

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


UpperCamelCase_    						=       logging.getLogger(__name__)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,							)
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}      )
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Whether to log verbose messages or not.'}						,							)
				lowerCamelCase_		       =    field(
				    default=2.0						,							metadata={'help': 'Maximum temperature for gumbel softmax.'}      )
				lowerCamelCase_		       =    field(
				    default=0.5						,							metadata={'help': 'Minimum temperature for gumbel softmax.'}      )
				lowerCamelCase_		       =    field(
				    default=0.9_9_9_9_9_5						,							metadata={'help': 'Decay of gumbel temperature during training.'}      )






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





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





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    42
				lowerCamelCase_		       =    42
				lowerCamelCase_		       =    "longest"
				lowerCamelCase_		       =    None
				lowerCamelCase_		       =    None







				def __call__(       self     :		Any		,					UpperCAmelCase__     :		List[Dict[str, Union[List[int], torch.Tensor]]]   ):
										'''simple docstring'''
										# reformat list to dict and set to pytorch format
										lowercase :							str    		=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'''		,					)
										lowercase :							List[str]    		=self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1]   )

										lowercase :							List[Any]    		=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
																lowercase :							Tuple    		=self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1   )   ).to(
																    torch.long   )

																lowercase :							Tuple    		=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
																lowercase :							str    		=1
																lowercase :							Union[str, Any]    		=attention_mask.flip([-1]   ).cumsum(-1   ).flip([-1]   ).bool()

										# sample randomly masked indices
										lowercase :							Optional[int]    		=_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 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):







				def __init__(       self     :		Optional[Any]		,					*UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Union[str, Any]=1		,					UpperCAmelCase__     :		Optional[Any]=0		,					UpperCAmelCase__     :		Dict=1.0		,					**UpperCAmelCase__     :		str   ):
										'''simple docstring'''
										super().__init__(*UpperCAmelCase__		,					**UpperCAmelCase__   )
										lowercase :							Any    		=0
										lowercase :							List[str]    		=max_gumbel_temp
										lowercase :							int    		=min_gumbel_temp
										lowercase :							Any    		=gumbel_temp_decay







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		nn.Module		,					UpperCAmelCase__     :		Dict[str, Union[torch.Tensor, Any]]   ):
										'''simple docstring'''
										model.train()
										lowercase :							Optional[int]    		=self._prepare_inputs(UpperCAmelCase__   )

										if self.use_amp:
																with autocast():
																						lowercase :							Dict    		=self.compute_loss(UpperCAmelCase__		,					UpperCAmelCase__   )
										else:
																lowercase :							Any    		=self.compute_loss(UpperCAmelCase__		,					UpperCAmelCase__   )

										if self.args.n_gpu > 1 or self.deepspeed:
																if model.module.config.ctc_loss_reduction == "mean":
																						lowercase :							Tuple    		=loss.mean()
																elif model.module.config.ctc_loss_reduction == "sum":
																						lowercase :							str    		=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:
																lowercase :							int    		=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 				_lowerCAmelCase			(   )				->  str:
						# 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.

						lowercase :							List[str]    		=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

						lowercase		,	lowercase		,	lowercase :							Optional[int]    		=parser.parse_args_into_dataclasses()
						configure_logger(__magic_name__   , __magic_name__						)

						# Downloading and loading a dataset from the hub.
						lowercase :							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"
												lowercase :							int    		=DatasetDict()
												lowercase :							Union[str, 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   , )
												lowercase :							Optional[int]    		=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"
												lowercase :							Any    		=DatasetDict()
												lowercase :							int    		=load_dataset(
												    data_args.dataset_name   , data_args.dataset_config_name   , split='''validation'''   , cache_dir=model_args.cache_dir   , )
												lowercase :							Optional[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
						lowercase :							List[Any]    		=WavaVecaFeatureExtractor.from_pretrained(
						    model_args.model_name_or_path   , cache_dir=model_args.cache_dir   , do_normalize=__magic_name__						)

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

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

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

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

						# normalize and transform to `BatchFeatures`
						lowercase :							Optional[Any]    		=vectorized_datasets.map(
						    __magic_name__   , batched=__magic_name__   , 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
						lowercase :							str    		=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\''''						)

						lowercase :							Tuple    		=WavaVecaForPreTraining(__magic_name__						)

						lowercase :							Tuple    		=DataCollatorForWavaVecaPretraining(model=__magic_name__   , feature_extractor=__magic_name__						)

						lowercase :							Tuple    		=WavaVecaPreTrainer(
						    model=__magic_name__   , data_collator=__magic_name__   , args=__magic_name__   , train_dataset=vectorized_datasets['''train''']   , eval_dataset=vectorized_datasets['''validation''']   , tokenizer=__magic_name__   , 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 __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[int]      ,					__lowercase :      int     )    ->   bool:





    """simple docstring"""

    if len(__lowercase     ) == 0:
        return False
    __A       =						len(__lowercase     ) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]      ,					__lowercase     )
    else:
        return binary_search(a_list[midpoint + 1 :]      ,					__lowercase     )


if __name__ == "__main__":
  __a   :							Tuple						= input("Enter numbers separated by comma:\n").strip()
  __a   :							Any						= [int(item.strip()) for item in user_input.split(",")]
  __a   :							List[Any]						= int(input("Enter the number to be found in the list:\n").strip())
  __a   :							Optional[int]						= "" if binary_search(sequence, target) else "not "
  print(f"""{target} was {not_str}found in {sequence}""")




