasets library).'}       )
							lowerCAmelCase	:   str           = field(
							    default=_lowerCamelCase      ,						metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}       )
							lowerCAmelCase	:   Dict           = field(
							    default='train'      ,						metadata={
							        'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
							    }      ,						)
							lowerCAmelCase	:   Any           = field(
							    default='validation'      ,						metadata={
							        'help': (
							            'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
							        )
							    }      ,						)
							lowerCAmelCase	:   int           = field(
							    default='file'      ,						metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}      ,						)
							lowerCAmelCase	:   Tuple           = field(
							    default=_lowerCamelCase      ,						metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}       )
							lowerCAmelCase	:   Optional[int]           = field(
							    default=1      ,						metadata={
							        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
							    }      ,						)
							lowerCAmelCase	:   Optional[Any]           = field(
							    default=_lowerCamelCase      ,						metadata={'help': 'The number of processes to use for the preprocessing.'}      ,						)
							lowerCAmelCase	:   Dict           = field(
							    default=20.0      ,						metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'}       )

@dataclass
class      __magic_name__						:
							lowerCAmelCase	:   Union[str, Any]           = 4_2
							lowerCAmelCase	:   str           = 4_2
							lowerCAmelCase	:   Dict           = 'longest'
							lowerCAmelCase	:   Optional[int]           = None
							lowerCAmelCase	:   Dict           = None







							def __call__(							self :	Tuple     ,_UpperCAmelCase :	List[Dict[str, Union[List[int], torch.Tensor]]]		):
									# reformat list to dict and set to pytorch format
									_a					:				List[str]				=							self.feature_extractor.pad(
									    lowercase_     ,max_length=self.max_length     ,padding=self.padding     ,pad_to_multiple_of=self.pad_to_multiple_of     ,return_tensors='pt'     ,)
									_a					:				Optional[int]				=							self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]		)

									_a					:				str				=							batch['input_values'].shape[0]

									# make sure that no loss is computed on padded inputs
									if batch["attention_mask"] is not None:
											# compute real output lengths according to convolution formula
											_a					:				Dict				=							self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1		)		).to(
											    torch.long		)

											_a					:				Optional[int]				=							torch.zeros(
											    (batch_size, mask_indices_seq_length)     ,dtype=torch.long     ,device=batch['input_values'].device		)

											# these two operations makes sure that all values
											# before the output lengths indices are attended to
											_a					:				Tuple				=							1
											_a					:				Any				=							attention_mask.flip([-1]		).cumsum(-1		).flip([-1]		).bool()

									# sample randomly masked indices
									_a					:				Union[str, Any]				=							_compute_mask_indices(
									    (batch_size, mask_indices_seq_length)     ,self.model.config.mask_time_prob     ,self.model.config.mask_time_length     ,attention_mask=lowercase_     ,min_masks=2     ,)

									return batch

class      __magic_name__						(   _lowerCamelCase       ):







							def __init__(							self :	Any     ,*_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Tuple=1     ,_UpperCAmelCase :	Optional[Any]=0     ,_UpperCAmelCase :	List[Any]=1.0     ,**_UpperCAmelCase :	Any		):
									super().__init__(*lowercase_     ,**lowercase_		)
									_a					:				Union[str, Any]				=							0
									_a					:				Any				=							max_gumbel_temp
									_a					:				List[str]				=							min_gumbel_temp
									_a					:				Tuple				=							gumbel_temp_decay







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	nn.Module     ,_UpperCAmelCase :	Dict[str, Union[torch.Tensor, Any]]		):
									model.train()
									_a					:				List[str]				=							self._prepare_inputs(lowercase_		)

									if self.use_amp:
											with autocast():
													_a					:				List[Any]				=							self.compute_loss(lowercase_     ,lowercase_		)
									else:
											_a					:				Union[str, Any]				=							self.compute_loss(lowercase_     ,lowercase_		)

									if self.args.n_gpu > 1 or self.deepspeed:
											if model.module.config.ctc_loss_reduction == "mean":
													_a					:				int				=							loss.mean()
											elif model.module.config.ctc_loss_reduction == "sum":
													_a					:				Optional[Any]				=							loss.sum() / (inputs['mask_time_indices']).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					:				Dict				=							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()

									self.num_update_step += 1
									# make sure gumbel softmax temperature is decayed
									if self.args.n_gpu > 1 or self.deepspeed:
											model.module.set_gumbel_temperature(
											    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step     ,self.min_gumbel_temp		)		)
									else:
											model.set_gumbel_temperature(
											    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step     ,self.min_gumbel_temp		)		)

									return loss.detach()



def 					__lowerCamelCase					(				)      ->				Tuple:
		_a					:				List[str]				=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )

		_a	, _a	, _a					:				Union[str, Any]				=							parser.parse_args_into_dataclasses()
		configure_logger(__UpperCAmelCase						,      __UpperCAmelCase  )

		# Downloading and loading a dataset from the hub.
		_a					:				Union[str, Any]				=							load_dataset(data_args.dataset_name						,      data_args.dataset_config_name						,      cache_dir=model_args.cache_dir  )

		if "validation" not in datasets.keys():
				# make sure only "validation" and "train" keys remain"
				_a					:				List[str]				=							DatasetDict()
				_a					:				Any				=							load_dataset(
				    data_args.dataset_name						,      data_args.dataset_config_name						,      split=f"""{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"""						,      cache_dir=model_args.cache_dir						,      )
				_a					:				Dict				=							load_dataset(
				    data_args.dataset_name						,      data_args.dataset_config_name						,      split=f"""{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"""						,      cache_dir=model_args.cache_dir						,      )
		else:
				# make sure only "validation" and "train" keys remain"
				_a					:				Union[str, Any]				=							DatasetDict()
				_a					:				Dict				=							load_dataset(
				    data_args.dataset_name						,      data_args.dataset_config_name						,      split='validation'						,      cache_dir=model_args.cache_dir						,      )
				_a					:				int				=							load_dataset(
				    data_args.dataset_name						,      data_args.dataset_config_name						,      split=f"""{data_args.train_split_name}"""						,      cache_dir=model_args.cache_dir						,      )

		# only normalized-inputs-training is supported
		_a					:				str				=							WavaVecaFeatureExtractor.from_pretrained(
		    model_args.model_name_or_path						,      cache_dir=model_args.cache_dir						,      do_normalize=__UpperCAmelCase  )

		def prepare_dataset(lowerCAmelCase_  ):
				# check that all files have the correct sampling rate
				_a	, _a					:				List[Any]				=							librosa.load(batch[data_args.speech_file_column]						,      sr=feature_extractor.sampling_rate  )
				return batch

		# load audio files into numpy arrays
		_a					:				Any				=							datasets.map(
		    __UpperCAmelCase						,      num_proc=data_args.preprocessing_num_workers						,      remove_columns=datasets['train'].column_names  )

		# filter audio files that are too long
		_a					:				int				=							vectorized_datasets.filter(
		    lambda lowerCAmelCase_  : len(data['speech']  ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate  )  )

		def normalize(lowerCAmelCase_  ):
				return feature_extractor(batch['speech']						,      sampling_rate=feature_extractor.sampling_rate  )

		# normalize and transform to `BatchFeatures`
		_a					:				Optional[int]				=							vectorized_datasets.map(
		    __UpperCAmelCase						,      batched=__UpperCAmelCase						,      num_proc=data_args.preprocessing_num_workers						,      load_from_cache_file=not data_args.overwrite_cache						,      remove_columns=vectorized_datasets['train'].column_names						,      )

		# pretraining is only supported for "newer" stable layer norm architecture
		# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
		_a					:				int				=							WavaVecaConfig.from_pretrained(
		    model_args.model_name_or_path						,      cache_dir=model_args.cache_dir						,      gradient_checkpointing=training_args.gradient_checkpointing						,      )

		if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
				raise ValueError(
				    'PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'
				    ' ``config.feat_extract_norm=\'layer\''  )

		_a					:				Tuple				=							WavaVecaForPreTraining(__UpperCAmelCase  )

		_a					:				List[str]				=							DataCollatorForWavaVecaPretraining(model=__UpperCAmelCase						,      feature_extractor=__UpperCAmelCase  )

		_a					:				Optional[int]				=							WavaVecaPreTrainer(
		    model=__UpperCAmelCase						,      data_collator=__UpperCAmelCase						,      args=__UpperCAmelCase						,      train_dataset=vectorized_datasets['train']						,      eval_dataset=vectorized_datasets['validation']						,      tokenizer=__UpperCAmelCase						,      max_gumbel_temp=model_args.max_gumbel_temperature						,      min_gumbel_temp=model_args.min_gumbel_temperature						,      gumbel_temp_decay=model_args.gumbel_temperature_decay						,      )
		trainer.train()


if __name__ == "__main__":
							main()









'''simple docstring'''

def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list:
		if any(not isinstance(lowerCAmelCase_						,      lowerCAmelCase_  ) or x < 0 for x in sequence  ):
				raise TypeError('Sequence must be list of non-negative integers'  )
		for _ in range(len(lowerCAmelCase_  )  ):
				for i, (rod_upper, rod_lower) in enumerate(zip(lowerCAmelCase_						,      sequence[1:]  )  ):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
							assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
							assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]




