



"""simple docstring"""







from __future__ import annotations






class      UpperCAmelCase__		:



		def __init__( self  :    Any      ,							snake_case  :    list[list[int]]				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					TypeError(
									    'Matrices must be formed from a list of zero or more lists containing at '
									    'least one and the same number of values, each of which must be of type '
									    'int or float.'				)
									if len(snake_case				) != 0:
																A         =					len(rows[0]				)
																if cols == 0:
																							raise error
																for row in rows:
																							if len(snake_case				) != cols:
																														raise error
																							for value in row:
																														if not isinstance(snake_case      ,							(int, float)				):
																																					raise error
																A         =					rows
									else:
																A         =					[]



		def 					A_		( self  :    Any				)			->						list[list[int]]:






									'''simple docstring'''

									return [[row[i] for row in self.rows] for i in range(len(self.rows[0]				)				)]



		@property
		def 					A_		( self  :    List[Any]				)			->						int:






									'''simple docstring'''

									return len(self.rows				)



		@property
		def 					A_		( self  :    int				)			->						int:






									'''simple docstring'''

									return len(self.rows[0]				)



		@property
		def 					A_		( self  :    int				)			->						tuple[int, int]:






									'''simple docstring'''

									return (self.num_rows, self.num_columns)



		@property
		def 					A_		( self  :    Tuple				)			->						bool:






									'''simple docstring'''

									return self.order[0] == self.order[1]



		def 					A_		( self  :    Tuple				)			->						Matrix:






									'''simple docstring'''

									A         =					[
									    [0 if column_num != row_num else 1 for column_num in range(self.num_rows				)]
									    for row_num in range(self.num_rows				)
									]
									return Matrix(snake_case				)



		def 					A_		( self  :    Union[str, Any]				)			->						int:






									'''simple docstring'''

									if not self.is_square:
																return 0
									if self.order == (0, 0):
																return 1
									if self.order == (1, 1):
																return int(self.rows[0][0]				)
									if self.order == (2, 2):
																return int(
																    (self.rows[0][0] * self.rows[1][1])
																    - (self.rows[0][1] * self.rows[1][0])				)
									else:
																return sum(
																    self.rows[0][column] * self.cofactors().rows[0][column]
																    for column in range(self.num_columns				)				)



		def 					A_		( self  :    Dict				)			->						bool:






									'''simple docstring'''

									return bool(self.determinant()				)



		def 					A_		( self  :    Any      ,							snake_case  :    int      ,							snake_case  :    int				)			->						int:






									'''simple docstring'''

									A         =					[
									    [
									        self.rows[other_row][other_column]
									        for other_column in range(self.num_columns				)
									        if other_column != column
									    ]
									    for other_row in range(self.num_rows				)
									    if other_row != row
									]
									return Matrix(snake_case				).determinant()



		def 					A_		( self  :    Tuple      ,							snake_case  :    int      ,							snake_case  :    int				)			->						int:






									'''simple docstring'''

									if (row + column) % 2 == 0:
																return self.get_minor(snake_case      ,							snake_case				)
									return -1 * self.get_minor(snake_case      ,							snake_case				)



		def 					A_		( self  :    List[str]				)			->						Matrix:






									'''simple docstring'''

									return Matrix(
									    [
									        [self.get_minor(snake_case      ,							snake_case				) for column in range(self.num_columns				)]
									        for row in range(self.num_rows				)
									    ]				)



		def 					A_		( self  :    Union[str, Any]				)			->						Matrix:






									'''simple docstring'''

									return Matrix(
									    [
									        [
									            self.minors().rows[row][column]
									            if (row + column) % 2 == 0
									            else self.minors().rows[row][column] * -1
									            for column in range(self.minors().num_columns				)
									        ]
									        for row in range(self.minors().num_rows				)
									    ]				)



		def 					A_		( self  :    Union[str, Any]				)			->						Matrix:






									'''simple docstring'''

									A         =					[
									    [self.cofactors().rows[column][row] for column in range(self.num_columns				)]
									    for row in range(self.num_rows				)
									]
									return Matrix(snake_case				)



		def 					A_		( self  :    Tuple				)			->						Matrix:






									'''simple docstring'''

									A         =					self.determinant()
									if not determinant:
																raise TypeError('Only matrices with a non-zero determinant have an inverse'				)
									return self.adjugate() * (1 / determinant)



		def __repr__( self  :    Optional[Any]				)			->						str:






									'''simple docstring'''

									return str(self.rows				)



		def __str__( self  :    List[str]				)			->						str:






									'''simple docstring'''

									if self.num_rows == 0:
																return "[]"
									if self.num_rows == 1:
																return "[[" + ". ".join(str(self.rows[0]				)				) + "]]"
									return (
									    "["
									    + "\n ".join(
									        [
									            '[' + '. '.join([str(snake_case				) for value in row]				) + '.]'
									            for row in self.rows
									        ]				)
									    + "]"
									)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    list[int]      ,							snake_case  :    int | None = None				)			->						None:






									'''simple docstring'''

									A         =					TypeError('Row must be a list containing all ints and/or floats'				)
									if not isinstance(snake_case      ,							snake_case				):
																raise type_error
									for value in row:
																if not isinstance(snake_case      ,							(int, float)				):
																							raise type_error
									if len(snake_case				) != self.num_columns:
																raise ValueError(
																    'Row must be equal in length to the other rows in the matrix'				)
									if position is None:
																self.rows.append(snake_case				)
									else:
																A         =					self.rows[0:position] + [row] + self.rows[position:]



		def 					A_		( self  :    Tuple      ,							snake_case  :    list[int]      ,							snake_case  :    int | None = None				)			->						None:






									'''simple docstring'''

									A         =					TypeError(
									    'Column must be a list containing all ints and/or floats'				)
									if not isinstance(snake_case      ,							snake_case				):
																raise type_error
									for value in column:
																if not isinstance(snake_case      ,							(int, float)				):
																							raise type_error
									if len(snake_case				) != self.num_rows:
																raise ValueError(
																    'Column must be equal in length to the other columns in the matrix'				)
									if position is None:
																A         =					[self.rows[i] + [column[i]] for i in range(self.num_rows				)]
									else:
																A         =					[
																    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
																    for i in range(self.num_rows				)
																]



		def __eq__( self  :    str      ,							snake_case  :    object				)			->						bool:






									'''simple docstring'''

									if not isinstance(snake_case      ,							snake_case				):
																return NotImplemented
									return self.rows == other.rows



		def __ne__( self  :    int      ,							snake_case  :    object				)			->						bool:






									'''simple docstring'''

									return not self == other



		def __neg__( self  :    List[Any]				)			->						Matrix:






									'''simple docstring'''

									return self * -1



		def __add__( self  :    Tuple      ,							snake_case  :    Matrix				)			->						Matrix:






									'''simple docstring'''

									if self.order != other.order:
																raise ValueError('Addition requires matrices of the same order'				)
									return Matrix(
									    [
									        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns				)]
									        for i in range(self.num_rows				)
									    ]				)



		def __sub__( self  :    Tuple      ,							snake_case  :    Matrix				)			->						Matrix:






									'''simple docstring'''

									if self.order != other.order:
																raise ValueError('Subtraction requires matrices of the same order'				)
									return Matrix(
									    [
									        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns				)]
									        for i in range(self.num_rows				)
									    ]				)



		def __mul__( self  :    Tuple      ,							snake_case  :    Matrix | int | float				)			->						Matrix:






									'''simple docstring'''

									if isinstance(snake_case      ,							(int, float)				):
																return Matrix(
																    [[int(element * other				) for element in row] for row in self.rows]				)
									elif isinstance(snake_case      ,							snake_case				):
																if self.num_columns != other.num_rows:
																							raise ValueError(
																							    'The number of columns in the first matrix must '
																							    'be equal to the number of rows in the second'				)
																return Matrix(
																    [
																        [Matrix.dot_product(snake_case      ,							snake_case				) for column in other.columns()]
																        for row in self.rows
																    ]				)
									else:
																raise TypeError(
																    'A Matrix can only be multiplied by an int, float, or another matrix'				)



		def __pow__( self  :    Optional[Any]      ,							snake_case  :    int				)			->						Matrix:






									'''simple docstring'''

									if not isinstance(snake_case      ,							snake_case				):
																raise TypeError('A Matrix can only be raised to the power of an int'				)
									if not self.is_square:
																raise ValueError('Only square matrices can be raised to a power'				)
									if other == 0:
																return self.identity()
									if other < 0:
																if self.is_invertable():
																							return self.inverse() ** (-other)
																raise ValueError(
																    'Only invertable matrices can be raised to a negative power'				)
									A         =					self
									for _ in range(other - 1				):
																result *= self
									return result



		@classmethod
		def 					A_		( cls  :    Optional[int]      ,							snake_case  :    list[int]      ,							snake_case  :    list[int]				)			->						int:






									'''simple docstring'''

									return sum(row[i] * column[i] for i in range(len(snake_case				)				)				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()








"""simple docstring"""







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    =			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__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = 16000			) ->							Union[str, Any]:
							A         =					int(round(sample_rate * max_length			)			)
							if len(lowerCamelCase__			) <= sample_length:
														return wav
							A         =					randint(0				,      len(lowerCamelCase__			) - sample_length - 1			)
							return wav[random_offset : random_offset + sample_length]






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






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



		def 					A_		( self  :    List[str]				)			->						str:






									'''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__			(     ) ->							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.

							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()

							# 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'				,      lowerCamelCase__				,      lowerCamelCase__			)

							# 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()

							A         =					training_args.get_process_log_level()
							logger.setLevel(lowerCamelCase__			)
							transformers.utils.logging.set_verbosity(lowerCamelCase__			)
							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.
							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 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.
							A         =					DatasetDict()
							A         =					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				,      )
							A         =					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
							A         =					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.
							A         =					raw_datasets.cast_column(
							    data_args.audio_column_name				,      datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate			)			)

							A         =					feature_extractor.model_input_names[0]

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

														return output_batch

							def val_transforms(lowerCamelCase__			):
														A         =					[audio['array'] for audio in batch[data_args.audio_column_name]]
														A         =					feature_extractor(lowerCamelCase__				,      sampling_rate=feature_extractor.sampling_rate			)
														A         =					{model_input_name: inputs.get(lowerCamelCase__			)}
														A         =					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.
							A         =					raw_datasets['train'].features[data_args.label_column_name].names
							A      ,  A         =					{}, {}
							for i, label in enumerate(lowerCamelCase__			):
														A         =					str(lowerCamelCase__			)
														A         =					label

							# Load the accuracy metric from the datasets package
							A         =					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(lowerCamelCase__			):
														A         =					np.argmax(eval_pred.predictions				,      axis=1			)
														return metric.compute(predictions=lowerCamelCase__				,      references=eval_pred.label_ids			)

							A         =					AutoConfig.from_pretrained(
							    model_args.config_name or model_args.model_name_or_path				,      num_labels=len(lowerCamelCase__			)				,      labelaid=lowerCamelCase__				,      idalabel=lowerCamelCase__				,      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				,      )
							A         =					AutoModelForAudioClassification.from_pretrained(
							    model_args.model_name_or_path				,      from_tf=bool('.ckpt' in model_args.model_name_or_path			)				,      config=lowerCamelCase__				,      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:
																					A         =					(
																					    raw_datasets['train'].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
																					)
														# Set the training transforms
														raw_datasets["train"].set_transform(lowerCamelCase__				,      output_all_columns=lowerCamelCase__			)

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

							# Initialize our trainer
							A         =					Trainer(
							    model=lowerCamelCase__				,      args=lowerCamelCase__				,      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=lowerCamelCase__				,      tokenizer=lowerCamelCase__				,      )

							# Training
							if training_args.do_train:
														A         =					None
														if training_args.resume_from_checkpoint is not None:
																					A         =					training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					A         =					last_checkpoint
														A         =					trainer.train(resume_from_checkpoint=lowerCamelCase__			)
														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:
														A         =					trainer.evaluate()
														trainer.log_metrics('eval'				,      lowerCamelCase__			)
														trainer.save_metrics('eval'				,      lowerCamelCase__			)

							# Write model card and (optionally) push to hub
							A         =					{
							    '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(**lowerCamelCase__			)
							else:
														trainer.create_model_card(**lowerCamelCase__			)


if __name__ == "__main__":
							main()



