



from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowerCAmelCase__		:					Tuple							=TypeVar('''T''')

class        UpperCAmelCase_  (       Generic[T]	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= None

													__SCREAMING_SNAKE_CASE									= len(_A		)
													__SCREAMING_SNAKE_CASE									= [any_type for _ in range(self.N		)] + arr
													__SCREAMING_SNAKE_CASE									= fnc
													self.build()






						def 	_A     (   self		):







													'''simple docstring'''

													for p in range(self.N - 1				,							0				,							-1		):
																				__SCREAMING_SNAKE_CASE									= self.fn(self.st[p * 2]				,							self.st[p * 2 + 1]		)






						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													p += self.N
													__SCREAMING_SNAKE_CASE									= v
													while p > 1:
																				__SCREAMING_SNAKE_CASE									= p // 2
																				__SCREAMING_SNAKE_CASE									= self.fn(self.st[p * 2]				,							self.st[p * 2 + 1]		)






						def 	_A     (   self				,							_A				,							_A		):  # noqa: E741







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= l + self.N, r + self.N

													__SCREAMING_SNAKE_CASE									= None
													while l <= r:
																				if l % 2 == 1:
																											__SCREAMING_SNAKE_CASE									= self.st[l] if res is None else self.fn(_A				,							self.st[l]		)
																				if r % 2 == 0:
																											__SCREAMING_SNAKE_CASE									= self.st[r] if res is None else self.fn(_A				,							self.st[r]		)
																				__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= (l + 1) // 2, (r - 1) // 2
													return res


if __name__ == "__main__":
			from functools import reduce

			lowerCAmelCase__		:					Optional[int]							=[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

			lowerCAmelCase__		:					List[str]							={
			    0: 7,
			    1: 2,
			    2: 6,
			    3: -14,
			    4: 5,
			    5: 4,
			    6: 7,
			    7: -10,
			    8: 9,
			    9: 10,
			    10: 12,
			    11: 1,
			}

			lowerCAmelCase__		:					List[Any]							=SegmentTree(test_array, min)
			lowerCAmelCase__		:					Any							=SegmentTree(test_array, max)
			lowerCAmelCase__		:					int							=SegmentTree(test_array, lambda a, b: a + b)






			def      __lowercase       (			)    ->			None:
										for i in range(len(a__       )       ):
																	for j in range(a__							,       len(a__       )       ):
																								__SCREAMING_SNAKE_CASE									= reduce(a__							,       test_array[i : j + 1]       )
																								__SCREAMING_SNAKE_CASE									= reduce(a__							,       test_array[i : j + 1]       )
																								__SCREAMING_SNAKE_CASE									= reduce(lambda a__							,       a__       : a + b							,       test_array[i : j + 1]       )
																								assert min_range == min_segment_tree.query(a__							,       a__       )
																								assert max_range == max_segment_tree.query(a__							,       a__       )
																								assert sum_range == sum_segment_tree.query(a__							,       a__       )

			test_all_segments()

			for index, value in test_updates.items():
						lowerCAmelCase__		:					Dict							=value
						min_segment_tree.update(index, value)
						max_segment_tree.update(index, value)
						sum_segment_tree.update(index, value)
						test_all_segments()











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__		:					Optional[int]							=True
			from torch.cuda.amp import autocast


lowerCAmelCase__		:					List[Any]							=logging.getLogger(__name__)

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(
						    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,   )
						UpperCamelCase__      :			Optional[bool]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}	)
						UpperCamelCase__      :			Optional[bool]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Whether to log verbose messages or not.'''}   ,   )
						UpperCamelCase__      :			Optional[float]      =  field(
						    default=2.0   ,   metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}	)
						UpperCamelCase__      :			Optional[float]      =  field(
						    default=0.5   ,   metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}	)
						UpperCamelCase__      :			Optional[float]      =  field(
						    default=0.9_9_9_9_9_5   ,   metadata={'''help''': '''Decay of gumbel temperature during training.'''}	)







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

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default='''train'''   ,   metadata={
						        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
						    }   ,   )
						UpperCamelCase__      :			Optional[str]      =  field(
						    default='''validation'''   ,   metadata={
						        '''help''': (
						            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
						        )
						    }   ,   )
						UpperCamelCase__      :			Optional[str]      =  field(
						    default='''file'''   ,   metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}   ,   )
						UpperCamelCase__      :			bool      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}	)
						UpperCamelCase__      :			Optional[int]      =  field(
						    default=1   ,   metadata={
						        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
						    }   ,   )
						UpperCamelCase__      :			Optional[int]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''The number of processes to use for the preprocessing.'''}   ,   )
						UpperCamelCase__      :			Optional[float]      =  field(
						    default=2_0.0   ,   metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}	)







@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			WavaVecaForPreTraining
						UpperCamelCase__      :			WavaVecaFeatureExtractor
						UpperCamelCase__      :			Union[bool, str]      =  "longest"
						UpperCamelCase__      :			Optional[int]      =  None
						UpperCamelCase__      :			Optional[int]      =  None






						def __call__(   self				,							_A		):







													'''simple docstring'''

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

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

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

													# sample randomly masked indices
													__SCREAMING_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=_A				,							min_masks=2				,							)

													return batch







class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




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







													'''simple docstring'''

													super().__init__(*_A				,							**_A		)
													__SCREAMING_SNAKE_CASE									= 0
													__SCREAMING_SNAKE_CASE									= max_gumbel_temp
													__SCREAMING_SNAKE_CASE									= min_gumbel_temp
													__SCREAMING_SNAKE_CASE									= gumbel_temp_decay






						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													model.train()
													__SCREAMING_SNAKE_CASE									= self._prepare_inputs(_A		)

													if self.use_amp:
																				with autocast():
																											__SCREAMING_SNAKE_CASE									= self.compute_loss(_A				,							_A		)
													else:
																				__SCREAMING_SNAKE_CASE									= self.compute_loss(_A				,							_A		)

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

													if self.use_amp:
																				self.scaler.scale(_A		).backward()
													elif self.use_apex:
																				with amp.scale_loss(_A				,							self.optimizer		) as scaled_loss:
																											scaled_loss.backward()
													elif self.deepspeed:
																				self.deepspeed.backward(_A		)
													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      __lowercase       (			)    ->			Tuple:
							# 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.

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

							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= parser.parse_args_into_dataclasses()
							configure_logger(a__							,       a__       )

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

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

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

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

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

							# normalize and transform to `BatchFeatures`
							__SCREAMING_SNAKE_CASE									= vectorized_datasets.map(
							    a__							,       batched=a__							,       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
							__SCREAMING_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\''       )

							__SCREAMING_SNAKE_CASE									= WavaVecaForPreTraining(a__       )

							__SCREAMING_SNAKE_CASE									= DataCollatorForWavaVecaPretraining(model=a__							,       feature_extractor=a__       )

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






