



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


lowerCAmelCase__      =    logging.getLogger(__name__)






@dataclass
class 	__snake_case	:
  snake_case__    :       str        =						field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
  snake_case__    :       Optional[str]        =						field(
      default=UpperCamelCase_  ,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,					)
  snake_case__    :       Optional[bool]        =						field(
      default=UpperCamelCase_  ,					metadata={"help": "Whether to freeze the feature extractor layers of the model."})
  snake_case__    :       Optional[bool]        =						field(
      default=UpperCamelCase_  ,					metadata={"help": "Whether to log verbose messages or not."}  ,					)
  snake_case__    :       Optional[float]        =						field(
      default=2.0  ,					metadata={"help": "Maximum temperature for gumbel softmax."})
  snake_case__    :       Optional[float]        =						field(
      default=0.5  ,					metadata={"help": "Minimum temperature for gumbel softmax."})
  snake_case__    :       Optional[float]        =						field(
      default=0.999_995  ,					metadata={"help": "Decay of gumbel temperature during training."})






def 	snake_case_  (				A_ :		Optional[int],    A_ :		Optional[int]						):


       '''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						)],    )
       _lowerCamelCase						:    Optional[int]         =			logging.WARNING
       if model_args.verbose_logging:
              _lowerCamelCase						:    int         =			logging.DEBUG
       elif trainer_utils.is_main_process(training_args.local_rank						):
              _lowerCamelCase						:    int         =			logging.INFO
       logger.setLevel(__lowerCAmelCase						)






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






@dataclass
class 	__snake_case	:
  snake_case__    :       WavaVecaForPreTraining
  snake_case__    :       WavaVecaFeatureExtractor
  snake_case__    :       Union[bool, str]        =						"longest"
  snake_case__    :       Optional[int]        =						None
  snake_case__    :       Optional[int]        =						None




  def __call__(			self					:						List[str]						, __lowerCAmelCase					:						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			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'''						, )
         _lowerCamelCase						:    Optional[Any]         =			self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1]		)

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

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

         # sample randomly masked indices
         _lowerCamelCase						:    str         =			_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 	__snake_case	(     UpperCamelCase_):




  def __init__(			self					:						Dict						, *__lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						str=1						, __lowerCAmelCase					:						int=0						, __lowerCAmelCase					:						List[Any]=1.0						, **__lowerCAmelCase					:						List[Any]		):






         """simple docstring"""






         super().__init__(*_a						, **_a		)
         _lowerCamelCase						:    int         =			0
         _lowerCamelCase						:    Dict         =			max_gumbel_temp
         _lowerCamelCase						:    str         =			min_gumbel_temp
         _lowerCamelCase						:    Optional[Any]         =			gumbel_temp_decay




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         model.train()
         _lowerCamelCase						:    List[Any]         =			self._prepare_inputs(_a		)

         if self.use_amp:
                with autocast():
                       _lowerCamelCase						:    Optional[Any]         =			self.compute_loss(_a						, _a		)
         else:
                _lowerCamelCase						:    str         =			self.compute_loss(_a						, _a		)

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


       '''simple docstring'''





       _lowerCamelCase						:    int         =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

       _lowerCamelCase						:    str         =			parser.parse_args_into_dataclasses()
       configure_logger(__lowerCAmelCase,    __lowerCAmelCase						)

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

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

       # load audio files into numpy arrays
       _lowerCamelCase						:    Union[str, Any]         =			datasets.map(
           __lowerCAmelCase,    num_proc=data_args.preprocessing_num_workers,    remove_columns=datasets['''train'''].column_names						)

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

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

       # normalize and transform to `BatchFeatures`
       _lowerCamelCase						:    str         =			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						:    Optional[int]         =			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						:    Tuple         =			WavaVecaForPreTraining(__lowerCAmelCase						)

       _lowerCamelCase						:    int         =			DataCollatorForWavaVecaPretraining(model=__lowerCAmelCase,    feature_extractor=__lowerCAmelCase						)

       _lowerCamelCase						:    str         =			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()







"""simple docstring"""






def 	snake_case_  (				A_ :		list						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			len(A_						)
       for i in range(1,    A_						):
              _lowerCamelCase						:    Tuple         =			collection[i]
              _lowerCamelCase						:    Dict         =			0
              _lowerCamelCase						:    Any         =			i - 1

              while low <= high:
                     _lowerCamelCase						:    Optional[int]         =			(low + high) // 2
                     if val < collection[mid]:
                            _lowerCamelCase						:    List[str]         =			mid - 1
                     else:
                            _lowerCamelCase						:    Dict         =			mid + 1
              for j in range(A_,    A_,    -1						):
                     _lowerCamelCase						:    Optional[int]         =			collection[j - 1]
              _lowerCamelCase						:    Tuple         =			val
       return collection


if __name__ == "__main__":
   lowerCAmelCase__      =    input('''Enter numbers separated by a comma:\n''').strip()
   lowerCAmelCase__      =    [int(item) for item in user_input.split(''',''')]
   print(binary_insertion_sort(unsorted))


