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


lowercase  : Optional[Any]						   =							logging.getLogger(__name__)

@dataclass
class __snake_case		:
    _a		:						str=    field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}					)
    _a		:						Optional[str]=    field(
        default=lowerCAmelCase ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,     )
    _a		:						Optional[bool]=    field(
        default=lowerCAmelCase ,     metadata={"help": "Whether to freeze the feature extractor layers of the model."}					)
    _a		:						Optional[bool]=    field(
        default=lowerCAmelCase ,     metadata={"help": "Whether to log verbose messages or not."} ,     )
    _a		:						Optional[float]=    field(
        default=2.0 ,     metadata={"help": "Maximum temperature for gumbel softmax."}					)
    _a		:						Optional[float]=    field(
        default=0.5 ,     metadata={"help": "Minimum temperature for gumbel softmax."}					)
    _a		:						Optional[float]=    field(
        default=0.99_99_95 ,     metadata={"help": "Decay of gumbel temperature during training."}					)







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

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






@dataclass
class __snake_case		:
    _a		:						WavaVecaForPreTraining
    _a		:						WavaVecaFeatureExtractor
    _a		:						Union[bool, str]=    "longest"
    _a		:						Optional[int]=    None
    _a		:						Optional[int]=    None
    def __call__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.feature_extractor.pad(
               snake_case		,max_length=self.max_length		,padding=self.padding		,pad_to_multiple_of=self.pad_to_multiple_of		,return_tensors="""pt"""		,)
           lowercase							:					Optional[int]	     =				self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]				)

           lowercase							:					Optional[int]	     =				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							:					Union[str, Any]	     =				self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1				)				).to(
                      torch.long				)

                  lowercase							:					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
                  lowercase							:					Union[str, Any]	     =				1
                  lowercase							:					Optional[int]	     =				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=snake_case		,min_masks=2		,)

           return batch






class __snake_case		(				lowerCAmelCase					):
    def __init__(  self		,*snake_case		,snake_case=1		,snake_case=0		,snake_case=1.0		,**snake_case				):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case				)
           lowercase							:					Optional[int]	     =				0
           lowercase							:					Optional[Any]	     =				max_gumbel_temp
           lowercase							:					List[str]	     =				min_gumbel_temp
           lowercase							:					Dict	     =				gumbel_temp_decay
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           model.train()
           lowercase							:					int	     =				self._prepare_inputs(snake_case				)

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

           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							:					Optional[int]	     =				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							:					str	     =				loss / self.args.gradient_accumulation_steps

           if self.use_amp:
                  self.scaler.scale(snake_case				).backward()
           elif self.use_apex:
                  with amp.scale_loss(snake_case		,self.optimizer				) as scaled_loss:
                         scaled_loss.backward()
           elif self.deepspeed:
                  self.deepspeed.backward(snake_case				)
           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(  )			->   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							:					Optional[Any]	     =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )

       lowercase							:					int	     =				parser.parse_args_into_dataclasses()
       configure_logger(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

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

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

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

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

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

       # normalize and transform to `BatchFeatures`
       lowercase							:					List[Any]	     =				vectorized_datasets.map(
           SCREAMING_SNAKE_CASE__ ,     batched=SCREAMING_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							:					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'"""    )

       lowercase							:					List[str]	     =				WavaVecaForPreTraining(SCREAMING_SNAKE_CASE__    )

       lowercase							:					str	     =				DataCollatorForWavaVecaPretraining(model=SCREAMING_SNAKE_CASE__ ,     feature_extractor=SCREAMING_SNAKE_CASE__    )

       lowercase							:					Optional[int]	     =				WavaVecaPreTrainer(
           model=SCREAMING_SNAKE_CASE__ ,     data_collator=SCREAMING_SNAKE_CASE__ ,     args=SCREAMING_SNAKE_CASE__ ,     train_dataset=vectorized_datasets["""train"""] ,     eval_dataset=vectorized_datasets["""validation"""] ,     tokenizer=SCREAMING_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()


from collections.abc import Callable

import numpy as np







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   np.array:
       lowercase							:					Optional[int]	     =				int(np.ceil((x_end - xa) / step_size    )    )
       lowercase							:					List[Any]	     =				np.zeros((n + 1,)    )
       lowercase							:					Optional[int]	     =				ya
       lowercase							:					Optional[int]	     =				xa

       for k in range(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					str	     =				y[k] + step_size * ode_func(SCREAMING_SNAKE_CASE__ ,     y[k]    )
              lowercase							:					Union[str, Any]	     =				y[k] + (
                  (step_size / 2) * (ode_func(SCREAMING_SNAKE_CASE__ ,     y[k]    ) + ode_func(x + step_size ,     SCREAMING_SNAKE_CASE__    ))
              )
              x += step_size

       return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
