


'''simple docstring'''






import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


a_   :      str				    =   logging.getLogger(__name__)
a_   :      Dict				    =   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
a_   :      Any				    =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 	a       :
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={
         """help""": (
             """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
         )
     }					,			)
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCAmelCase__    )}					,			)
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={
         """help""": (
             """Override some existing default config settings when a model is trained from scratch. Example: """
             """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
         )
     }					,			)
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}					,			)
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}					,			)
 _lowerCAmelCase	   =		field(
     default="""main"""					,			metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}					,			)
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={
         """help""": (
             """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
             """with private models)."""
         )
     }					,			)
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
           raise ValueError(
               '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'  )





@dataclass
class 	a       :
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}    )
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
 _lowerCAmelCase	   =		field(default=lowerCAmelCase__					,			metadata={"""help""": """The input training data file (a text file)."""}    )
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}					,			)
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}					,			)
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}					,			)
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )
 _lowerCAmelCase	   =		field(
     default=5					,			metadata={
         """help""": """The percentage of the train set used as validation set in case there's no validation split"""
     }					,			)
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={
         """help""": (
             """The maximum total input sequence length after tokenization. Sequences longer """
             """than this will be truncated. Default to the max input length of the model."""
         )
     }					,			)
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={"""help""": """The number of processes to use for the preprocessing."""}					,			)
 _lowerCAmelCase	   =		field(
     default=0.15					,			metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}    )
 _lowerCAmelCase	   =		field(
     default=lowerCAmelCase__					,			metadata={
         """help""": (
             """Whether to pad all samples to `max_seq_length`. """
             """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
         )
     }					,			)
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      if self.train_file is not None:
           _a      =							self.train_file.split('.'  )[-1]
           assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
      if self.validation_file is not None:
           _a      =							self.validation_file.split('.'  )[-1]
           assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."






def 		_A			(lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:List[str]						)				->					List[Any]:




     '''simple docstring'''



     with open(_A			,  'r'			,  encoding='utf-8'						) as f:
          _a      =							[json.loads(_A						) for line in f.read().splitlines() if (len(_A						) > 0 and not line.isspace())]
     assert len(_A						) == len(_A						)

     _a      =							{c: dataset[c] for c in dataset.column_names}
     _a      =							refs
     return Dataset.from_dict(_A						)






def 		_A			()				->					List[str]:




     '''simple docstring'''



     _a      =							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.
          _a ,				_a ,				_a      =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          _a ,				_a ,				_a      =							parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     _a      =							None
     if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
          _a      =							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()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('Training/evaluation parameters %s'			,  _A						)

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

     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
     # (the dataset will be downloaded automatically from the datasets Hub).
     #
     # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
     # 'text' is found. You can easily tweak this behavior (see below).
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
     if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          _a      =							load_dataset(data_args.dataset_name			,  data_args.dataset_config_name						)
          if "validation" not in datasets.keys():
               _a      =							load_dataset(
                   data_args.dataset_name			,  data_args.dataset_config_name			,  split=f'train[:{data_args.validation_split_percentage}%]'			,  )
               _a      =							load_dataset(
                   data_args.dataset_name			,  data_args.dataset_config_name			,  split=f'train[{data_args.validation_split_percentage}%:]'			,  )
     else:
          _a      =							{}
          if data_args.train_file is not None:
               _a      =							data_args.train_file
          if data_args.validation_file is not None:
               _a      =							data_args.validation_file
          _a      =							data_args.train_file.split('.'						)[-1]
          if extension == "txt":
               _a      =							'text'
          _a      =							load_dataset(_A			,  data_files=_A						)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     _a      =							{
         'cache_dir': model_args.cache_dir,
         'revision': model_args.model_revision,
         'use_auth_token': True if model_args.use_auth_token else None,
     }
     if model_args.config_name:
          _a      =							AutoConfig.from_pretrained(model_args.config_name			,  **_A						)
     elif model_args.model_name_or_path:
          _a      =							AutoConfig.from_pretrained(model_args.model_name_or_path			,  **_A						)
     else:
          _a      =							CONFIG_MAPPING[model_args.model_type]()
          logger.warning('You are instantiating a new config instance from scratch.'						)
          if model_args.config_overrides is not None:
               logger.info(f'Overriding config: {model_args.config_overrides}'						)
               config.update_from_string(model_args.config_overrides						)
               logger.info(f'New config: {config}'						)

     _a      =							{
         'cache_dir': model_args.cache_dir,
         'use_fast': model_args.use_fast_tokenizer,
         'revision': model_args.model_revision,
         'use_auth_token': True if model_args.use_auth_token else None,
     }
     if model_args.tokenizer_name:
          _a      =							AutoTokenizer.from_pretrained(model_args.tokenizer_name			,  **_A						)
     elif model_args.model_name_or_path:
          _a      =							AutoTokenizer.from_pretrained(model_args.model_name_or_path			,  **_A						)
     else:
          raise ValueError(
              'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
              'You can do it from another script, save it, and load it from here, using --tokenizer_name.'						)

     if model_args.model_name_or_path:
          _a      =							AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path			,  from_tf=bool('.ckpt' in model_args.model_name_or_path						)			,  config=_A			,  cache_dir=model_args.cache_dir			,  revision=model_args.model_revision			,  use_auth_token=True if model_args.use_auth_token else None			,  )
     else:
          logger.info('Training new model from scratch'						)
          _a      =							AutoModelForMaskedLM.from_config(_A						)

     model.resize_token_embeddings(len(_A						)						)

     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
          _a      =							datasets['train'].column_names
     else:
          _a      =							datasets['validation'].column_names
     _a      =							'text' if 'text' in column_names else column_names[0]

     _a      =							'max_length' if data_args.pad_to_max_length else False

     def tokenize_function(lowerCAmelCase__					:List[str]						):
          # Remove empty lines
          _a      =							[line for line in examples['text'] if len(_A						) > 0 and not line.isspace()]
          return tokenizer(examples['text']			,  padding=_A			,  truncation=_A			,  max_length=data_args.max_seq_length						)

     _a      =							datasets.map(
         _A			,  batched=_A			,  num_proc=data_args.preprocessing_num_workers			,  remove_columns=[text_column_name]			,  load_from_cache_file=not data_args.overwrite_cache			,  )

     # Add the chinese references if provided
     if data_args.train_ref_file is not None:
          _a      =							add_chinese_references(tokenized_datasets['train']			,  data_args.train_ref_file						)
     if data_args.validation_ref_file is not None:
          _a      =							add_chinese_references(
              tokenized_datasets['validation']			,  data_args.validation_ref_file						)
     # If we have ref files, need to avoid it removed by trainer
     _a      =							data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          _a      =							False

     # Data collator
     # This one will take care of randomly masking the tokens.
     _a      =							DataCollatorForWholeWordMask(tokenizer=_A			,  mlm_probability=data_args.mlm_probability						)

     # Initialize our Trainer
     _a      =							Trainer(
         model=_A			,  args=_A			,  train_dataset=tokenized_datasets['train'] if training_args.do_train else None			,  eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None			,  tokenizer=_A			,  data_collator=_A			,  )

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               _a      =							last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path						):
               _a      =							model_args.model_name_or_path
          else:
               _a      =							None
          _a      =							trainer.train(resume_from_checkpoint=_A						)
          trainer.save_model()  # Saves the tokenizer too for easy upload

          _a      =							os.path.join(training_args.output_dir			,  'train_results.txt'						)
          if trainer.is_world_process_zero():
               with open(_A			,  'w'						) as writer:
                    logger.info('***** Train results *****'						)
                    for key, value in sorted(train_result.metrics.items()						):
                         logger.info(f'  {key} = {value}'						)
                         writer.write(f'{key} = {value}\n'						)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
               trainer.state.save_to_json(os.path.join(training_args.output_dir			,  'trainer_state.json'						)						)

    # Evaluation
     _a      =							{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***'						)

          _a      =							trainer.evaluate()

          _a      =							math.exp(eval_output['eval_loss']						)
          _a      =							perplexity

          _a      =							os.path.join(training_args.output_dir			,  'eval_results_mlm_wwm.txt'						)
          if trainer.is_world_process_zero():
               with open(_A			,  'w'						) as writer:
                    logger.info('***** Eval results *****'						)
                    for key, value in sorted(results.items()						):
                         logger.info(f'  {key} = {value}'						)
                         writer.write(f'{key} = {value}\n'						)

     return results






def 		_A			(lowerCAmelCase__					:str						)				->					Union[str, Any]:




     '''simple docstring'''



     main()


if __name__ == "__main__":
 main()





'''simple docstring'''






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a_   :      Tuple				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		["""input_features"""]
 def __init__(	self ,			__magic_name__=80 ,			__magic_name__=1_60_00 ,			__magic_name__=1_60 ,			__magic_name__=30 ,			__magic_name__=4_00 ,			__magic_name__=0.0 ,			__magic_name__=False ,			**__magic_name__ ,			)   ->							Optional[int]:
      super().__init__(
          feature_size=__magic_name__ ,			sampling_rate=__magic_name__ ,			padding_value=__magic_name__ ,			return_attention_mask=__magic_name__ ,			**__magic_name__ ,			)
      _a      =							n_fft
      _a      =							hop_length
      _a      =							chunk_length
      _a      =							chunk_length * sampling_rate
      _a      =							self.n_samples // hop_length
      _a      =							sampling_rate
      _a      =							mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2 ,			num_mel_filters=__magic_name__ ,			min_frequency=0.0 ,			max_frequency=8_0_0_0.0 ,			sampling_rate=__magic_name__ ,			norm='slaney' ,			mel_scale='slaney' ,			)
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							np.ndarray:
      _a      =							spectrogram(
          __magic_name__ ,			window_function(self.n_fft ,			'hann'  ) ,			frame_length=self.n_fft ,			hop_length=self.hop_length ,			power=2.0 ,			mel_filters=self.mel_filters ,			log_mel='log10' ,			)
      _a      =							log_spec[:, :-1]
      _a      =							np.maximum(__magic_name__ ,			log_spec.max() - 8.0  )
      _a      =							(log_spec + 4.0) / 4.0
      return log_spec
 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 							__UpperCAmelCase						(	__magic_name__ ,			__magic_name__ ,			__magic_name__ = 0.0  )   ->							List[np.ndarray]:
      if attention_mask is not None:
           _a      =							np.array(__magic_name__ ,			np.intaa  )
           _a      =							[]

           for vector, length in zip(__magic_name__ ,			attention_mask.sum(-1  )  ):
                _a      =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7  )
                if length < normed_slice.shape[0]:
                     _a      =							padding_value

                normed_input_values.append(__magic_name__  )
      else:
           _a      =							[(x - x.mean()) / np.sqrt(x.var() + 1e-7  ) for x in input_values]

      return normed_input_values
 def __call__(	self ,			__magic_name__ ,			__magic_name__ = True ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = "max_length" ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							BatchFeature:

      if sampling_rate is not None:
           if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                    f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                    f' was sampled with {self.sampling_rate} and not {sampling_rate}.'  )
      else:
           logger.warning(
               'It is strongly recommended to pass the `sampling_rate` argument to this function. '
               'Failing to do so can result in silent errors that might be hard to debug.'  )

      _a      =							isinstance(__magic_name__ ,			np.ndarray  ) and len(raw_speech.shape  ) > 1
      if is_batched_numpy and len(raw_speech.shape  ) > 2:
           raise ValueError(f'Only mono-channel audio is supported for input to {self}'  )
      _a      =							is_batched_numpy or (
          isinstance(__magic_name__ ,			(list, tuple)  ) and (isinstance(raw_speech[0] ,			(np.ndarray, tuple, list)  ))
      )

      if is_batched:
           _a      =							[np.asarray([speech] ,			dtype=np.floataa  ).T for speech in raw_speech]
      elif not is_batched and not isinstance(__magic_name__ ,			np.ndarray  ):
           _a      =							np.asarray(__magic_name__ ,			dtype=np.floataa  )
      elif isinstance(__magic_name__ ,			np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
           _a      =							raw_speech.astype(np.floataa  )

      # always return batch
      if not is_batched:
           _a      =							[np.asarray([raw_speech]  ).T]

      _a      =							BatchFeature({'input_features': raw_speech}  )

      # convert into correct format for padding

      _a      =							self.pad(
          __magic_name__ ,			padding=__magic_name__ ,			max_length=max_length if max_length else self.n_samples ,			truncation=__magic_name__ ,			pad_to_multiple_of=__magic_name__ ,			return_attention_mask=return_attention_mask or do_normalize ,			)

      # zero-mean and unit-variance normalization
      if do_normalize:
           _a      =							self.zero_mean_unit_var_norm(
               padded_inputs['input_features'] ,			attention_mask=padded_inputs['attention_mask'] ,			padding_value=self.padding_value ,			)
           _a      =							np.stack(padded_inputs['input_features'] ,			axis=0  )

      # make sure list is in array format
      _a      =							padded_inputs.get('input_features'  ).transpose(2 ,			0 ,			1  )

      _a      =							[self._np_extract_fbank_features(__magic_name__  ) for waveform in input_features[0]]

      if isinstance(input_features[0] ,			__magic_name__  ):
           _a      =							[np.asarray(__magic_name__ ,			dtype=np.floataa  ) for feature in input_features]
      else:
           _a      =							input_features

      if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           _a      =							padded_inputs['attention_mask'][:, :: self.hop_length]

      if return_tensors is not None:
           _a      =							padded_inputs.convert_to_tensors(__magic_name__  )

      return padded_inputs




 def 							__UpperCAmelCase						(	self  )   ->							Dict[str, Any]:
      _a      =							copy.deepcopy(self.__dict__  )
      _a      =							self.__class__.__name__
      if "mel_filters" in output:
           del output["mel_filters"]
      return output

