



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


lowercase					=       logging.getLogger(__name__)




@dataclass
class       lowercase__      :
							'''simple docstring'''







							_UpperCAmelCase   					=       field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     )
							_UpperCAmelCase   					=       field(
							    default=__snake_case,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},       )
							_UpperCAmelCase   					=       field(
							    default=__snake_case,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}     )
							_UpperCAmelCase   					=       field(
							    default=__snake_case,       metadata={'''help''': '''Whether to log verbose messages or not.'''},       )
							_UpperCAmelCase   					=       field(
							    default=2.0,       metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}     )
							_UpperCAmelCase   					=       field(
							    default=0.5,       metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}     )
							_UpperCAmelCase   					=       field(
							    default=0.999995,       metadata={'''help''': '''Decay of gumbel temperature during training.'''}     )







def 						UpperCAmelCase  (		A						:    Tuple  ,					A						:    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							)]  ,					)
		_UpperCAmelCase							=		logging.WARNING
		if model_args.verbose_logging:
				_UpperCAmelCase							=		logging.DEBUG
		elif trainer_utils.is_main_process(training_args.local_rank							):
				_UpperCAmelCase							=		logging.INFO
		logger.setLevel(lowercase__							)




@dataclass
class       lowercase__      :
							'''simple docstring'''







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







@dataclass
class       lowercase__      :
							'''simple docstring'''







							_UpperCAmelCase   					=       42
							_UpperCAmelCase   					=       42
							_UpperCAmelCase   					=       '''longest'''
							_UpperCAmelCase   					=       None
							_UpperCAmelCase   					=       None






							def __call__(	self						,							snake_case    )      ->				Dict:
									_UpperCAmelCase							=		self.feature_extractor.pad(
									    __UpperCamelCase						,							max_length=self.max_length						,							padding=self.padding						,							pad_to_multiple_of=self.pad_to_multiple_of						,							return_tensors='pt'						,							)
									_UpperCAmelCase							=		self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]    )

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

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

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

									return batch







class       lowercase__      (   __snake_case     ):
							'''simple docstring'''







							def __init__(	self						,							*snake_case						,							snake_case=1						,							snake_case=0						,							snake_case=1.0						,							**snake_case    )      ->				List[str]:
									super().__init__(*__UpperCamelCase						,							**__UpperCamelCase    )
									_UpperCAmelCase							=		0
									_UpperCAmelCase							=		max_gumbel_temp
									_UpperCAmelCase							=		min_gumbel_temp
									_UpperCAmelCase							=		gumbel_temp_decay






							def 							lowerCamelCase_ (	self						,							snake_case						,							snake_case    )      ->				Optional[int]:
									model.train()
									_UpperCAmelCase							=		self._prepare_inputs(__UpperCamelCase    )

									if self.use_amp:
											with autocast():
													_UpperCAmelCase							=		self.compute_loss(__UpperCamelCase						,							__UpperCamelCase    )
									else:
											_UpperCAmelCase							=		self.compute_loss(__UpperCamelCase						,							__UpperCamelCase    )

									if self.args.n_gpu > 1 or self.deepspeed:
											if model.module.config.ctc_loss_reduction == "mean":
													_UpperCAmelCase							=		loss.mean()
											elif model.module.config.ctc_loss_reduction == "sum":
													_UpperCAmelCase							=		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:
											_UpperCAmelCase							=		loss / self.args.gradient_accumulation_steps

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

		'''simple docstring'''


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

		_UpperCAmelCase			,       _UpperCAmelCase			,       _UpperCAmelCase							=		parser.parse_args_into_dataclasses()
		configure_logger(lowercase__  ,					lowercase__							)

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

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

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

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

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

		# normalize and transform to `BatchFeatures`
		_UpperCAmelCase							=		vectorized_datasets.map(
		    lowercase__  ,					batched=lowercase__  ,					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
		_UpperCAmelCase							=		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\''							)

		_UpperCAmelCase							=		WavaVecaForPreTraining(lowercase__							)

		_UpperCAmelCase							=		DataCollatorForWavaVecaPretraining(model=lowercase__  ,					feature_extractor=lowercase__							)

		_UpperCAmelCase							=		WavaVecaPreTrainer(
		    model=lowercase__  ,					data_collator=lowercase__  ,					args=lowercase__  ,					train_dataset=vectorized_datasets['train']  ,					eval_dataset=vectorized_datasets['validation']  ,					tokenizer=lowercase__  ,					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()





class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			val
				snake_case_   		=			None
				snake_case_   		=			None







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if self.val:
					if val < self.val:
						if self.left is None:
							snake_case_   		=			Node(__UpperCamelCase				)
						else:
							self.left.insert(__UpperCamelCase				)
					elif val > self.val:
						if self.right is None:
							snake_case_   		=			Node(__UpperCamelCase				)
						else:
							self.right.insert(__UpperCamelCase				)
				else:
					snake_case_   		=			val







def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	# Recursive traversal
	if root:
		inorder(root.left      ,			lowercase__						)
		res.append(root.val						)
		inorder(root.right      ,			lowercase__						)





def a(lowercase__						):





	'''simple docstring'''
	# Build BST
	if len(lowercase__						) == 0:
		return arr
	snake_case_   		=			Node(arr[0]						)
	for i in range(1      ,			len(lowercase__						)						):
		root.insert(arr[i]						)
	# Traverse BST in order.
	snake_case_   		=			[]
	inorder(lowercase__      ,			lowercase__						)
	return res


if __name__ == "__main__":
		print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
