


"""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


UpperCamelCase__				:int			   =	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 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__ = 1_60_00		)					->  int:
			_UpperCamelCase					:Optional[Any]					     =	int(round(sample_rate * max_length		)		)
			if len(snake_case__		) <= sample_length:
						return wav
			_UpperCamelCase					:Optional[int]					     =	randint(0       ,					len(snake_case__		) - sample_length - 1		)
			return wav[random_offset : random_offset + sample_length]






@dataclass
class      A:

		"""simple docstring"""





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






@dataclass
class      A:

		"""simple docstring"""





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

					"""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`.'''					,				SCREAMING_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 				A_       ( )					->  Union[str, Any]:
			# 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.

			_UpperCamelCase					:Optional[int]					     =	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.
						_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:int					     =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
			else:
						_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:int					     =	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'''       ,					snake_case__       ,					snake_case__		)

			# 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()

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

			_UpperCamelCase					:Dict					     =	feature_extractor.model_input_names[0]

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

						return output_batch

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

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

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

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

			# Initialize our trainer
			_UpperCamelCase					:List[Any]					     =	Trainer(
			    model=snake_case__       ,					args=snake_case__       ,					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=snake_case__       ,					tokenizer=snake_case__       ,					)

			# Training
			if training_args.do_train:
						_UpperCamelCase					:List[str]					     =	None
						if training_args.resume_from_checkpoint is not None:
									_UpperCamelCase					:List[str]					     =	training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									_UpperCamelCase					:Tuple					     =	last_checkpoint
						_UpperCamelCase					:Tuple					     =	trainer.train(resume_from_checkpoint=snake_case__		)
						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:
						_UpperCamelCase					:Dict					     =	trainer.evaluate()
						trainer.log_metrics('''eval'''       ,					snake_case__		)
						trainer.save_metrics('''eval'''       ,					snake_case__		)

			# Write model card and (optionally) push to hub
			_UpperCamelCase					:Optional[Any]					     =	{
			    '''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(**snake_case__		)
			else:
						trainer.create_model_card(**snake_case__		)


if __name__ == "__main__":
					main()





"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__				:Optional[int]			   =	logging.get_logger(__name__)

UpperCamelCase__				:int			   =	{
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			for attribute in key.split('''.'''		):
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		)

			if weight_type is not None:
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		).shape
			else:
						_UpperCamelCase					:Optional[int]					     =	hf_pointer.shape

			assert hf_shape == value.shape, (
			    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":
						_UpperCamelCase					:str					     =	value
			elif weight_type == "weight_g":
						_UpperCamelCase					:Dict					     =	value
			elif weight_type == "weight_v":
						_UpperCamelCase					:Optional[Any]					     =	value
			elif weight_type == "bias":
						_UpperCamelCase					:str					     =	value
			else:
						_UpperCamelCase					:int					     =	value

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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			_UpperCamelCase					:Optional[int]					     =	[]
			_UpperCamelCase					:List[str]					     =	fairseq_model.state_dict()

			_UpperCamelCase					:str					     =	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCamelCase					:Optional[Any]					     =	False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					hf_model.config.feat_extract_norm == '''group'''       ,					)
									_UpperCamelCase					:Dict					     =	True
						else:
									for key, mapped_key in MAPPING.items():
												_UpperCamelCase					:Optional[int]					     =	'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

												if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
															_UpperCamelCase					:List[str]					     =	True
															if "*" in mapped_key:
																		_UpperCamelCase					:List[str]					     =	name.split(snake_case__		)[0].split('''.'''		)[-2]
																		_UpperCamelCase					:Tuple					     =	mapped_key.replace('''*'''       ,					snake_case__		)
															if "weight_g" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight_g'''
															elif "weight_v" in name:
																		_UpperCamelCase					:Union[str, Any]					     =	'''weight_v'''
															elif "weight" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight'''
															elif "bias" in name:
																		_UpperCamelCase					:List[Any]					     =	'''bias'''
															else:
																		_UpperCamelCase					:List[Any]					     =	None
															set_recursively(snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)
												continue
						if not is_used:
									unused_weights.append(snake_case__		)

			logger.warning(f"Unused weights: {unused_weights}"		)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[int]					     =	full_name.split('''conv_layers.'''		)[-1]
			_UpperCamelCase					:Optional[int]					     =	name.split('''.'''		)
			_UpperCamelCase					:Optional[Any]					     =	int(items[0]		)
			_UpperCamelCase					:List[str]					     =	int(items[1]		)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    f"{full_name} has size {value.shape}, but"
									    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
									)
									_UpperCamelCase					:Optional[int]					     =	value
									logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    f"{full_name} has size {value.shape}, but"
									    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
									)
									_UpperCamelCase					:Optional[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:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
									    " found."
									)
									_UpperCamelCase					:int					     =	value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    f"{full_name} has size {value.shape}, but"
									    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
									)
									_UpperCamelCase					:Dict					     =	value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
			else:
						unused_weights.append(snake_case__		)





def 				A_       ( snake_case__       ,					snake_case__		)					->  List[str]:
			_UpperCamelCase					:str					     =	SEWConfig()
			if is_finetuned:
						_UpperCamelCase					:Optional[int]					     =	model.wav_encoder.wav_model.cfg
			else:
						_UpperCamelCase					:Dict					     =	model.cfg

			_UpperCamelCase					:Dict					     =	fs_config.conv_bias
			_UpperCamelCase					:int					     =	eval(fs_config.conv_feature_layers		)
			_UpperCamelCase					:List[Any]					     =	[x[0] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[1] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[2] for x in conv_layers]
			_UpperCamelCase					:str					     =	'''gelu'''
			_UpperCamelCase					:Optional[int]					     =	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
			_UpperCamelCase					:List[Any]					     =	0.0
			_UpperCamelCase					:Optional[int]					     =	fs_config.activation_fn.name
			_UpperCamelCase					:str					     =	fs_config.encoder_embed_dim
			_UpperCamelCase					:Dict					     =	0.02
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_ffn_embed_dim
			_UpperCamelCase					:str					     =	1E-5
			_UpperCamelCase					:int					     =	fs_config.encoder_layerdrop
			_UpperCamelCase					:Union[str, Any]					     =	fs_config.encoder_attention_heads
			_UpperCamelCase					:List[str]					     =	fs_config.conv_pos_groups
			_UpperCamelCase					:List[Any]					     =	fs_config.conv_pos
			_UpperCamelCase					:List[str]					     =	len(snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_layers
			_UpperCamelCase					:Optional[int]					     =	fs_config.squeeze_factor

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						_UpperCamelCase					:List[Any]					     =	model.cfg
						_UpperCamelCase					:List[Any]					     =	fs_config.final_dropout
						_UpperCamelCase					:Dict					     =	fs_config.layerdrop
			_UpperCamelCase					:Any					     =	fs_config.activation_dropout
			_UpperCamelCase					:List[str]					     =	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			_UpperCamelCase					:Optional[Any]					     =	fs_config.attention_dropout
			_UpperCamelCase					:List[Any]					     =	fs_config.dropout_input
			_UpperCamelCase					:Dict					     =	fs_config.dropout
			_UpperCamelCase					:int					     =	fs_config.mask_channel_length
			_UpperCamelCase					:Tuple					     =	fs_config.mask_channel_prob
			_UpperCamelCase					:int					     =	fs_config.mask_length
			_UpperCamelCase					:Dict					     =	fs_config.mask_prob

			_UpperCamelCase					:List[Any]					     =	'''Wav2Vec2FeatureExtractor'''
			_UpperCamelCase					:Optional[Any]					     =	'''Wav2Vec2CTCTokenizer'''

			return config





@torch.no_grad()
def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__=None       ,					snake_case__=None       ,					snake_case__=True		)					->  int:

			if is_finetuned:
						_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:List[Any]					     =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]       ,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		)}		)
			else:
						_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:str					     =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

			if config_path is not None:
						_UpperCamelCase					:Any					     =	SEWConfig.from_pretrained(snake_case__		)
			else:
						_UpperCamelCase					:List[str]					     =	convert_config(model[0]       ,					snake_case__		)
			_UpperCamelCase					:List[str]					     =	model[0].eval()

			_UpperCamelCase					:Optional[Any]					     =	True if config.feat_extract_norm == '''layer''' else False
			_UpperCamelCase					:str					     =	WavaVecaFeatureExtractor(
			    feature_size=1       ,					sampling_rate=1_60_00       ,					padding_value=0       ,					do_normalize=snake_case__       ,					return_attention_mask=snake_case__       ,					)

			if is_finetuned:
						if dict_path:
									_UpperCamelCase					:int					     =	Dictionary.load(snake_case__		)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									_UpperCamelCase					:List[Any]					     =	target_dict.pad_index
									_UpperCamelCase					:str					     =	target_dict.bos_index
									_UpperCamelCase					:int					     =	target_dict.pad_index
									_UpperCamelCase					:Dict					     =	target_dict.bos_index
									_UpperCamelCase					:int					     =	target_dict.eos_index
									_UpperCamelCase					:str					     =	len(target_dict.symbols		)
									_UpperCamelCase					:Optional[int]					     =	os.path.join(snake_case__       ,					'''vocab.json'''		)
									if not os.path.isdir(snake_case__		):
												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case__		)		)
												return
									os.makedirs(snake_case__       ,					exist_ok=snake_case__		)
									with open(snake_case__       ,					'''w'''       ,					encoding='''utf-8'''		) as vocab_handle:
												json.dump(target_dict.indices       ,					snake_case__		)
									_UpperCamelCase					:int					     =	WavaVecaCTCTokenizer(
									    snake_case__       ,					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=snake_case__       ,					)
									_UpperCamelCase					:Dict					     =	WavaVecaProcessor(feature_extractor=snake_case__       ,					tokenizer=snake_case__		)
									processor.save_pretrained(snake_case__		)

						_UpperCamelCase					:Tuple					     =	SEWForCTC(snake_case__		)
			else:
						_UpperCamelCase					:str					     =	SEWModel(snake_case__		)
						feature_extractor.save_pretrained(snake_case__		)

			recursively_load_weights(snake_case__       ,					snake_case__       ,					snake_case__		)

			hf_model.save_pretrained(snake_case__		)


if __name__ == "__main__":
					UpperCamelCase__				:List[str]			   =	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(
					    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					UpperCamelCase__				:Tuple			   =	parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)

