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


SCREAMING_SNAKE_CASE__      						=  logging.getLogger(__name__)



@dataclass
class   lowerCAmelCase_    :



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}					)
				_lowerCAmelCase     :    Optional[str]			  =						field(
				    default=lowerCAmelCase     ,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}     ,    )
				_lowerCAmelCase     :    Optional[bool]			  =						field(
				    default=lowerCAmelCase     ,    metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}					)
				_lowerCAmelCase     :    Optional[bool]			  =						field(
				    default=lowerCAmelCase     ,    metadata={"""help""": """Whether to log verbose messages or not."""}     ,    )
				_lowerCAmelCase     :    Optional[float]			  =						field(
				    default=2.0     ,    metadata={"""help""": """Maximum temperature for gumbel softmax."""}					)
				_lowerCAmelCase     :    Optional[float]			  =						field(
				    default=0.5     ,    metadata={"""help""": """Minimum temperature for gumbel softmax."""}					)
				_lowerCAmelCase     :    Optional[float]			  =						field(
				    default=0.99_9995     ,    metadata={"""help""": """Decay of gumbel temperature during training."""}					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				ModelArguments ,			_UpperCamelCase       :				TrainingArguments					)					->				List[str]:





						"""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(_UpperCamelCase					)



@dataclass
class   lowerCAmelCase_    :



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						field(
				    default=lowerCAmelCase     ,    metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}					)
				_lowerCAmelCase     :    Optional[str]			  =						field(
				    default=lowerCAmelCase     ,    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="""file"""     ,    metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""}     ,    )
				_lowerCAmelCase     :    bool			  =						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[int]			  =						field(
				    default=lowerCAmelCase     ,    metadata={"""help""": """The number of processes to use for the preprocessing."""}     ,    )
				_lowerCAmelCase     :    Optional[float]			  =						field(
				    default=20.0     ,    metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}					)





@dataclass
class   lowerCAmelCase_    :



				"""simple docstring"""
				_lowerCAmelCase     :    WavaVecaForPreTraining
				_lowerCAmelCase     :    WavaVecaFeatureExtractor
				_lowerCAmelCase     :    Union[bool, str]			  =						"longest"
				_lowerCAmelCase     :    Optional[int]			  =						None
				_lowerCAmelCase     :    Optional[int]			  =						None

				def __call__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.feature_extractor.pad(
										    lowerCAmelCase		,    max_length=self.max_length		,    padding=self.padding		,    pad_to_multiple_of=self.pad_to_multiple_of		,    return_tensors='pt'		,    )
										snake_case								=			self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]    )

										snake_case								=			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
																snake_case								=			self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1    )    ).to(
																    torch.long    )

																snake_case								=			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
																snake_case								=			1
																snake_case								=			attention_mask.flip([-1]    ).cumsum(-1    ).flip([-1]    ).bool()

										# sample randomly masked indices
										snake_case								=			_compute_mask_indices(
										    (batch_size, mask_indices_seq_length)		,    self.model.config.mask_time_prob		,    self.model.config.mask_time_length		,    attention_mask=lowerCAmelCase		,    min_masks=2		,    )

										return batch





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    *lowerCAmelCase		,    lowerCAmelCase=1		,    lowerCAmelCase=0		,    lowerCAmelCase=1.0		,    **lowerCAmelCase    ):






										"""simple docstring"""

										super().__init__(*lowerCAmelCase		,    **lowerCAmelCase    )
										snake_case								=			0
										snake_case								=			max_gumbel_temp
										snake_case								=			min_gumbel_temp
										snake_case								=			gumbel_temp_decay

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										model.train()
										snake_case								=			self._prepare_inputs(lowerCAmelCase    )

										if self.use_amp:
																with autocast():
																						snake_case								=			self.compute_loss(lowerCAmelCase		,    lowerCAmelCase    )
										else:
																snake_case								=			self.compute_loss(lowerCAmelCase		,    lowerCAmelCase    )

										if self.args.n_gpu > 1 or self.deepspeed:
																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['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:
																snake_case								=			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()

										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__    (			)					->				Any:





						"""simple docstring"""






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

						snake_case					,snake_case					,snake_case								=			parser.parse_args_into_dataclasses()
						configure_logger(_UpperCamelCase ,			_UpperCamelCase					)

						# Downloading and loading a dataset from the hub.
						snake_case								=			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"
												snake_case								=			DatasetDict()
												snake_case								=			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 ,			)
												snake_case								=			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"
												snake_case								=			DatasetDict()
												snake_case								=			load_dataset(
												    data_args.dataset_name ,			data_args.dataset_config_name ,			split='validation' ,			cache_dir=model_args.cache_dir ,			)
												snake_case								=			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
						snake_case								=			WavaVecaFeatureExtractor.from_pretrained(
						    model_args.model_name_or_path ,			cache_dir=model_args.cache_dir ,			do_normalize=_UpperCamelCase					)

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

						# load audio files into numpy arrays
						snake_case								=			datasets.map(
						    _UpperCamelCase ,			num_proc=data_args.preprocessing_num_workers ,			remove_columns=datasets['train'].column_names					)

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

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

						# normalize and transform to `BatchFeatures`
						snake_case								=			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
						snake_case								=			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\''					)

						snake_case								=			WavaVecaForPreTraining(_UpperCamelCase					)

						snake_case								=			DataCollatorForWavaVecaPretraining(model=_UpperCamelCase ,			feature_extractor=_UpperCamelCase					)

						snake_case								=			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"""
class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[
										    [],
										    [],
										    [],
										]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										try:
																if len(self.queues[priority]    ) >= 1_00:
																						raise OverflowError('Maximum queue size is 100'    )
																self.queues[priority].append(lowerCAmelCase    )
										except IndexError:
																raise ValueError('Valid priorities are 0, 1, and 2'    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										for queue in self.queues:
																if queue:
																						return queue.pop(0    )
										raise UnderFlowError('All queues are empty'    )

				def __str__(				self    ):






										"""simple docstring"""

										return "\n".join(F"""Priority {i}: {q}""" for i, q in enumerate(self.queues    )    )





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[]

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if len(self.queue    ) == 1_00:
																raise OverFlowError('Maximum queue size is 100'    )
										self.queue.append(lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										if not self.queue:
																raise UnderFlowError('The queue is empty'    )
										else:
																snake_case								=			min(self.queue    )
																self.queue.remove(lowerCAmelCase    )
																return data

				def __str__(				self    ):






										"""simple docstring"""

										return str(self.queue    )





def    lowerCAmelCase__    (			)					->				int:





						"""simple docstring"""






						snake_case								=			FixedPriorityQueue()
						fpq.enqueue(0 ,			1_0					)
						fpq.enqueue(1 ,			7_0					)
						fpq.enqueue(0 ,			1_0_0					)
						fpq.enqueue(2 ,			1					)
						fpq.enqueue(2 ,			5					)
						fpq.enqueue(1 ,			7					)
						fpq.enqueue(2 ,			4					)
						fpq.enqueue(1 ,			6_4					)
						fpq.enqueue(0 ,			1_2_8					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)





def    lowerCAmelCase__    (			)					->				List[str]:





						"""simple docstring"""






						snake_case								=			ElementPriorityQueue()
						epq.enqueue(1_0					)
						epq.enqueue(7_0					)
						epq.enqueue(1_0_0					)
						epq.enqueue(1					)
						epq.enqueue(5					)
						epq.enqueue(7					)
						epq.enqueue(4					)
						epq.enqueue(6_4					)
						epq.enqueue(1_2_8					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()


