



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






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






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






@dataclass
class     __a			:
	SCREAMING_SNAKE_CASE  	=							42
	SCREAMING_SNAKE_CASE  	=							42
	SCREAMING_SNAKE_CASE  	=							"longest"
	SCREAMING_SNAKE_CASE  	=							None
	SCREAMING_SNAKE_CASE  	=							None



	def __call__(    self				:     Any					,						snake_case_				:     List[Dict[str, Union[List[int], torch.Tensor]]])->				Dict[str, torch.Tensor]:
						# reformat list to dict and set to pytorch format
						__lowerCAmelCase						 =self.feature_extractor.pad(
						    snake_case_					,						max_length=self.max_length					,						padding=self.padding					,						pad_to_multiple_of=self.pad_to_multiple_of					,						return_tensors="""pt"""					,						)
						__lowerCAmelCase						 =self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1])

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

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

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

						return batch






class     __a			(  SCREAMING_SNAKE_CASE					):



	def __init__(    self				:     Tuple					,						*snake_case_				:     Dict					,						snake_case_				:     List[str]=1					,						snake_case_				:     Any=0					,						snake_case_				:     List[Any]=1.0					,						**snake_case_				:     Optional[int])->				Union[str, Any]:
						super().__init__(*snake_case_					,						**snake_case_)
						__lowerCAmelCase						 =0
						__lowerCAmelCase						 =max_gumbel_temp
						__lowerCAmelCase						 =min_gumbel_temp
						__lowerCAmelCase						 =gumbel_temp_decay



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     nn.Module					,						snake_case_				:     Dict[str, Union[torch.Tensor, Any]])->				torch.Tensor:
						model.train()
						__lowerCAmelCase						 =self._prepare_inputs(snake_case_)

						if self.use_amp:
											with autocast():
																__lowerCAmelCase						 =self.compute_loss(snake_case_					,						snake_case_)
						else:
											__lowerCAmelCase						 =self.compute_loss(snake_case_					,						snake_case_)

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

						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		(	)	->    Optional[int]:
					# 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.

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

					__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =parser.parse_args_into_dataclasses()
					configure_logger(__lowerCamelCase					, __lowerCamelCase						)

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

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

					# load audio files into numpy arrays
					__lowerCAmelCase						 =datasets.map(
					    __lowerCamelCase					, num_proc=data_args.preprocessing_num_workers					, remove_columns=datasets["""train"""].column_names						)

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

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

					# normalize and transform to `BatchFeatures`
					__lowerCAmelCase						 =vectorized_datasets.map(
					    __lowerCamelCase					, batched=__lowerCamelCase					, 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
					__lowerCAmelCase						 =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'"""						)

					__lowerCAmelCase						 =WavaVecaForPreTraining(__lowerCamelCase						)

					__lowerCAmelCase						 =DataCollatorForWavaVecaPretraining(model=__lowerCamelCase					, feature_extractor=__lowerCamelCase						)

					__lowerCAmelCase						 =WavaVecaPreTrainer(
					    model=__lowerCamelCase					, data_collator=__lowerCamelCase					, args=__lowerCamelCase					, train_dataset=vectorized_datasets["""train"""]					, eval_dataset=vectorized_datasets["""validation"""]					, tokenizer=__lowerCamelCase					, 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()









from collections.abc import Sequence
from queue import Queue






class     __a			:



	def __init__(    self				:     str					,						snake_case_				:     List[str]					,						snake_case_				:     Tuple					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[Any]=None					,						snake_case_				:     Dict=None)->				Optional[int]:
						__lowerCAmelCase						 =start
						__lowerCAmelCase						 =end
						__lowerCAmelCase						 =val
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =left
						__lowerCAmelCase						 =right



	def __repr__(    self				:     List[Any])->				Dict:
						return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""






class     __a			:



	def __init__(    self				:     List[str]					,						snake_case_				:     Sequence					,						snake_case_				:     int)->				str:
						__lowerCAmelCase						 =collection
						__lowerCAmelCase						 =function
						if self.collection:
											__lowerCAmelCase						 =self._build_tree(0					,						len(snake_case_) - 1)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[int])->				Optional[int]:
						self._update_tree(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     Any					,						snake_case_				:     Union[str, Any])->				Union[str, Any]:
						return self._query_range(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     List[str]					,						snake_case_				:     List[str])->				List[Any]:
						if start == end:
											return SegmentTreeNode(snake_case_					,						snake_case_					,						self.collection[start])
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =self._build_tree(snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self._build_tree(mid + 1					,						snake_case_)
						return SegmentTreeNode(snake_case_					,						snake_case_					,						self.fn(left.val					,						right.val)					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     int					,						snake_case_				:     int)->				List[Any]:
						if node.start == i and node.end == i:
											__lowerCAmelCase						 =val
											return
						if i <= node.mid:
											self._update_tree(node.left					,						snake_case_					,						snake_case_)
						else:
											self._update_tree(node.right					,						snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self.fn(node.left.val					,						node.right.val)



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     str					,						snake_case_				:     List[Any])->				Optional[int]:
						if node.start == i and node.end == j:
											return node.val

						if i <= node.mid:
											if j <= node.mid:
																# range in left child tree
																return self._query_range(node.left					,						snake_case_					,						snake_case_)
											else:
																# range in left child tree and right child tree
																return self.fn(
																    self._query_range(node.left					,						snake_case_					,						node.mid)					,						self._query_range(node.right					,						node.mid + 1					,						snake_case_)					,						)
						else:
											# range in right child tree
											return self._query_range(node.right					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str])->				Tuple:
						if self.root is not None:
											__lowerCAmelCase						 =Queue()
											queue.put(self.root)
											while not queue.empty():
																__lowerCAmelCase						 =queue.get()
																yield node

																if node.left is not None:
																					queue.put(node.left)

																if node.right is not None:
																					queue.put(node.right)


if __name__ == "__main__":
				import operator

				for fn in [operator.add, max, min]:
								print('''*''' * 50)
								lowercase_													=      SegmentTree([2, 1, 5, 3, 4], fn)
								for node in arr.traverse():
												print(node)
								print()

								arr.update(1, 5)
								for node in arr.traverse():
												print(node)
								print()

								print(arr.query_range(3, 4))  # 7
								print(arr.query_range(2, 2))  # 5
								print(arr.query_range(1, 3))  # 13
								print()




