

import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


a_     :    Any     		=   logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")


def 							__lowerCAmelCase     (							_UpperCamelCase		:	np.ndarray		,				_UpperCamelCase		:	float		,				_UpperCamelCase		:	int = 1_60_00						)		->				Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      int(round(sample_rate * max_length						)						)
 if len(_UpperCamelCase						) <= sample_length:
  return wav
 SCREAMING_SNAKE_CASE		    =      randint(0		,				len(_UpperCamelCase						) - sample_length - 1						)
 return wav[random_offset : random_offset + sample_length]




@dataclass
class   UpperCamelCase			:
    __UpperCamelCase    =field(default=SCREAMING_SNAKE_CASE						,      metadata={"help": "Name of a dataset from the datasets package"} )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={"help": "A file containing the training audio paths and labels."} )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={"help": "A file containing the validation audio paths and labels."} )
    __UpperCamelCase    =field(
        default="train"						,      metadata={
            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
        }						,      )
    __UpperCamelCase    =field(
        default="validation"						,      metadata={
            "help": (
                "The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
            )
        }						,      )
    __UpperCamelCase    =field(
        default="audio"						,      metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}						,      )
    __UpperCamelCase    =field(
        default="label"						,      metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"} )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        }						,      )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        }						,      )
    __UpperCamelCase    =field(
        default=20						,      metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."}						,      )




@dataclass
class   UpperCamelCase			:
    __UpperCamelCase    =field(
        default="facebook/wav2vec2-base"						,      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						,      )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={"help": "Pretrained config name or path if not the same as model_name"} )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} )
    __UpperCamelCase    =field(
        default="main"						,      metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}						,      )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={"help": "Name or path of preprocessor config."} )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={"help": "Whether to freeze the feature encoder layers of the model."} )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={"help": "Whether to generate an attention mask in the feature extractor."} )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        }						,      )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
    __UpperCamelCase    =field(
        default=SCREAMING_SNAKE_CASE						,      metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}						,      )

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     if not self.freeze_feature_extractor and self.freeze_feature_encoder:
      warnings.warn(
          'The argument `--freeze_feature_extractor` is deprecated and '
          'will be removed in a future version. Use `--freeze_feature_encoder`'
          'instead. Setting `freeze_feature_encoder==True`.'				,     snake_case__				,     )
     if self.freeze_feature_extractor and not self.freeze_feature_encoder:
      raise ValueError(
          'The argument `--freeze_feature_extractor` is deprecated and '
          'should not be used in combination with `--freeze_feature_encoder`.'
          'Only make use of `--freeze_feature_encoder`.'     )


def 							__lowerCAmelCase     (							)		->				List[Any]:
 '''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()

 # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
 # information sent is the one passed as arguments along with your Python/PyTorch versions.
 send_example_telemetry('run_audio_classification'		,				_UpperCamelCase		,				_UpperCamelCase						)

 # 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						)]		,				)

 if training_args.should_log:
  # The default of training_args.log_level is passive, so we set log level at info here to have that default.
  transformers.utils.logging.set_verbosity_info()

 SCREAMING_SNAKE_CASE		    =      training_args.get_process_log_level()
 logger.setLevel(_UpperCamelCase						)
 transformers.utils.logging.set_verbosity(_UpperCamelCase						)
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()

 # 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}"""						)
 logger.info(f"""Training/evaluation parameters {training_args}"""						)

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

 # 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 train from scratch.'						)
  elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.'						)

    # Initialize our dataset and prepare it for the audio classification task.
 SCREAMING_SNAKE_CASE		    =      DatasetDict()
 SCREAMING_SNAKE_CASE		    =      load_dataset(
     data_args.dataset_name		,				data_args.dataset_config_name		,				split=data_args.train_split_name		,				use_auth_token=True if model_args.use_auth_token else None		,				)
 SCREAMING_SNAKE_CASE		    =      load_dataset(
     data_args.dataset_name		,				data_args.dataset_config_name		,				split=data_args.eval_split_name		,				use_auth_token=True if model_args.use_auth_token else None		,				)

 if data_args.audio_column_name not in raw_datasets["train"].column_names:
  raise ValueError(
      f"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
      'Make sure to set `--audio_column_name` to the correct audio column - one of '
      f"""{', '.join(raw_datasets['train'].column_names						)}."""						)

 if data_args.label_column_name not in raw_datasets["train"].column_names:
  raise ValueError(
      f"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
      'Make sure to set `--label_column_name` to the correct text column - one of '
      f"""{', '.join(raw_datasets['train'].column_names						)}."""						)

 # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
 # transformer outputs in the classifier, but it doesn't always lead to better accuracy
 SCREAMING_SNAKE_CASE		    =      AutoFeatureExtractor.from_pretrained(
     model_args.feature_extractor_name or model_args.model_name_or_path		,				return_attention_mask=model_args.attention_mask		,				cache_dir=model_args.cache_dir		,				revision=model_args.model_revision		,				use_auth_token=True if model_args.use_auth_token else None		,				)

 # `datasets` takes care of automatically loading and resampling the audio,
 # so we just need to set the correct target sampling rate.
 SCREAMING_SNAKE_CASE		    =      raw_datasets.cast_column(
     data_args.audio_column_name		,				datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate						)						)

 SCREAMING_SNAKE_CASE		    =      feature_extractor.model_input_names[0]

 def train_transforms(_UpperCamelCase		:	Union[str, Any]						):
  SCREAMING_SNAKE_CASE		    =      []
  for audio in batch[data_args.audio_column_name]:
   SCREAMING_SNAKE_CASE		    =      random_subsample(
       audio['array']		,				max_length=data_args.max_length_seconds		,				sample_rate=feature_extractor.sampling_rate						)
   subsampled_wavs.append(_UpperCamelCase						)
  SCREAMING_SNAKE_CASE		    =      feature_extractor(_UpperCamelCase		,				sampling_rate=feature_extractor.sampling_rate						)
  SCREAMING_SNAKE_CASE		    =      {model_input_name: inputs.get(_UpperCamelCase						)}
  SCREAMING_SNAKE_CASE		    =      list(batch[data_args.label_column_name]						)

  return output_batch

 def val_transforms(_UpperCamelCase		:	List[Any]						):
  SCREAMING_SNAKE_CASE		    =      [audio['array'] for audio in batch[data_args.audio_column_name]]
  SCREAMING_SNAKE_CASE		    =      feature_extractor(_UpperCamelCase		,				sampling_rate=feature_extractor.sampling_rate						)
  SCREAMING_SNAKE_CASE		    =      {model_input_name: inputs.get(_UpperCamelCase						)}
  SCREAMING_SNAKE_CASE		    =      list(batch[data_args.label_column_name]						)

  return output_batch

 # Prepare label mappings.
 # We'll include these in the model's config to get human readable labels in the Inference API.
 SCREAMING_SNAKE_CASE		    =      raw_datasets['train'].features[data_args.label_column_name].names
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      {}, {}
 for i, label in enumerate(_UpperCamelCase						):
  SCREAMING_SNAKE_CASE		    =      str(_UpperCamelCase						)
  SCREAMING_SNAKE_CASE		    =      label

 # Load the accuracy metric from the datasets package
 SCREAMING_SNAKE_CASE		    =      evaluate.load('accuracy'						)

 # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
 # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
 def compute_metrics(_UpperCamelCase		:	Optional[Any]						):
  SCREAMING_SNAKE_CASE		    =      np.argmax(eval_pred.predictions		,				axis=1						)
  return metric.compute(predictions=_UpperCamelCase		,				references=eval_pred.label_ids						)

 SCREAMING_SNAKE_CASE		    =      AutoConfig.from_pretrained(
     model_args.config_name or model_args.model_name_or_path		,				num_labels=len(_UpperCamelCase						)		,				labelaid=_UpperCamelCase		,				idalabel=_UpperCamelCase		,				finetuning_task='audio-classification'		,				cache_dir=model_args.cache_dir		,				revision=model_args.model_revision		,				use_auth_token=True if model_args.use_auth_token else None		,				)
 SCREAMING_SNAKE_CASE		    =      AutoModelForAudioClassification.from_pretrained(
     model_args.model_name_or_path		,				from_tf=bool('.ckpt' in model_args.model_name_or_path						)		,				config=_UpperCamelCase		,				cache_dir=model_args.cache_dir		,				revision=model_args.model_revision		,				use_auth_token=True if model_args.use_auth_token else None		,				ignore_mismatched_sizes=model_args.ignore_mismatched_sizes		,				)

 # freeze the convolutional waveform encoder
 if model_args.freeze_feature_encoder:
  model.freeze_feature_encoder()

 if training_args.do_train:
  if data_args.max_train_samples is not None:
   SCREAMING_SNAKE_CASE		    =      (
       raw_datasets['train'].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
   )
  # Set the training transforms
  raw_datasets["train"].set_transform(_UpperCamelCase		,				output_all_columns=_UpperCamelCase						)

 if training_args.do_eval:
  if data_args.max_eval_samples is not None:
   SCREAMING_SNAKE_CASE		    =      (
       raw_datasets['eval'].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
   )
  # Set the validation transforms
  raw_datasets["eval"].set_transform(_UpperCamelCase		,				output_all_columns=_UpperCamelCase						)

 # Initialize our trainer
 SCREAMING_SNAKE_CASE		    =      Trainer(
     model=_UpperCamelCase		,				args=_UpperCamelCase		,				train_dataset=raw_datasets['train'] if training_args.do_train else None		,				eval_dataset=raw_datasets['eval'] if training_args.do_eval else None		,				compute_metrics=_UpperCamelCase		,				tokenizer=_UpperCamelCase		,				)

 # Training
 if training_args.do_train:
  SCREAMING_SNAKE_CASE		    =      None
  if training_args.resume_from_checkpoint is not None:
   SCREAMING_SNAKE_CASE		    =      training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   SCREAMING_SNAKE_CASE		    =      last_checkpoint
  SCREAMING_SNAKE_CASE		    =      trainer.train(resume_from_checkpoint=_UpperCamelCase						)
  trainer.save_model()
  trainer.log_metrics('train'		,				train_result.metrics						)
  trainer.save_metrics('train'		,				train_result.metrics						)
  trainer.save_state()

 # Evaluation
 if training_args.do_eval:
  SCREAMING_SNAKE_CASE		    =      trainer.evaluate()
  trainer.log_metrics('eval'		,				_UpperCamelCase						)
  trainer.save_metrics('eval'		,				_UpperCamelCase						)

 # Write model card and (optionally) push to hub
 SCREAMING_SNAKE_CASE		    =      {
     'finetuned_from': model_args.model_name_or_path,
     'tasks': 'audio-classification',
     'dataset': data_args.dataset_name,
     'tags': ['audio-classification'],
 }
 if training_args.push_to_hub:
  trainer.push_to_hub(**_UpperCamelCase						)
 else:
  trainer.create_model_card(**_UpperCamelCase						)


if __name__ == "__main__":
      main()




from __future__ import annotations

from math import gcd


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	int = 2		,				_UpperCamelCase		:	int = 1		,				_UpperCamelCase		:	int = 3		,				)		->				int | None:
 '''simple docstring'''


 if num < 2:
  raise ValueError('The input value cannot be less than 2'						)

 # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
 # algorithm struggles to find factors that are divisible by two.
 # As a workaround, we specifically check for two and even inputs.
 #   See: https://math.stackexchange.com/a/2856214/165820
 if num > 2 and num % 2 == 0:
  return 2

 # Pollard's Rho algorithm requires a function that returns pseudorandom
 # values between 0 <= X < ``num``.  It doesn't need to be random in the
 # sense that the output value is cryptographically secure or difficult
 # to calculate, it only needs to be random in the sense that all output
 # values should be equally likely to appear.
 # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
 # However, the success of Pollard's algorithm isn't guaranteed and is
 # determined in part by the initial seed and the chosen random function.
 # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
 # where ``C`` is a value that we can modify between each attempt.
 def rand_fn(_UpperCamelCase		:	int		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	int						) -> int:
  return (pow(_UpperCamelCase		,				2						) + step) % modulus

 for _ in range(_UpperCamelCase						):
  # These track the position within the cycle detection logic.
  SCREAMING_SNAKE_CASE		    =      seed
  SCREAMING_SNAKE_CASE		    =      seed

  while True:
   # At each iteration, the tortoise moves one step and the hare moves two.
   SCREAMING_SNAKE_CASE		    =      rand_fn(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      rand_fn(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      rand_fn(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)

   # At some point both the tortoise and the hare will enter a cycle whose
   # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
   # the tortoise and hare will end up on the same value modulo ``p``.
   # We can detect when this happens because the position difference between
   # the tortoise and the hare will share a common divisor with ``num``.
   SCREAMING_SNAKE_CASE		    =      gcd(hare - tortoise		,				_UpperCamelCase						)

   if divisor == 1:
    # No common divisor yet, just keep searching.
    continue
   else:
    # We found a common divisor!
    if divisor == num:
     # Unfortunately, the divisor is ``num`` itself and is useless.
     break
    else:
     # The divisor is a nontrivial factor of ``num``!
     return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
  SCREAMING_SNAKE_CASE		    =      hare

  # The new step value for the random function can just be incremented.
  # At first the results will be similar to what the old function would
  # have produced, but the value will quickly diverge after a bit.
  step += 1

 # We haven't found a divisor within the requested number of attempts.
 # We were unlucky or ``num`` itself is actually prime.
 return None


if __name__ == "__main__":
      import argparse

      a_     :    int     		=   argparse.ArgumentParser()
      parser.add_argument(
          "num",
          type=int,
          help="The value to find a divisor of",
      )
      parser.add_argument(
          "--attempts",
          type=int,
          default=3,
          help="The number of attempts before giving up",
      )
      a_     :    Tuple     		=   parser.parse_args()

      a_     :    int     		=   pollard_rho(args.num, attempts=args.attempts)
      if divisor is None:
            print(F"""{args.num} is probably prime""")
      else:
            a_     :    Union[str, Any]     		=   args.num // divisor
            print(F"""{args.num} = {divisor} * {quotient}""")

