

import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
       from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
       _SCREAMING_SNAKE_CASE  :						Tuple         =    True
       from torch.cuda.amp import autocast

_SCREAMING_SNAKE_CASE  :						Dict         =    logging.getLogger(__name__)
def        UpperCAmelCase_     (		_A=None   ,	_A=None       ):





     '''simple docstring'''




     return field(default_factory=lambda: default   ,	metadata=_A       )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
  a     =      field(
      default=A__		,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
  a     =      field(
      default=0.1		,  metadata={"help": "The dropout ratio for the attention probabilities."} )
  a     =      field(
      default=0.1		,  metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
  a     =      field(
      default=0.1		,  metadata={
          "help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
      }		,  )
  a     =      field(
      default=0.1		,  metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}		,  )
  a     =      field(
      default=0.0_5		,  metadata={
          "help": (
              "Propability of each feature vector along the time axis to be chosen as the start of the vector"
              "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
              "vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
          )
      }		,  )
  a     =      field(default=0.0		,  metadata={"help": "The LayerDrop probability."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=A__		,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
  a     =      field(
      default="train+validation"		,  metadata={
          "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
      }		,  )
  a     =      field(
      default=A__		,  metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
  a     =      field(
      default=A__		,  metadata={"help": "The number of processes to use for the preprocessing."}		,  )
  a     =      field(
      default=A__		,  metadata={
          "help": (
              "For debugging purposes or quicker training, truncate the number of training examples to this "
              "value if set."
          )
      }		,  )
  a     =      field(
      default=A__		,  metadata={
          "help": (
              "For debugging purposes or quicker training, truncate the number of validation examples to this "
              "value if set."
          )
      }		,  )
  a     =      list_field(
      default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"]		,  metadata={"help": "A list of characters to remove from the transcripts."}		,  )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      42
  a     =      True
  a     =      None
  a     =      None
  a     =      None
  a     =      None






  def __call__(							self		:	Optional[Any]			,     __lowerCamelCase		:	List[Dict[str, Union[List[int], torch.Tensor]]]      )	->							Dict[str, torch.Tensor]:
       # split inputs and labels since they have to be of different lenghts and need
       # different padding methods
       SCREAMING_SNAKE_CASE__					=					[{'''input_values''': feature['''input_values''']} for feature in features]
       SCREAMING_SNAKE_CASE__					=					[{'''input_ids''': feature['''labels''']} for feature in features]

       SCREAMING_SNAKE_CASE__					=					self.processor.pad(
           __lowerCamelCase			,     padding=self.padding			,     max_length=self.max_length			,     pad_to_multiple_of=self.pad_to_multiple_of			,     return_tensors='''pt'''			,     )
       SCREAMING_SNAKE_CASE__					=					self.processor.pad(
           labels=__lowerCamelCase			,     padding=self.padding			,     max_length=self.max_length_labels			,     pad_to_multiple_of=self.pad_to_multiple_of_labels			,     return_tensors='''pt'''			,     )

       # replace padding with -100 to ignore loss correctly
       SCREAMING_SNAKE_CASE__					=					labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1      )			,     -100      )

       SCREAMING_SNAKE_CASE__					=					labels

       return batch






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	nn.Module			,     __lowerCamelCase		:	Dict[str, Union[torch.Tensor, Any]]      )	->							torch.Tensor:
       model.train()
       SCREAMING_SNAKE_CASE__					=					self._prepare_inputs(__lowerCamelCase      )

       if self.use_amp:
            with autocast():
                 SCREAMING_SNAKE_CASE__					=					self.compute_loss(__lowerCamelCase			,     __lowerCamelCase      )
       else:
            SCREAMING_SNAKE_CASE__					=					self.compute_loss(__lowerCamelCase			,     __lowerCamelCase      )

       if self.args.n_gpu > 1:
            if model.module.config.ctc_loss_reduction == "mean":
                 SCREAMING_SNAKE_CASE__					=					loss.mean()
            elif model.module.config.ctc_loss_reduction == "sum":
                 SCREAMING_SNAKE_CASE__					=					loss.sum() / (inputs['''labels'''] >= 0).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:
            SCREAMING_SNAKE_CASE__					=					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()

       return loss.detach()



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
     if len(sys.argv       ) == 2 and sys.argv[1].endswith('''.json'''       ):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
     else:
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     SCREAMING_SNAKE_CASE__					=					None
     if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
          SCREAMING_SNAKE_CASE__					=					get_last_checkpoint(training_args.output_dir       )
          if last_checkpoint is None and len(os.listdir(training_args.output_dir       )       ) > 0:
               raise ValueError(
                   F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                   '''Use --overwrite_output_dir to overcome.'''       )
          elif last_checkpoint is not None:
               logger.info(
                   F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                   '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''       )

    # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   ,	datefmt='''%m/%d/%Y %H:%M:%S'''   ,	handlers=[logging.StreamHandler(sys.stdout       )]   ,	)
     logger.setLevel(logging.INFO if is_main_process(training_args.local_rank       ) else logging.WARN       )

     # Log on each process the small summary:
     logger.warning(
         F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
         + F'''distributed training: {bool(training_args.local_rank != -1       )}, 16-bits training: {training_args.fpaa}'''       )
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank       ):
          transformers.utils.logging.set_verbosity_info()
     logger.info('''Training/evaluation parameters %s'''   ,	_A       )

     # Set seed before initializing model.
     set_seed(training_args.seed       )

     # Get the datasets:
     SCREAMING_SNAKE_CASE__					=					datasets.load_dataset(
         '''common_voice'''   ,	data_args.dataset_config_name   ,	split=data_args.train_split_name       )
     SCREAMING_SNAKE_CASE__					=					datasets.load_dataset('''common_voice'''   ,	data_args.dataset_config_name   ,	split='''test'''       )

     # Create and save tokenizer
     SCREAMING_SNAKE_CASE__					=					F'''[{"".join(data_args.chars_to_ignore       )}]'''

     def remove_special_characters(_A       ):
          SCREAMING_SNAKE_CASE__					=					re.sub(_A   ,	''''''   ,	batch['''sentence''']       ).lower() + ''' '''
          return batch

     SCREAMING_SNAKE_CASE__					=					train_dataset.map(_A   ,	remove_columns=['''sentence''']       )
     SCREAMING_SNAKE_CASE__					=					eval_dataset.map(_A   ,	remove_columns=['''sentence''']       )

     def extract_all_chars(_A       ):
          SCREAMING_SNAKE_CASE__					=					''' '''.join(batch['''text''']       )
          SCREAMING_SNAKE_CASE__					=					list(set(_A       )       )
          return {"vocab": [vocab], "all_text": [all_text]}

     SCREAMING_SNAKE_CASE__					=					train_dataset.map(
         _A   ,	batched=_A   ,	batch_size=-1   ,	keep_in_memory=_A   ,	remove_columns=train_dataset.column_names   ,	)
     SCREAMING_SNAKE_CASE__					=					train_dataset.map(
         _A   ,	batched=_A   ,	batch_size=-1   ,	keep_in_memory=_A   ,	remove_columns=eval_dataset.column_names   ,	)

     SCREAMING_SNAKE_CASE__					=					list(set(vocab_train['''vocab'''][0]       ) | set(vocab_test['''vocab'''][0]       )       )
     SCREAMING_SNAKE_CASE__					=					{v: k for k, v in enumerate(_A       )}
     SCREAMING_SNAKE_CASE__					=					vocab_dict[''' ''']
     del vocab_dict[" "]
     SCREAMING_SNAKE_CASE__					=					len(_A       )
     SCREAMING_SNAKE_CASE__					=					len(_A       )

     with open('''vocab.json'''   ,	'''w'''       ) as vocab_file:
          json.dump(_A   ,	_A       )

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     SCREAMING_SNAKE_CASE__					=					WavaVecaCTCTokenizer(
         '''vocab.json'''   ,	unk_token='''[UNK]'''   ,	pad_token='''[PAD]'''   ,	word_delimiter_token='''|'''   ,	)
     SCREAMING_SNAKE_CASE__					=					WavaVecaFeatureExtractor(
         feature_size=1   ,	sampling_rate=1_60_00   ,	padding_value=0.0   ,	do_normalize=_A   ,	return_attention_mask=_A       )
     SCREAMING_SNAKE_CASE__					=					WavaVecaProcessor(feature_extractor=_A   ,	tokenizer=_A       )
     SCREAMING_SNAKE_CASE__					=					WavaVecaForCTC.from_pretrained(
         model_args.model_name_or_path   ,	cache_dir=model_args.cache_dir   ,	activation_dropout=model_args.activation_dropout   ,	attention_dropout=model_args.attention_dropout   ,	hidden_dropout=model_args.hidden_dropout   ,	feat_proj_dropout=model_args.feat_proj_dropout   ,	mask_time_prob=model_args.mask_time_prob   ,	gradient_checkpointing=training_args.gradient_checkpointing   ,	layerdrop=model_args.layerdrop   ,	ctc_loss_reduction='''mean'''   ,	pad_token_id=processor.tokenizer.pad_token_id   ,	vocab_size=len(processor.tokenizer       )   ,	)

     if data_args.max_train_samples is not None:
          SCREAMING_SNAKE_CASE__					=					min(len(_A       )   ,	data_args.max_train_samples       )
          SCREAMING_SNAKE_CASE__					=					train_dataset.select(range(_A       )       )

     if data_args.max_val_samples is not None:
          SCREAMING_SNAKE_CASE__					=					eval_dataset.select(range(data_args.max_val_samples       )       )

     SCREAMING_SNAKE_CASE__					=					torchaudio.transforms.Resample(4_80_00   ,	1_60_00       )

     # Preprocessing the datasets.
     # We need to read the aduio files as arrays and tokenize the targets.
     def speech_file_to_array_fn(_A       ):
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					torchaudio.load(batch['''path''']       )
          SCREAMING_SNAKE_CASE__					=					resampler(_A       ).squeeze().numpy()
          SCREAMING_SNAKE_CASE__					=					1_60_00
          SCREAMING_SNAKE_CASE__					=					batch['''text''']
          return batch

     SCREAMING_SNAKE_CASE__					=					train_dataset.map(
         _A   ,	remove_columns=train_dataset.column_names   ,	num_proc=data_args.preprocessing_num_workers   ,	)
     SCREAMING_SNAKE_CASE__					=					eval_dataset.map(
         _A   ,	remove_columns=eval_dataset.column_names   ,	num_proc=data_args.preprocessing_num_workers   ,	)

     def prepare_dataset(_A       ):
          # check that all files have the correct sampling rate
          assert (
              len(set(batch['''sampling_rate''']       )       ) == 1
          ), F'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

          SCREAMING_SNAKE_CASE__					=					processor(
              audio=batch['''speech''']   ,	text=batch['''target_text''']   ,	sampling_rate=batch['''sampling_rate'''][0]       )
          batch.update(_A       )
          return batch

     SCREAMING_SNAKE_CASE__					=					train_dataset.map(
         _A   ,	remove_columns=train_dataset.column_names   ,	batch_size=training_args.per_device_train_batch_size   ,	batched=_A   ,	num_proc=data_args.preprocessing_num_workers   ,	)
     SCREAMING_SNAKE_CASE__					=					eval_dataset.map(
         _A   ,	remove_columns=eval_dataset.column_names   ,	batch_size=training_args.per_device_train_batch_size   ,	batched=_A   ,	num_proc=data_args.preprocessing_num_workers   ,	)

     # Metric
     SCREAMING_SNAKE_CASE__					=					datasets.load_metric('''wer'''       )

     def compute_metrics(_A       ):
          SCREAMING_SNAKE_CASE__					=					pred.predictions
          SCREAMING_SNAKE_CASE__					=					np.argmax(_A   ,	axis=-1       )

          SCREAMING_SNAKE_CASE__					=					processor.tokenizer.pad_token_id

          SCREAMING_SNAKE_CASE__					=					processor.batch_decode(_A       )
          # we do not want to group tokens when computing the metrics
          SCREAMING_SNAKE_CASE__					=					processor.batch_decode(pred.label_ids   ,	group_tokens=_A       )

          SCREAMING_SNAKE_CASE__					=					wer_metric.compute(predictions=_A   ,	references=_A       )

          return {"wer": wer}

     if model_args.freeze_feature_extractor:
          model.freeze_feature_extractor()

     # Data collator
     SCREAMING_SNAKE_CASE__					=					DataCollatorCTCWithPadding(processor=_A   ,	padding=_A       )

     # Initialize our Trainer
     SCREAMING_SNAKE_CASE__					=					CTCTrainer(
         model=_A   ,	data_collator=_A   ,	args=_A   ,	compute_metrics=_A   ,	train_dataset=train_dataset if training_args.do_train else None   ,	eval_dataset=eval_dataset if training_args.do_eval else None   ,	tokenizer=processor.feature_extractor   ,	)

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               SCREAMING_SNAKE_CASE__					=					last_checkpoint
          elif os.path.isdir(model_args.model_name_or_path       ):
               SCREAMING_SNAKE_CASE__					=					model_args.model_name_or_path
          else:
               SCREAMING_SNAKE_CASE__					=					None

          # Save the feature_extractor and the tokenizer
          if is_main_process(training_args.local_rank       ):
               processor.save_pretrained(training_args.output_dir       )

          SCREAMING_SNAKE_CASE__					=					trainer.train(resume_from_checkpoint=_A       )
          trainer.save_model()

          SCREAMING_SNAKE_CASE__					=					train_result.metrics
          SCREAMING_SNAKE_CASE__					=					(
              data_args.max_train_samples if data_args.max_train_samples is not None else len(_A       )
          )
          SCREAMING_SNAKE_CASE__					=					min(_A   ,	len(_A       )       )

          trainer.log_metrics('''train'''   ,	_A       )
          trainer.save_metrics('''train'''   ,	_A       )
          trainer.save_state()

     # Evaluation
     SCREAMING_SNAKE_CASE__					=					{}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''       )
          SCREAMING_SNAKE_CASE__					=					trainer.evaluate()
          SCREAMING_SNAKE_CASE__					=					data_args.max_val_samples if data_args.max_val_samples is not None else len(_A       )
          SCREAMING_SNAKE_CASE__					=					min(_A   ,	len(_A       )       )

          trainer.log_metrics('''eval'''   ,	_A       )
          trainer.save_metrics('''eval'''   ,	_A       )

     return results


if __name__ == "__main__":
       main()





from dataclasses import dataclass, field
from typing import Optional






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be trained."} )
  a     =      field(
      default="./"		,  metadata={"help": "Save dir where model repo is cloned and models updates are saved to."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-train"		,  metadata={"help": "Name or path of training dataset."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-valid"		,  metadata={"help": "Name or path of validation dataset."} )
  a     =      field(default=2		,  metadata={"help": "Batch size for training."} )
  a     =      field(default=2		,  metadata={"help": "Batch size for evaluation."} )
  a     =      field(default=0.1		,  metadata={"help": "Value of weight decay."} )
  a     =      field(
      default=1_00_00		,  metadata={"help": "Size of buffer used to shuffle streaming dataset."} )
  a     =      field(default=2e-4		,  metadata={"help": "Learning rate fo training."} )
  a     =      field(default="cosine"		,  metadata={"help": "Learning rate."} )
  a     =      field(
      default=7_50		,  metadata={"help": "Number of warmup steps in the learning rate schedule."} )
  a     =      field(
      default=16		,  metadata={"help": "Number of gradient accumulation steps."} )
  a     =      field(
      default=A__		,  metadata={"help": "Use gradient checkpointing to reduce memory footprint."} )
  a     =      field(default=5_00_00		,  metadata={"help": "Maximum number of training steps."} )
  a     =      field(
      default=-1		,  metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} )
  a     =      field(default=10_24		,  metadata={"help": "Sequence lengths used for training."} )
  a     =      field(default=1		,  metadata={"help": "Training seed."} )
  a     =      field(
      default=10_24		,  metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "States path if the training should continue from a checkpoint folder."} )
  a     =      field(default=A__		,  metadata={"help": "If True the data is pretokenized."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be evaluated."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-valid"		,  metadata={"help": "Name or path of validation dataset."} )
  a     =      field(default=2		,  metadata={"help": "Batch size used for evaluation."} )
  a     =      field(
      default=-1		,  metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} )
  a     =      field(default=10_24		,  metadata={"help": "Length of sequences to be evaluated."} )
  a     =      field(default=1		,  metadata={"help": "Random seed used for evaluation."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be evaluated."} )
  a     =      field(default=A__		,  metadata={"help": "Number of workers used for code evaluation."} )
  a     =      field(
      default=A__		,  metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "Sample from the language model's output distribution."} )
  a     =      field(default=0.2		,  metadata={"help": "Sampling temperature used for generation."} )
  a     =      field(default=2_56		,  metadata={"help": "Maximum number of newly generated tokens."} )
  a     =      field(default=0		,  metadata={"help": "Top-k parameter used for generation."} )
  a     =      field(default=0.9_5		,  metadata={"help": "Top-p parameter used for nucleus sampling."} )
  a     =      field(default=10		,  metadata={"help": "Number of generations to run in parallel."} )
  a     =      field(
      default=2_00		,  metadata={"help": "Number of completions to generate for each sample."} )
  a     =      field(default=1		,  metadata={"help": "Random seed used for evaluation."} )
  a     =      field(
      default="eval_results.json"		,  metadata={"help": "Random seed used for evaluation."} )
  a     =      field(
      default="0"		,  metadata={"help": "Allow `code_eval` to execute Python code on machine"} )
  a     =      field(
      default=-1		,  metadata={
          "help": (
              "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
              " number corresponds to which GPU device id to run on."
          )
      }		,  )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=A__		,  metadata={
          "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
      }		,  )
  a     =      field(
      default="transformersbook/codeparrot"		,  metadata={"help": "Folder or name of dataset to process."} )
  a     =      field(
      default="codeparrot-clean"		,  metadata={"help": "Folder to save processed processed dataset."} )
  a     =      field(
      default=10_00_00		,  metadata={"help": "Number of files to save per JSON output file."} )
  a     =      field(default="content"		,  metadata={"help": "Column containing text data to process."} )
  a     =      field(
      default=10_00		,  metadata={"help": "Maximum line length in file, otherwise file is filtered."} )
  a     =      field(
      default=1_00		,  metadata={"help": "Maximum mean line length in file, otherwise file is filtered."} )
  a     =      field(
      default=0.2_5		,  metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."} )
  a     =      field(
      default=1.5		,  metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."} )
  a     =      field(
      default=0.7		,  metadata={"help": "Probability for filtering config, test and uncommon files."} )
  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Name or path to the tokenizer."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "If True, near-duplicate samples are removed."} )
  a     =      field(
      default=0.8_5		,  metadata={"help": "Jaccard threshold for near-duplicate samples."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="gpt2"		,  metadata={"help": "Base tokenizer to build new tokenizer from."} )
  a     =      field(
      default="transformersbook/codeparrot-train"		,  metadata={"help": "Dataset to train tokenizer on."} )
  a     =      field(default="content"		,  metadata={"help": "Column containing text data to process."} )
  a     =      field(default=20_00_00		,  metadata={"help": "Number of examples to train tokenizer on."} )
  a     =      field(
      default=3_27_68		,  metadata={"help": "Number of examples to train the tokenizer on."} )
  a     =      field(default="codeparrot"		,  metadata={"help": "Name of new tokenizer."} )
  a     =      field(default=A__		,  metadata={"help": "Push saved tokenizer to the hub."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Name or path to the tokenizer."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-train"		,  metadata={"help": "Name or path to the dataset to pretokenize."} )
  a     =      field(
      default="tokenized-codeparrot-train"		,  metadata={"help": "Repo name of the pretokenized data."} )
  a     =      field(default=A__		,  metadata={"help": "Number of workers used for code evaluation."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="gpt2-large"		,  metadata={"help": "Configuration to use for model initialization."} )
  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Tokenizer attached to model."} )
  a     =      field(default="codeparrot"		,  metadata={"help": "Name of the created model."} )
  a     =      field(default=A__		,  metadata={"help": "Push saved tokenizer to the hub."} )


