




"""simple docstring"""







SCREAMING_SNAKE_CASE_	     =       9.80665





def      __snake_case (				_lowercase			,_lowercase			,_lowercase = g    ):


    """simple docstring"""







    if fluid_density <= 0:
        raise ValueError('''Impossible fluid density'''    )
    if volume < 0:
        raise ValueError('''Impossible Object volume'''    )
    if gravity <= 0:
        raise ValueError('''Impossible Gravity'''    )

    return fluid_density * gravity * volume


if __name__ == "__main__":
  import doctest

  # run doctest
  doctest.testmod()

"""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"""):
   _A     :			List[str]  						=			True
   from torch.cuda.amp import autocast


_A     :			Any  						=			logging.getLogger(__name__)

@dataclass
class 						a__		:
   __lowerCAmelCase				=							field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},     )
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}		)
   __lowerCAmelCase				=							field(
       default=a_,     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=0.999995,     metadata={"""help""": """Decay of gumbel temperature during training."""}		)


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

@dataclass
class 						a__		:
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}		)
   __lowerCAmelCase				=							field(
       default=a_,     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=a_,     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=a_,     metadata={"""help""": """The number of processes to use for the preprocessing."""},     )
   __lowerCAmelCase				=							field(
       default=20.0,     metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}		)


@dataclass
class 						a__		:
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							"longest"
   __lowerCAmelCase				=							None
   __lowerCAmelCase				=							None
   def __call__(     self      ,       _a							):
          # reformat list to dict and set to pytorch format
          lowercase       :							Tuple									=				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"      ,       )
          lowercase       :							str									=				self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]							)

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

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

          # sample randomly masked indices
          lowercase       :							int									=				_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 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       _a=1      ,       _a=0      ,       _a=1.0      ,       **_a							):
          super().__init__(*_a      ,       **_a							)
          lowercase       :							Tuple									=				0
          lowercase       :							Optional[Any]									=				max_gumbel_temp
          lowercase       :							Union[str, Any]									=				min_gumbel_temp
          lowercase       :							str									=				gumbel_temp_decay

   def      __magic_name__     (     self      ,       _a      ,       _a							):
          model.train()
          lowercase       :							Union[str, Any]									=				self._prepare_inputs(_a							)

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

          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       :							Any									=				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       :							List[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     __magic_name__      ( )					->							List[Any]:
       # 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       :							Any									=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )

       lowercase						,       lowercase						,       lowercase       :							Union[str, Any]									=				parser.parse_args_into_dataclasses()
       configure_logger(__snake_case	,					__snake_case    )

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

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

       # load audio files into numpy arrays
       lowercase       :							str									=				datasets.map(
           __snake_case	,					num_proc=data_args.preprocessing_num_workers	,					remove_columns=datasets["train"].column_names    )

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

       def normalize(__snake_case							:			Union[str, Any]    ):
              return feature_extractor(batch["speech"]	,					sampling_rate=feature_extractor.sampling_rate    )

       # normalize and transform to `BatchFeatures`
       lowercase       :							Any									=				vectorized_datasets.map(
           __snake_case	,					batched=__snake_case	,					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       :							Union[str, Any]									=				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       :							Optional[Any]									=				WavaVecaForPreTraining(__snake_case    )

       lowercase       :							Optional[int]									=				DataCollatorForWavaVecaPretraining(model=__snake_case	,					feature_extractor=__snake_case    )

       lowercase       :							List[Any]									=				WavaVecaPreTrainer(
           model=__snake_case	,					data_collator=__snake_case	,					args=__snake_case	,					train_dataset=vectorized_datasets["train"]	,					eval_dataset=vectorized_datasets["validation"]	,					tokenizer=__snake_case	,					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()


