'''simple docstring'''


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"""):
       __snake_case						  =		True
       from torch.cuda.amp import autocast

__snake_case						  =		logging.getLogger(__name__)

def   A_      (       SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None						)			->str:
  return field(default_factory=lambda: default ,	metadata=SCREAMING_SNAKE_CASE_						)





@dataclass
class       _a    :


  """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=0.05 ,				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       _a    :


  """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       _a    :


  """simple docstring"""

  A_    							=						42
  A_    							=						True
  A_    							=						None
  A_    							=						None
  A_    							=						None
  A_    							=						None




  def __call__(		self      :       List[Any]   ,				lowercase_      :       List[Dict[str, Union[List[int], torch.Tensor]]]      ):







    '''simple docstring'''


    lowercase_     =       [{"""input_values""": feature["""input_values"""]} for feature in features]
    lowercase_     =       [{"""input_ids""": feature["""labels"""]} for feature in features]

    lowercase_     =       self.processor.pad(
        lowercase_   ,				padding=self.padding   ,				max_length=self.max_length   ,				pad_to_multiple_of=self.pad_to_multiple_of   ,				return_tensors="""pt"""   ,				)
    lowercase_     =       self.processor.pad(
        labels=lowercase_   ,				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
    lowercase_     =       labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1      )   ,				-100      )

    lowercase_     =       labels

    return batch






class       _a    (		__a      ):


  """simple docstring"""

  def 		lowerCamelCase__  (		self      :       Optional[int]   ,				lowercase_      :       nn.Module   ,				lowercase_      :       Dict[str, Union[torch.Tensor, Any]]      ):







    '''simple docstring'''


    model.train()
    lowercase_     =       self._prepare_inputs(lowercase_      )

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

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

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

    return loss.detach()






def   A_      (       )			->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_     =       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.
    lowercase_     ,	lowercase_     ,	lowercase_     =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
  else:
    lowercase_     ,	lowercase_     ,	lowercase_     =       parser.parse_args_into_dataclasses()

  # Detecting last checkpoint.
  lowercase_     =       None
  if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
    lowercase_     =       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""" ,	SCREAMING_SNAKE_CASE_						)

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

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

  # Create and save tokenizer
  lowercase_     =       f"""[{"".join(data_args.chars_to_ignore						)}]"""

  def remove_special_characters(SCREAMING_SNAKE_CASE_						):
    lowercase_     =       re.sub(SCREAMING_SNAKE_CASE_ ,	"""""" ,	batch["""sentence"""]						).lower() + """ """
    return batch

  lowercase_     =       train_dataset.map(SCREAMING_SNAKE_CASE_ ,	remove_columns=["""sentence"""]						)
  lowercase_     =       eval_dataset.map(SCREAMING_SNAKE_CASE_ ,	remove_columns=["""sentence"""]						)

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

  lowercase_     =       train_dataset.map(
      SCREAMING_SNAKE_CASE_ ,	batched=SCREAMING_SNAKE_CASE_ ,	batch_size=-1 ,	keep_in_memory=SCREAMING_SNAKE_CASE_ ,	remove_columns=train_dataset.column_names ,	)
  lowercase_     =       train_dataset.map(
      SCREAMING_SNAKE_CASE_ ,	batched=SCREAMING_SNAKE_CASE_ ,	batch_size=-1 ,	keep_in_memory=SCREAMING_SNAKE_CASE_ ,	remove_columns=eval_dataset.column_names ,	)

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

  with open("""vocab.json""" ,	"""w"""						) as vocab_file:
    json.dump(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  lowercase_     =       WavaVecaCTCTokenizer(
      """vocab.json""" ,	unk_token="""[UNK]""" ,	pad_token="""[PAD]""" ,	word_delimiter_token="""|""" ,	)
  lowercase_     =       WavaVecaFeatureExtractor(
      feature_size=1 ,	sampling_rate=1_60_00 ,	padding_value=0.0 ,	do_normalize=SCREAMING_SNAKE_CASE_ ,	return_attention_mask=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_ ,	tokenizer=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       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:
    lowercase_     =       min(len(SCREAMING_SNAKE_CASE_						) ,	data_args.max_train_samples						)
    lowercase_     =       train_dataset.select(range(SCREAMING_SNAKE_CASE_						)						)

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

  lowercase_     =       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(SCREAMING_SNAKE_CASE_						):
    lowercase_     ,	lowercase_     =       torchaudio.load(batch["""path"""]						)
    lowercase_     =       resampler(SCREAMING_SNAKE_CASE_						).squeeze().numpy()
    lowercase_     =       1_60_00
    lowercase_     =       batch["""text"""]
    return batch

  lowercase_     =       train_dataset.map(
      SCREAMING_SNAKE_CASE_ ,	remove_columns=train_dataset.column_names ,	num_proc=data_args.preprocessing_num_workers ,	)
  lowercase_     =       eval_dataset.map(
      SCREAMING_SNAKE_CASE_ ,	remove_columns=eval_dataset.column_names ,	num_proc=data_args.preprocessing_num_workers ,	)

  def prepare_dataset(SCREAMING_SNAKE_CASE_						):
    # 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}."""

    lowercase_     =       processor(
        audio=batch["""speech"""] ,	text=batch["""target_text"""] ,	sampling_rate=batch["""sampling_rate"""][0]						)
    batch.update(SCREAMING_SNAKE_CASE_						)
    return batch

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

  # Metric
  lowercase_     =       datasets.load_metric("""wer"""						)

  def compute_metrics(SCREAMING_SNAKE_CASE_						):
    lowercase_     =       pred.predictions
    lowercase_     =       np.argmax(SCREAMING_SNAKE_CASE_ ,	axis=-1						)

    lowercase_     =       processor.tokenizer.pad_token_id

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

    lowercase_     =       wer_metric.compute(predictions=SCREAMING_SNAKE_CASE_ ,	references=SCREAMING_SNAKE_CASE_						)

    return {"wer": wer}

  if model_args.freeze_feature_extractor:
    model.freeze_feature_extractor()

  # Data collator
  lowercase_     =       DataCollatorCTCWithPadding(processor=SCREAMING_SNAKE_CASE_ ,	padding=SCREAMING_SNAKE_CASE_						)

  # Initialize our Trainer
  lowercase_     =       CTCTrainer(
      model=SCREAMING_SNAKE_CASE_ ,	data_collator=SCREAMING_SNAKE_CASE_ ,	args=SCREAMING_SNAKE_CASE_ ,	compute_metrics=SCREAMING_SNAKE_CASE_ ,	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:
      lowercase_     =       last_checkpoint
    elif os.path.isdir(model_args.model_name_or_path						):
      lowercase_     =       model_args.model_name_or_path
    else:
      lowercase_     =       None

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

    lowercase_     =       trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE_						)
    trainer.save_model()

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

    trainer.log_metrics("""train""" ,	SCREAMING_SNAKE_CASE_						)
    trainer.save_metrics("""train""" ,	SCREAMING_SNAKE_CASE_						)
    trainer.save_state()

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

    trainer.log_metrics("""eval""" ,	SCREAMING_SNAKE_CASE_						)
    trainer.save_metrics("""eval""" ,	SCREAMING_SNAKE_CASE_						)

  return results


if __name__ == "__main__":
       main()


'''simple docstring'''


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
       import torch


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


__snake_case						  =		logging.get_logger(__name__)
__snake_case						  =		{"""vocab_file""": """spiece.model"""}

__snake_case						  =		{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

__snake_case						  =		{
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						VOCAB_FILES_NAMES
  A_    							=						PRETRAINED_VOCAB_FILES_MAP
  A_    							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_    							=						['''input_ids''', '''attention_mask''']




  def __init__(		self      :       str   ,				lowercase_      :       List[str]   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       Optional[int]=False   ,				lowercase_      :       Dict=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       List[Any]=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       Optional[Dict[str, Any]] = None   ,				**lowercase_      :       Tuple   ,				):







    '''simple docstring'''


    lowercase_     =       {} if sp_model_kwargs is None else sp_model_kwargs

    lowercase_     =       kwargs.get("""name_or_path"""      )
    if name_or_path is None:
      logger.warning(
          """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
          """ you are testing the model, this can safely be ignored"""      )
      lowercase_     =       """None"""

    # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
    lowercase_     =       """<|endoftext|>""" if eos_token is None else eos_token
    lowercase_     =       """<unk>""" if unk_token is None else unk_token
    if "gpt-sw3-7b" in name_or_path:
      lowercase_     =       unk_token if pad_token is None else pad_token
      lowercase_     =       eos_token if bos_token is None else bos_token
    else:
      lowercase_     =       """<pad>""" if pad_token is None else pad_token
      lowercase_     =       """<s>""" if bos_token is None else bos_token

    super().__init__(
        do_lower_case=lowercase_   ,				remove_space=lowercase_   ,				keep_accents=lowercase_   ,				bos_token=lowercase_   ,				eos_token=lowercase_   ,				unk_token=lowercase_   ,				pad_token=lowercase_   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**lowercase_   ,				)

    lowercase_     =       do_lower_case
    lowercase_     =       remove_space
    lowercase_     =       keep_accents
    lowercase_     =       vocab_file

    lowercase_     =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(lowercase_      )

    # Used for whitespace normalization in input texts
    # fmt : off
    lowercase_     =       {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
    # fmt : on

    # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
    lowercase_     =       re.compile(
        F"""[{"".join(map(lowercase_   ,				list(range(0   ,				9      )      ) + list(range(11   ,				32      )      ) + list(range(127   ,				160      )      ) + [160, 173, 8_203]      )      )}]"""      )




  def __getstate__(		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       self.__dict__.copy()
    lowercase_     =       None
    return state




  def __setstate__(		self      :       Tuple   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       d

    # for backward compatibility
    if not hasattr(self   ,				"""sp_model_kwargs"""      ):
      lowercase_     =       {}

    lowercase_     =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(self.vocab_file      )




  @property
  # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    return len(self.sp_model      )




  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.non_printing_characters_re.sub(""""""   ,				lowercase_      )

    # Normalize whitespaces
    lowercase_     =       """""".join([char if char not in self.whitespaces else """ """ for char in text]      )

    # NFC Unicode normalization
    lowercase_     =       unicodedata.normalize("""NFC"""   ,				lowercase_      )
    return text




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       str   ,				**lowercase_      :       Any      ):







    '''simple docstring'''


    lowercase_     =       self.preprocess_text(lowercase_      )
    return self.sp_model.encode(lowercase_   ,				out_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       str      ):







    '''simple docstring'''


    return self.sp_model.PieceToId(lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       int      ):







    '''simple docstring'''


    return self.sp_model.IdToPiece(lowercase_      )




  @staticmethod
  def 		lowerCamelCase__  (		lowercase_      :       str      ):







    '''simple docstring'''


    return out_string




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       []
    lowercase_     =       """"""
    lowercase_     =       False
    for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
        # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
        if not prev_is_special:
          out_string += " "

        out_string += self.sp_model.decode(lowercase_      ) + token
        lowercase_     =       True
        lowercase_     =       []
      else:
        current_sub_tokens.append(lowercase_      )
        lowercase_     =       False
    out_string += self.sp_model.decode(lowercase_      )

    return out_string




  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       {self.convert_ids_to_tokens(lowercase_      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab




  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       str   ,				lowercase_      :       Optional[str] = None      ):







    '''simple docstring'''


    if not os.path.isdir(lowercase_      ):
      logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
      return
    lowercase_     =       os.path.join(
        lowercase_   ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

    if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowercase_      ) and os.path.isfile(self.vocab_file      ):
      copyfile(self.vocab_file   ,				lowercase_      )
    elif not os.path.isfile(self.vocab_file      ):
      with open(lowercase_   ,				"""wb"""      ) as fi:
        lowercase_     =       self.sp_model.serialized_model_proto()
        fi.write(lowercase_      )

    return (out_vocab_file,)




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       Union[str, List[str]]   ,				lowercase_      :       Union[str, bool] = False      ):







    '''simple docstring'''


    if isinstance(lowercase_   ,				lowercase_      ):
      lowercase_     =       self.preprocess_text(lowercase_      )
      lowercase_     =       self.sp_model.encode(lowercase_      )
    else:
      lowercase_     =       [self.preprocess_text(lowercase_      ) for t in text]
      lowercase_     =       self.sp_model.encode(lowercase_      )

    if return_tensors is True or return_tensors == "pt":
      lowercase_     =       torch.tensor(lowercase_      )

    return token_ids




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       Union[int, List[int]]      ):







    '''simple docstring'''


    return self.sp_model.decode(lowercase_      )





  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       "Conversation"      ):







    '''simple docstring'''


    lowercase_     =       [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
    lowercase_     =       (
        F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(lowercase_      ) + F"""{self.bos_token}Bot:"""
    )
    return self.encode(text=lowercase_      )

