






'''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__						:List[str]          =			True
   from torch.cuda.amp import autocast


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







@dataclass
class 	SCREAMING_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=lowerCAmelCase_    ,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}    ,   )
       snake_case__    :     Optional[bool]   		=			field(
           default=lowerCAmelCase_    ,   metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}  )
       snake_case__    :     Optional[bool]   		=			field(
           default=lowerCAmelCase_    ,   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=0.9_9_9_9_9_5    ,   metadata={'help': 'Decay of gumbel temperature during training.'}  )

def     __lowercase  (_lowercase,						_lowercase     )   ->     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						:       str				   =				logging.DEBUG
     elif trainer_utils.is_main_process(training_args.local_rank     ):
          __lowerCamelCase						:       Optional[Any]				   =				logging.INFO
     logger.setLevel(_lowercase     )







@dataclass
class 	SCREAMING_SNAKE_CASE	:
       snake_case__    :     str   		=			field(
           default=lowerCAmelCase_    ,   metadata={'help': 'The name of the dataset to use (via the datasets library).'}  )
       snake_case__    :     Optional[str]   		=			field(
           default=lowerCAmelCase_    ,   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=lowerCAmelCase_    ,   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=lowerCAmelCase_    ,   metadata={'help': 'The number of processes to use for the preprocessing.'}    ,   )
       snake_case__    :     Optional[float]   		=			field(
           default=2_0.0    ,   metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'}  )







@dataclass
class 	SCREAMING_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      : str	,    A__      : List[Dict[str, Union[List[int], torch.Tensor]]]					):


            """simple docstring"""







            __lowerCamelCase						:       List[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						:       List[str]				   =				self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]					)

            __lowerCamelCase						:       List[str]				   =				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						:       Tuple				   =				self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1					)					).to(
                     torch.long					)

                 __lowerCamelCase						:       int				   =				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						:       Dict				   =				1
                 __lowerCamelCase						:       Any				   =				attention_mask.flip([-1]					).cumsum(-1					).flip([-1]					).bool()

            # sample randomly masked indices
            __lowerCamelCase						:       Union[str, Any]				   =				_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 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




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


            """simple docstring"""







            super().__init__(*A__	,    **A__					)
            __lowerCamelCase						:       Tuple				   =				0
            __lowerCamelCase						:       Tuple				   =				max_gumbel_temp
            __lowerCamelCase						:       List[str]				   =				min_gumbel_temp
            __lowerCamelCase						:       Tuple				   =				gumbel_temp_decay




       def 					a_					(					self      : Any	,    A__      : nn.Module	,    A__      : Dict[str, Union[torch.Tensor, Any]]					):


            """simple docstring"""







            model.train()
            __lowerCamelCase						:       List[str]				   =				self._prepare_inputs(A__					)

            if self.use_amp:
                 with autocast():
                      __lowerCamelCase						:       List[str]				   =				self.compute_loss(A__	,    A__					)
            else:
                 __lowerCamelCase						:       int				   =				self.compute_loss(A__	,    A__					)

            if self.args.n_gpu > 1 or self.deepspeed:
                 if model.module.config.ctc_loss_reduction == "mean":
                      __lowerCamelCase						:       List[str]				   =				loss.mean()
                 elif model.module.config.ctc_loss_reduction == "sum":
                      __lowerCamelCase						:       List[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:
                 __lowerCamelCase						:       Any				   =				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  ()   ->     Optional[Any]:


     """simple docstring"""
     # 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						:       Optional[Any]				   =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

     __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       str				   =				parser.parse_args_into_dataclasses()
     configure_logger(_lowercase,						_lowercase     )

     # Downloading and loading a dataset from the hub.
     __lowerCamelCase						:       List[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						:       Dict				   =				DatasetDict()
          __lowerCamelCase						:       List[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,						)
          __lowerCamelCase						:       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,						)
     else:
          # make sure only "validation" and "train" keys remain"
          __lowerCamelCase						:       str				   =				DatasetDict()
          __lowerCamelCase						:       int				   =				load_dataset(
              data_args.dataset_name,						data_args.dataset_config_name,						split="""validation""",						cache_dir=model_args.cache_dir,						)
          __lowerCamelCase						:       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
     __lowerCamelCase						:       Union[str, Any]				   =				WavaVecaFeatureExtractor.from_pretrained(
         model_args.model_name_or_path,						cache_dir=model_args.cache_dir,						do_normalize=_lowercase     )

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

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

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

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

     # normalize and transform to `BatchFeatures`
     __lowerCamelCase						:       Optional[int]				   =				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
     __lowerCamelCase						:       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						:       List[Any]				   =				WavaVecaForPreTraining(_lowercase     )

     __lowerCamelCase						:       List[Any]				   =				DataCollatorForWavaVecaPretraining(model=_lowercase,						feature_extractor=_lowercase     )

     __lowerCamelCase						:       str				   =				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()











'''simple docstring'''

from __future__ import annotations

from random import random







class 	SCREAMING_SNAKE_CASE	:




       def __init__(					self      : Tuple	,    A__      : int | None = None					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				value
            __lowerCamelCase						:       Dict				   =				random()
            __lowerCamelCase						:       Node | None				   =				None
            __lowerCamelCase						:       Node | None				   =				None




       def __repr__(					self      : Any					):


            """simple docstring"""







            from pprint import pformat

            if self.left is None and self.right is None:
                 return f"'{self.value}: {self.prior:.5}'"
            else:
                 return pformat(
                     {f"{self.value}: {self.prior:.5}": (self.left, self.right)}	,    indent=1					)




       def __str__(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				str(self.value					) + """ """
            __lowerCamelCase						:       Any				   =				str(self.left or """"""					)
            __lowerCamelCase						:       List[str]				   =				str(self.right or """"""					)
            return value + left + right

def     __lowercase  (_lowercase,						_lowercase     )   ->     tuple[Node | None, Node | None]:


     """simple docstring"""
     if root is None:  # None tree is split into 2 Nones
          return None, None
     elif root.value is None:
          return None, None
     else:
          if value < root.value:
               __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				split(root.left,						_lowercase     )
               return left, root
          else:
               __lowerCamelCase   ,      __lowerCamelCase						:       Union[str, Any]				   =				split(root.right,						_lowercase     )
               return root, right
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     if (not left) or (not right):  # If one node is None, return the other
          return left or right
     elif left.prior < right.prior:
          __lowerCamelCase						:       Tuple				   =				merge(left.right,						_lowercase     )
          return left
     else:
          __lowerCamelCase						:       int				   =				merge(_lowercase,						right.left     )
          return right
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     __lowerCamelCase						:       List[str]				   =				Node(_lowercase     )
     __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				split(_lowercase,						_lowercase     )
     return merge(merge(_lowercase,						_lowercase     ),						_lowercase     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				split(_lowercase,						value - 1     )
     __lowerCamelCase   ,      __lowerCamelCase						:       List[str]				   =				split(_lowercase,						_lowercase     )
     return merge(_lowercase,						_lowercase     )
def     __lowercase  (_lowercase     )   ->     None:


     """simple docstring"""
     if not root:  # None
          return
     else:
          inorder(root.left     )
          print(root.value,						end=""","""     )
          inorder(root.right     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     for arg in args.split():
          if arg[0] == "+":
               __lowerCamelCase						:       Dict				   =				insert(_lowercase,						int(arg[1:]     )     )

          elif arg[0] == "-":
               __lowerCamelCase						:       List[str]				   =				erase(_lowercase,						int(arg[1:]     )     )

          else:
               print("""Unknown command"""     )

     return root
def     __lowercase  ()   ->     None:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				None
     print(
         """enter numbers to create a tree, + value to add value into treap, """
         """- value to erase all nodes with value. 'q' to quit. """     )

     __lowerCamelCase						:       int				   =				input()
     while args != "q":
          __lowerCamelCase						:       List[Any]				   =				interact_treap(_lowercase,						_lowercase     )
          print(_lowercase     )
          __lowerCamelCase						:       List[Any]				   =				input()

     print("""good by!"""     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()



