


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
snake_case__			 =						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]


def 				lowerCamelCase__	(      a	:   Optional[int]				)    ->	Optional[int]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    {}
			with open(__lowerCAmelCase	,  "r"				) as file:
						for line_number, line in enumerate(__lowerCAmelCase				):
									a__ :Tuple						  =    line.strip()
									if line:
												a__ :int						  =    line.split()
												a__ :Union[str, Any]						  =    line_number
												a__ :Tuple						  =    words[0]
												a__ :Tuple						  =    value
			return result


def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   Tuple	,  a	:   str	,  a	:   Dict	,  a	:   List[str]				)    ->	Union[str, Any]:





			"""simple docstring"""


			for attribute in key.split("."				):
						a__ :int						  =    getattr(__lowerCAmelCase	,  __lowerCAmelCase				)

			a__ :Union[str, Any]						  =    None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(__lowerCAmelCase				):
									a__ :Any						  =    PARAM_MAPPING[full_name.split("."				)[-1]]
									a__ :Optional[Any]						  =    "param"

			if weight_type is not None and weight_type != "param":
						a__ :List[str]						  =    getattr(__lowerCAmelCase	,  __lowerCAmelCase				).shape
			elif weight_type is not None and weight_type == "param":
						a__ :Optional[Any]						  =    hf_pointer
						for attribute in hf_param_name.split("."				):
									a__ :str						  =    getattr(__lowerCAmelCase	,  __lowerCAmelCase				)
						a__ :int						  =    shape_pointer.shape

						# let's reduce dimension
						a__ :int						  =    value[0]
			else:
						a__ :int						  =    hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
						    F''' {value.shape} for {full_name}'''				)

			if weight_type == "weight":
						a__ :Tuple						  =    value
			elif weight_type == "weight_g":
						a__ :Optional[int]						  =    value
			elif weight_type == "weight_v":
						a__ :int						  =    value
			elif weight_type == "bias":
						a__ :Union[str, Any]						  =    value
			elif weight_type == "param":
						for attribute in hf_param_name.split("."				):
									a__ :Optional[int]						  =    getattr(__lowerCAmelCase	,  __lowerCAmelCase				)
						a__ :List[Any]						  =    value
			else:
						a__ :List[Any]						  =    value

			logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''				)


def 				lowerCamelCase__	(      a	:   int	,  a	:   Optional[Any]	,  a	:   str	,  a	:   List[Any]	,  a	:   Union[str, Any]				)    ->	Any:





			"""simple docstring"""
			a__ :Tuple						  =    None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(__lowerCAmelCase				):
									a__ :Optional[int]						  =    PARAM_MAPPING[full_name.split("."				)[-1]]
									a__ :List[str]						  =    "param"

			if weight_type is not None and weight_type != "param":
						a__ :int						  =    ".".join([key, weight_type]				)
			elif weight_type is not None and weight_type == "param":
						a__ :Optional[int]						  =    ".".join([key, hf_param_name]				)
			else:
						a__ :Tuple						  =    key

			a__ :str						  =    value if "lm_head" in full_key else value[0]


snake_case__			 =						{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}


def 				lowerCamelCase__	(      a	:   List[str]	,  a	:   Union[str, Any]	,  a	:   List[Any]=None	,  a	:   Optional[int]=None				)    ->	Optional[Any]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    False
			for key, mapped_key in MAPPING.items():
						a__ :Union[str, Any]						  =    "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
						if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
									a__ :Union[str, Any]						  =    True
									if "*" in mapped_key:
												a__ :Optional[Any]						  =    name.split(__lowerCAmelCase				)[0].split("."				)[-2]
												a__ :Any						  =    mapped_key.replace("*"	,  __lowerCAmelCase				)
									if "weight_g" in name:
												a__ :List[Any]						  =    "weight_g"
									elif "weight_v" in name:
												a__ :Optional[int]						  =    "weight_v"
									elif "bias" in name:
												a__ :Dict						  =    "bias"
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												a__ :Any						  =    "weight"
									else:
												a__ :List[str]						  =    None
									if hf_dict is not None:
												rename_dict(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase				)
									else:
												set_recursively(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase				)
									return is_used
			return is_used


def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   Any	,  a	:   Any				)    ->	int:





			"""simple docstring"""
			a__ :Any						  =    []
			a__ :Tuple						  =    fairseq_model.state_dict()

			a__ :int						  =    hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						a__ :Dict						  =    False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  hf_model.config.feat_extract_norm == "group"	,  )
									a__ :Tuple						  =    True
						else:
									a__ :Optional[Any]						  =    load_wavaveca_layer(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase				)
						if not is_used:
									unused_weights.append(__lowerCAmelCase				)

			logger.warning(F'''Unused weights: {unused_weights}'''				)


def 				lowerCamelCase__	(      a	:   Optional[int]	,  a	:   Optional[int]	,  a	:   int	,  a	:   Optional[Any]	,  a	:   Optional[Any]				)    ->	Dict:





			"""simple docstring"""
			a__ :List[Any]						  =    full_name.split("conv_layers."				)[-1]
			a__ :List[str]						  =    name.split("."				)
			a__ :List[Any]						  =    int(items[0]				)
			a__ :Tuple						  =    int(items[1]				)

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''				)
									a__ :Optional[int]						  =    value
									logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''				)
									a__ :Any						  =    value
									logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''				)
									a__ :List[str]						  =    value
									logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    F'''{full_name} has size {value.shape}, but'''
												    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''				)
									a__ :Dict						  =    value
									logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
			else:
						unused_weights.append(__lowerCAmelCase				)


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   int	,  a	:   Optional[Any]	,  a	:   List[str]=None	,  a	:   Tuple=None	,  a	:   List[str]=True	,  a	:   Optional[int]=False				)    ->	Dict:





			"""simple docstring"""


			if config_path is not None:
						a__ :str						  =    WavaVecaConfig.from_pretrained(__lowerCAmelCase				)
			else:
						a__ :int						  =    WavaVecaConfig()

			if is_seq_class:
						a__ :Dict						  =    read_txt_into_dict(__lowerCAmelCase				)
						a__ :Dict						  =    idalabel
						a__ :int						  =    WavaVecaForSequenceClassification(__lowerCAmelCase				)
						a__ :Tuple						  =    WavaVecaFeatureExtractor(
						    feature_size=1	,  sampling_rate=16_000	,  padding_value=0	,  do_normalize=__lowerCAmelCase	,  return_attention_mask=__lowerCAmelCase	,  )
						feature_extractor.save_pretrained(__lowerCAmelCase				)

			elif is_finetuned:
						if dict_path:
									a__ :List[str]						  =    Dictionary.load(__lowerCAmelCase				)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									a__ :Optional[int]						  =    target_dict.pad_index
									a__ :Optional[Any]						  =    target_dict.bos_index
									a__ :Union[str, Any]						  =    target_dict.eos_index
									a__ :Optional[Any]						  =    len(target_dict.symbols				)
									a__ :int						  =    os.path.join(__lowerCAmelCase	,  "vocab.json"				)
									if not os.path.isdir(__lowerCAmelCase				):
												logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__lowerCAmelCase				)				)
												return
									os.makedirs(__lowerCAmelCase	,  exist_ok=__lowerCAmelCase				)
									a__ :Optional[Any]						  =    target_dict.indices

									# fairseq has the <pad> and <s> switched
									a__ :Tuple						  =    0
									a__ :Tuple						  =    1
									with open(__lowerCAmelCase	,  "w"	,  encoding="utf-8"				) as vocab_handle:
												json.dump(__lowerCAmelCase	,  __lowerCAmelCase				)
									a__ :Union[str, Any]						  =    WavaVecaCTCTokenizer(
									    __lowerCAmelCase	,  unk_token=target_dict.unk_word	,  pad_token=target_dict.pad_word	,  bos_token=target_dict.bos_word	,  eos_token=target_dict.eos_word	,  word_delimiter_token="|"	,  do_lower_case=__lowerCAmelCase	,  )
									a__ :int						  =    True if config.feat_extract_norm == "layer" else False
									a__ :Union[str, Any]						  =    WavaVecaFeatureExtractor(
									    feature_size=1	,  sampling_rate=16_000	,  padding_value=0	,  do_normalize=__lowerCAmelCase	,  return_attention_mask=__lowerCAmelCase	,  )
									a__ :int						  =    WavaVecaProcessor(feature_extractor=__lowerCAmelCase	,  tokenizer=__lowerCAmelCase				)
									processor.save_pretrained(__lowerCAmelCase				)

						a__ :Any						  =    WavaVecaForCTC(__lowerCAmelCase				)
			else:
						a__ :Dict						  =    WavaVecaForPreTraining(__lowerCAmelCase				)

			if is_finetuned or is_seq_class:
						a__  ,  a__  ,  a__ :List[Any]						  =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]	,  arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
			else:
						a__ :Tuple						  =    argparse.Namespace(task="audio_pretraining"				)
						a__ :Optional[int]						  =    fairseq.tasks.setup_task(__lowerCAmelCase				)

						a__  ,  a__  ,  a__ :List[Any]						  =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	,  task=__lowerCAmelCase				)

			a__ :Optional[int]						  =    model[0].eval()

			recursively_load_weights(__lowerCAmelCase	,  __lowerCAmelCase	,  not is_finetuned				)

			hf_wavavec.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
		snake_case__			 =						argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
		)
		parser.add_argument(
		    '''--is_seq_class''',
		    action='''store_true''',
		    help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
		)
		snake_case__			 =						parser.parse_args()

		snake_case__			 =						not args.not_finetuned and not args.is_seq_class
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.dict_path,
		    is_finetuned,
		    args.is_seq_class,
		)






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


snake_case__			 =						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__	(      a	:   np.ndarray	,  a	:   float	,  a	:   int = 16_000				)    ->	List[str]:





			"""simple docstring"""
			a__ :Optional[int]						  =    int(round(sample_rate * max_length				)				)
			if len(a				) <= sample_length:
						return wav
			a__ :Optional[Any]						  =    randint(0	,  len(a				) - sample_length - 1				)
			return wav[random_offset : random_offset + sample_length]


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






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




						def  _snake_case					(	self						:   Optional[Any]						)      ->Dict:






									"""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`."   ,     __A   ,     )
									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__	(      )    ->	Optional[Any]:





			"""simple docstring"""
			# 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__ :Dict						  =    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__ :int						  =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
			else:
						a__  ,  a__  ,  a__ :Union[str, Any]						  =    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"	,  a	,  a				)

			# 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__ :Union[str, Any]						  =    training_args.get_process_log_level()
			logger.setLevel(a				)
			transformers.utils.logging.set_verbosity(a				)
			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__ :List[str]						  =    None
			if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
						a__ :List[Any]						  =    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__ :List[str]						  =    DatasetDict()
			a__ :int						  =    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__ :List[str]						  =    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__ :str						  =    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__ :List[Any]						  =    raw_datasets.cast_column(
			    data_args.audio_column_name	,  datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate				)				)

			a__ :Optional[Any]						  =    feature_extractor.model_input_names[0]

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

						return output_batch

			def val_transforms(a	:   Optional[int]				):
						a__ :Any						  =    [audio["array"] for audio in batch[data_args.audio_column_name]]
						a__ :int						  =    feature_extractor(a	,  sampling_rate=feature_extractor.sampling_rate				)
						a__ :int						  =    {model_input_name: inputs.get(a				)}
						a__ :Dict						  =    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__ :Union[str, Any]						  =    raw_datasets["train"].features[data_args.label_column_name].names
			a__  ,  a__ :str						  =    {}, {}
			for i, label in enumerate(a				):
						a__ :Tuple						  =    str(a				)
						a__ :List[str]						  =    label

			# Load the accuracy metric from the datasets package
			a__ :Optional[int]						  =    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(a	:   List[Any]				):
						a__ :str						  =    np.argmax(eval_pred.predictions	,  axis=1				)
						return metric.compute(predictions=a	,  references=eval_pred.label_ids				)

			a__ :Dict						  =    AutoConfig.from_pretrained(
			    model_args.config_name or model_args.model_name_or_path	,  num_labels=len(a				)	,  labelaid=a	,  idalabel=a	,  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__ :Any						  =    AutoModelForAudioClassification.from_pretrained(
			    model_args.model_name_or_path	,  from_tf=bool(".ckpt" in model_args.model_name_or_path				)	,  config=a	,  cache_dir=model_args.cache_dir	,  revision=model_args.model_revision	,  use_auth_token=True if model_args.use_auth_token else None	,  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__ :Tuple						  =    (
									    raw_datasets["train"].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
									)
						# Set the training transforms
						raw_datasets["train"].set_transform(a	,  output_all_columns=a				)

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

			# Initialize our trainer
			a__ :List[str]						  =    Trainer(
			    model=a	,  args=a	,  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=a	,  tokenizer=a	,  )

			# Training
			if training_args.do_train:
						a__ :Tuple						  =    None
						if training_args.resume_from_checkpoint is not None:
									a__ :List[Any]						  =    training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									a__ :Any						  =    last_checkpoint
						a__ :Any						  =    trainer.train(resume_from_checkpoint=a				)
						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__ :int						  =    trainer.evaluate()
						trainer.log_metrics("eval"	,  a				)
						trainer.save_metrics("eval"	,  a				)

			# Write model card and (optionally) push to hub
			a__ :str						  =    {
			    "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(**a				)
			else:
						trainer.create_model_card(**a				)


if __name__ == "__main__":
		main()


