

"""simple docstring"""


import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


UpperCAmelCase													=logging.get_logger(__name__)



def   _A					(	_a	:						int     ,   _a	:						Union[str, Any]				):



      """simple docstring"""






      A				  =		set()
      A				  =		[]

      def parse_line(_a	:						List[str]				):
            for line in fp:
                  if isinstance(_a     ,   _a				):
                        A				  =		line.decode("""UTF-8"""				)
                  if "warnings summary (final)" in line:
                        continue
                  # This means we are outside the body of a warning
                  elif not line.startswith(""" """				):
                        # process a single warning and move it to `selected_warnings`.
                        if len(_a				) > 0:
                              A				  =		"""\n""".join(_a				)
                              # Only keep the warnings specified in `targets`
                              if any(f': {x}: ' in warning for x in targets				):
                                    selected_warnings.add(_a				)
                              buffer.clear()
                        continue
                  else:
                        A				  =		line.strip()
                        buffer.append(_a				)

      if from_gh:
            for filename in os.listdir(_a				):
                  A				  =		os.path.join(_a     ,   _a				)
                  if not os.path.isdir(_a				):
                        # read the file
                        if filename != "warnings.txt":
                              continue
                        with open(_a				) as fp:
                              parse_line(_a				)
      else:
            try:
                  with zipfile.ZipFile(_a				) as z:
                        for filename in z.namelist():
                              if not os.path.isdir(_a				):
                                    # read the file
                                    if filename != "warnings.txt":
                                          continue
                                    with z.open(_a				) as fp:
                                          parse_line(_a				)
            except Exception:
                  logger.warning(
                      f'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'				)

      return selected_warnings



def   _A					(	_a	:						int     ,   _a	:						Any				):



      """simple docstring"""






      A				  =		set()

      A				  =		[os.path.join(_a     ,   _a				) for p in os.listdir(_a				) if (p.endswith(""".zip"""				) or from_gh)]
      for p in paths:
            selected_warnings.update(extract_warnings_from_single_artifact(_a     ,   _a				)				)

      return selected_warnings


if __name__ == "__main__":



  def   _A					(	_a	:						int				):



        """simple docstring"""






        return values.split(""","""				)

  UpperCAmelCase													=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
  parser.add_argument(
      "--output_dir",
      type=str,
      required=True,
      help="Where to store the downloaded artifacts and other result files.",
  )
  parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
  # optional parameters
  parser.add_argument(
      "--targets",
      default="DeprecationWarning,UserWarning,FutureWarning",
      type=list_str,
      help="Comma-separated list of target warning(s) which we want to extract.",
  )
  parser.add_argument(
      "--from_gh",
      action="store_true",
      help="If running from a GitHub action workflow and collecting warnings from its artifacts.",
  )

  UpperCAmelCase													=parser.parse_args()

  UpperCAmelCase													=args.from_gh
  if from_gh:
    # The artifacts have to be downloaded using `actions/download-artifact@v3`
    pass
  else:
    os.makedirs(args.output_dir, exist_ok=True)

    # get download links
    UpperCAmelCase													=get_artifacts_links(args.workflow_run_id, token=args.token)
    with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
      json.dump(artifacts, fp, ensure_ascii=False, indent=4)

    # download artifacts
    for idx, (name, url) in enumerate(artifacts.items()):
      print(name)
      print(url)
      print("=" * 80)
      download_artifact(name, url, args.output_dir, args.token)
      # Be gentle to GitHub
      time.sleep(1)

    # extract warnings from artifacts
  UpperCAmelCase													=extract_warnings(args.output_dir, args.targets)
  UpperCAmelCase													=sorted(selected_warnings)
  with open(os.path.join(args.output_dir, "selected_warnings.json"), "w", encoding="UTF-8") as fp:
    json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)







"""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 lowerCamelCase__   :



    '''simple docstring'''




    _lowerCamelCase					=   field(
        metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
    _lowerCamelCase					=   field(
        default=SCREAMING_SNAKE_CASE     ,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,   )
    _lowerCamelCase					=   field(
        default=SCREAMING_SNAKE_CASE     ,   metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}    )
    _lowerCamelCase					=   field(
        default=SCREAMING_SNAKE_CASE     ,   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   _A					(	_a	:						ModelArguments     ,   _a	:						TrainingArguments				):



      """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				)]     ,   )
      A				  =		logging.WARNING
      if model_args.verbose_logging:
            A				  =		logging.DEBUG
      elif trainer_utils.is_main_process(training_args.local_rank				):
            A				  =		logging.INFO
      logger.setLevel(_a				)





@dataclass
class lowerCamelCase__   :



    '''simple docstring'''




    _lowerCamelCase					=   field(
        default=SCREAMING_SNAKE_CASE     ,   metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}    )
    _lowerCamelCase					=   field(
        default=SCREAMING_SNAKE_CASE     ,   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=SCREAMING_SNAKE_CASE     ,   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=SCREAMING_SNAKE_CASE     ,   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 lowerCamelCase__   :



    '''simple docstring'''




    _lowerCamelCase					=   42
    _lowerCamelCase					=   42
    _lowerCamelCase					=   "longest"
    _lowerCamelCase					=   None
    _lowerCamelCase					=   None




    def __call__( self ,lowerCamelCase_    )    ->      Dict[str, torch.Tensor]:
          # reformat list to dict and set to pytorch format
          A				  =		self.feature_extractor.pad(
              lowerCamelCase_ ,max_length=self.max_length ,padding=self.padding ,pad_to_multiple_of=self.pad_to_multiple_of ,return_tensors="""pt""" ,)
          A				  =		self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]    )

          A				  =		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
                A				  =		self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1    )    ).to(
                    torch.long    )

                A				  =		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
                A				  =		1
                A				  =		attention_mask.flip([-1]    ).cumsum(-1    ).flip([-1]    ).bool()

          # sample randomly masked indices
          A				  =		_compute_mask_indices(
              (batch_size, mask_indices_seq_length) ,self.model.config.mask_time_prob ,self.model.config.mask_time_length ,attention_mask=lowerCamelCase_ ,min_masks=2 ,)

          return batch





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    def __init__( self ,*lowerCamelCase_ ,lowerCamelCase_=1 ,lowerCamelCase_=0 ,lowerCamelCase_=1.0 ,**lowerCamelCase_    )    ->      Union[str, Any]:
          super().__init__(*lowerCamelCase_ ,**lowerCamelCase_    )
          A				  =		0
          A				  =		max_gumbel_temp
          A				  =		min_gumbel_temp
          A				  =		gumbel_temp_decay




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      torch.Tensor:
          model.train()
          A				  =		self._prepare_inputs(lowerCamelCase_    )

          if self.use_amp:
                with autocast():
                      A				  =		self.compute_loss(lowerCamelCase_ ,lowerCamelCase_    )
          else:
                A				  =		self.compute_loss(lowerCamelCase_ ,lowerCamelCase_    )

          if self.args.n_gpu > 1 or self.deepspeed:
                if model.module.config.ctc_loss_reduction == "mean":
                      A				  =		loss.mean()
                elif model.module.config.ctc_loss_reduction == "sum":
                      A				  =		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:
                A				  =		loss / self.args.gradient_accumulation_steps

          if self.use_amp:
                self.scaler.scale(lowerCamelCase_    ).backward()
          elif self.use_apex:
                with amp.scale_loss(lowerCamelCase_ ,self.optimizer    ) as scaled_loss:
                      scaled_loss.backward()
          elif self.deepspeed:
                self.deepspeed.backward(lowerCamelCase_    )
          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   _A					(	):



      """simple docstring"""






      A				  =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)

      A  ,			A  ,			A				  =		parser.parse_args_into_dataclasses()
      configure_logger(_a     ,   _a				)

      # Downloading and loading a dataset from the hub.
      A				  =		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"
            A				  =		DatasetDict()
            A				  =		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     ,   )
            A				  =		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"
            A				  =		DatasetDict()
            A				  =		load_dataset(
                data_args.dataset_name     ,   data_args.dataset_config_name     ,   split="""validation"""     ,   cache_dir=model_args.cache_dir     ,   )
            A				  =		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
      A				  =		WavaVecaFeatureExtractor.from_pretrained(
          model_args.model_name_or_path     ,   cache_dir=model_args.cache_dir     ,   do_normalize=_a				)

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

      # load audio files into numpy arrays
      A				  =		datasets.map(
          _a     ,   num_proc=data_args.preprocessing_num_workers     ,   remove_columns=datasets["""train"""].column_names				)

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

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

      # normalize and transform to `BatchFeatures`
      A				  =		vectorized_datasets.map(
          _a     ,   batched=_a     ,   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
      A				  =		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'"""				)

      A				  =		WavaVecaForPreTraining(_a				)

      A				  =		DataCollatorForWavaVecaPretraining(model=_a     ,   feature_extractor=_a				)

      A				  =		WavaVecaPreTrainer(
          model=_a     ,   data_collator=_a     ,   args=_a     ,   train_dataset=vectorized_datasets["""train"""]     ,   eval_dataset=vectorized_datasets["""validation"""]     ,   tokenizer=_a     ,   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()




