






from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowerCamelCase					    =	load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
lowerCamelCase					    =	train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

lowerCamelCase					    =	KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowerCamelCase					    =	[[1, 2, 1, 4], [2, 3, 4, 5]]

lowerCamelCase					    =	knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)



import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
       from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
       UpperCamelCase__	:   str								=     True
       from torch.cuda.amp import autocast


UpperCamelCase__	:   Tuple								=     logging.getLogger(__name__)



@dataclass
class    __snake_case	:
  __lowerCAmelCase  :						str    			=	field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}							)
  __lowerCAmelCase  :						Optional[str]    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,			)
  __lowerCAmelCase  :						Optional[bool]    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}							)
  __lowerCAmelCase  :						Optional[bool]    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'Whether to log verbose messages or not.'}  ,			)






def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		ModelArguments			, _SCREAMING_SNAKE_CASE       :		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							)]			, )
    SCREAMING_SNAKE_CASE_						=    logging.WARNING
    if model_args.verbose_logging:
        SCREAMING_SNAKE_CASE_						=    logging.DEBUG
    elif trainer_utils.is_main_process(training_args.local_rank							):
        SCREAMING_SNAKE_CASE_						=    logging.INFO
    logger.setLevel(_SCREAMING_SNAKE_CASE							)



@dataclass
class    __snake_case	:
  __lowerCAmelCase  :						str    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'The name of the dataset to use (via the datasets library).'}							)
  __lowerCAmelCase  :						Optional[str]    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}							)
  __lowerCAmelCase  :						Optional[str]    			=	field(
      default='train'  ,			metadata={
          'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
      }  ,			)
  __lowerCAmelCase  :						Optional[str]    			=	field(
      default='validation'  ,			metadata={
          'help': (
              'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
          )
      }  ,			)
  __lowerCAmelCase  :						Optional[str]    			=	field(
      default='text'  ,			metadata={'help': 'Column in the dataset that contains label (target text). Defaults to \'text\''}  ,			)
  __lowerCAmelCase  :						Optional[str]    			=	field(
      default='file'  ,			metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}  ,			)
  __lowerCAmelCase  :						Optional[bool]    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'Resample loaded audio to target feature extractor\'s sampling rate or not.'}  ,			)
  __lowerCAmelCase  :						Optional[float]    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'Filters out examples longer than specified. Defaults to no filtering.'}  ,			)
  __lowerCAmelCase  :						Optional[str]    			=	field(
      default='librispeech'  ,			metadata={
          'help': (
              'Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'
              ' \'buckwalter\'.'
          )
      }  ,			)
  __lowerCAmelCase  :						bool    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}							)
  __lowerCAmelCase  :						Optional[int]    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'The number of processes to use for the preprocessing.'}  ,			)






@dataclass
class    __snake_case	:
  __lowerCAmelCase  :						bool    			=	False
  __lowerCAmelCase  :						Optional[str]    			=	None
  __lowerCAmelCase  :						Optional[str]    			=	"|"
  __lowerCAmelCase  :						Optional[Dict[str, str]]    			=	field(default_factory=lowerCAmelCase__							)
  __lowerCAmelCase  :						Optional[Set[str]]    			=	field(default_factory=lowerCAmelCase__							)
  __lowerCAmelCase  :						Optional[Callable[[str], str]]    			=	None

  @classmethod
  def     lowerCAmelCase__  (       cls    ,		_A):
      if name == "librispeech":
          return cls()
      if name == "timit":
          return cls(
              do_lower_case=_A    ,		translation_table=str.maketrans({'-': ' '})    ,		)
      if name == "buckwalter":
          SCREAMING_SNAKE_CASE_						=    {
              '-': ' ',  # sometimes used to represent pauses
              '^': 'v',  # fixing "tha" in arabic_speech_corpus dataset
          }
          return cls(
              vocab_file=pathlib.Path(_A).parent.joinpath('vocab/buckwalter.json')    ,		word_delimiter_token='/'    ,		translation_table=str.maketrans(_A)    ,		words_to_remove={'sil'}    ,		untransliterator=arabic.buckwalter.untransliterate    ,		)
      raise ValueError(f"""Unsupported orthography: '{name}'.""")

  def     lowerCAmelCase__  (       self    ,		_A):
      # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
      if len(self.translation_table) > 0:
          SCREAMING_SNAKE_CASE_						=    text.translate(self.translation_table)
      if len(self.words_to_remove) == 0:
          SCREAMING_SNAKE_CASE_						=    ' '.join(text.split())  # clean up whitespaces
      else:
          SCREAMING_SNAKE_CASE_						=    ' '.join(w for w in text.split() if w not in self.words_to_remove)  # and clean up whilespaces
      return text


  def     lowerCAmelCase__  (       self    ,		_A):
      SCREAMING_SNAKE_CASE_						=    WavaVecaFeatureExtractor.from_pretrained(
          model_args.model_name_or_path    ,		cache_dir=model_args.cache_dir)
      if self.vocab_file:
          SCREAMING_SNAKE_CASE_						=    WavaVecaCTCTokenizer(
              self.vocab_file    ,		cache_dir=model_args.cache_dir    ,		do_lower_case=self.do_lower_case    ,		word_delimiter_token=self.word_delimiter_token    ,		)
      else:
          SCREAMING_SNAKE_CASE_						=    WavaVecaCTCTokenizer.from_pretrained(
              model_args.model_name_or_path    ,		cache_dir=model_args.cache_dir    ,		do_lower_case=self.do_lower_case    ,		word_delimiter_token=self.word_delimiter_token    ,		)
      return WavaVecaProcessor(_A    ,		_A)






@dataclass
class    __snake_case	:
  __lowerCAmelCase  :						WavaVecaProcessor
  __lowerCAmelCase  :						Union[bool, str]    			=	True
  __lowerCAmelCase  :						Optional[int]    			=	None
  __lowerCAmelCase  :						Optional[int]    			=	None
  __lowerCAmelCase  :						Optional[int]    			=	None
  __lowerCAmelCase  :						Optional[int]    			=	None

  def __call__(       self    ,		_A):
      # 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(
          _A    ,		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=_A    ,		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    __snake_case	(     lowerCAmelCase__							):

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A):
      model.train()
      SCREAMING_SNAKE_CASE_						=    self._prepare_inputs(_A)

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

      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(_A).backward()
      elif self.use_apex:
          with amp.scale_loss(_A    ,		self.optimizer) as scaled_loss:
              scaled_loss.backward()
      elif self.deepspeed:
          self.deepspeed.backward(_A)
      else:
          loss.backward()

      return loss.detach()






def        _UpperCAmelCase							(	):





    """simple docstring"""


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

    SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    parser.parse_args_into_dataclasses()
    configure_logger(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)

    SCREAMING_SNAKE_CASE_						=    Orthography.from_name(data_args.orthography.lower()							)
    SCREAMING_SNAKE_CASE_						=    orthography.create_processor(_SCREAMING_SNAKE_CASE							)
    SCREAMING_SNAKE_CASE_						=    WavaVecaForCTC.from_pretrained(
        model_args.model_name_or_path			, cache_dir=model_args.cache_dir			, gradient_checkpointing=training_args.gradient_checkpointing			, vocab_size=len(processor.tokenizer							)			, )

    SCREAMING_SNAKE_CASE_						=    datasets.load_dataset(
        data_args.dataset_name			, data_args.dataset_config_name			, split=data_args.train_split_name							)
    SCREAMING_SNAKE_CASE_						=    datasets.load_dataset(
        data_args.dataset_name			, data_args.dataset_config_name			, split=data_args.validation_split_name							)

    SCREAMING_SNAKE_CASE_						=    datasets.load_metric('wer'							)
    SCREAMING_SNAKE_CASE_						=    processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
    SCREAMING_SNAKE_CASE_						=    ''.join(t for t in processor.tokenizer.get_vocab().keys() if len(_SCREAMING_SNAKE_CASE							) == 1							)
    SCREAMING_SNAKE_CASE_						=    re.compile(  # remove characters not in vocabulary
        rf"""[^\s{re.escape(_SCREAMING_SNAKE_CASE							)}]"""			, flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0			, )
    SCREAMING_SNAKE_CASE_						=    []

    def prepare_example(_SCREAMING_SNAKE_CASE       :		Any							):  # TODO(elgeish) make use of multiprocessing?
        SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    librosa.load(example[data_args.speech_file_column]			, sr=_SCREAMING_SNAKE_CASE							)
        if data_args.max_duration_in_seconds is not None:
            SCREAMING_SNAKE_CASE_						=    len(example['speech']							) / example['sampling_rate']
        # Normalize and clean up text; order matters!
        SCREAMING_SNAKE_CASE_						=    orthography.preprocess_for_training(example[data_args.target_text_column]							)
        SCREAMING_SNAKE_CASE_						=    vocabulary_text_cleaner.sub(''			, _SCREAMING_SNAKE_CASE							)
        if updated_text != example[data_args.target_text_column]:
            text_updates.append((example[data_args.target_text_column], updated_text)							)
            SCREAMING_SNAKE_CASE_						=    updated_text
        return example

    SCREAMING_SNAKE_CASE_						=    train_dataset.map(_SCREAMING_SNAKE_CASE			, remove_columns=[data_args.speech_file_column]							)
    SCREAMING_SNAKE_CASE_						=    val_dataset.map(_SCREAMING_SNAKE_CASE			, remove_columns=[data_args.speech_file_column]							)

    if data_args.max_duration_in_seconds is not None:

        def filter_by_max_duration(_SCREAMING_SNAKE_CASE       :		Tuple							):
            return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

        SCREAMING_SNAKE_CASE_						=    len(_SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    len(_SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    train_dataset.filter(_SCREAMING_SNAKE_CASE			, remove_columns=['duration_in_seconds']							)
        SCREAMING_SNAKE_CASE_						=    val_dataset.filter(_SCREAMING_SNAKE_CASE			, remove_columns=['duration_in_seconds']							)
        if len(_SCREAMING_SNAKE_CASE							) > old_train_size:
            logger.warning(
                f"""Filtered out {len(_SCREAMING_SNAKE_CASE							) - old_train_size} train example(s) longer than"""
                f""" {data_args.max_duration_in_seconds} second(s)."""							)
        if len(_SCREAMING_SNAKE_CASE							) > old_val_size:
            logger.warning(
                f"""Filtered out {len(_SCREAMING_SNAKE_CASE							) - old_val_size} validation example(s) longer than"""
                f""" {data_args.max_duration_in_seconds} second(s)."""							)
    logger.info(f"""Split sizes: {len(_SCREAMING_SNAKE_CASE							)} train and {len(_SCREAMING_SNAKE_CASE							)} validation."""							)

    logger.warning(f"""Updated {len(_SCREAMING_SNAKE_CASE							)} transcript(s) using '{data_args.orthography}' orthography rules."""							)
    if logger.isEnabledFor(logging.DEBUG							):
        for original_text, updated_text in text_updates:
            logger.debug(f"""Updated text: \"{original_text}\" -> \"{updated_text}\""""							)
    SCREAMING_SNAKE_CASE_						=    None

    def prepare_dataset(_SCREAMING_SNAKE_CASE       :		List[Any]							):
        # 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[data_args.target_text_column]			, sampling_rate=batch['sampling_rate'][0]							)
        batch.update(_SCREAMING_SNAKE_CASE							)
        return batch

    SCREAMING_SNAKE_CASE_						=    train_dataset.map(
        _SCREAMING_SNAKE_CASE			, batch_size=training_args.per_device_train_batch_size			, batched=_SCREAMING_SNAKE_CASE			, num_proc=data_args.preprocessing_num_workers			, )
    SCREAMING_SNAKE_CASE_						=    val_dataset.map(
        _SCREAMING_SNAKE_CASE			, batch_size=training_args.per_device_train_batch_size			, batched=_SCREAMING_SNAKE_CASE			, num_proc=data_args.preprocessing_num_workers			, )

    SCREAMING_SNAKE_CASE_						=    DataCollatorCTCWithPadding(processor=_SCREAMING_SNAKE_CASE			, padding=_SCREAMING_SNAKE_CASE							)

    def compute_metrics(_SCREAMING_SNAKE_CASE       :		Optional[int]							):
        SCREAMING_SNAKE_CASE_						=    pred.predictions
        SCREAMING_SNAKE_CASE_						=    np.argmax(_SCREAMING_SNAKE_CASE			, axis=-1							)

        SCREAMING_SNAKE_CASE_						=    processor.tokenizer.pad_token_id

        SCREAMING_SNAKE_CASE_						=    processor.batch_decode(_SCREAMING_SNAKE_CASE							)
        # we do not want to group tokens when computing the metrics
        SCREAMING_SNAKE_CASE_						=    processor.batch_decode(pred.label_ids			, group_tokens=_SCREAMING_SNAKE_CASE							)
        if logger.isEnabledFor(logging.DEBUG							):
            for reference, predicted in zip(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
                logger.debug(f"""reference: \"{reference}\""""							)
                logger.debug(f"""predicted: \"{predicted}\""""							)
                if orthography.untransliterator is not None:
                    logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(_SCREAMING_SNAKE_CASE							)}\""""							)
                    logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(_SCREAMING_SNAKE_CASE							)}\""""							)

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

        return {"wer": wer}

    if model_args.freeze_feature_extractor:
        model.freeze_feature_extractor()

    SCREAMING_SNAKE_CASE_						=    CTCTrainer(
        model=_SCREAMING_SNAKE_CASE			, data_collator=_SCREAMING_SNAKE_CASE			, args=_SCREAMING_SNAKE_CASE			, compute_metrics=_SCREAMING_SNAKE_CASE			, train_dataset=_SCREAMING_SNAKE_CASE			, eval_dataset=_SCREAMING_SNAKE_CASE			, tokenizer=processor.feature_extractor			, )

    trainer.train()


if __name__ == "__main__":
       main()


