



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()
SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)

SCREAMING_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''',
}
SCREAMING_SNAKE_CASE__   			=   [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]


def A    (							__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     {}
			with open(__UpperCamelCase			,						'r'       ) as file:
						for line_number, line in enumerate(__UpperCamelCase       ):
									A__	  =     line.strip()
									if line:
												A__	  =     line.split()
												A__	  =     line_number
												A__	  =     words[0]
												A__	  =     value
			return result


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Tuple:
			for attribute in key.split('.'       ):
						A__	  =     getattr(__UpperCamelCase			,						__UpperCamelCase       )

			A__	  =     None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(__UpperCamelCase       ):
									A__	  =     PARAM_MAPPING[full_name.split('.'       )[-1]]
									A__	  =     'param'

			if weight_type is not None and weight_type != "param":
						A__	  =     getattr(__UpperCamelCase			,						__UpperCamelCase       ).shape
			elif weight_type is not None and weight_type == "param":
						A__	  =     hf_pointer
						for attribute in hf_param_name.split('.'       ):
									A__	  =     getattr(__UpperCamelCase			,						__UpperCamelCase       )
						A__	  =     shape_pointer.shape

						# let's reduce dimension
						A__	  =     value[0]
			else:
						A__	  =     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__	  =     value
			elif weight_type == "weight_g":
						A__	  =     value
			elif weight_type == "weight_v":
						A__	  =     value
			elif weight_type == "bias":
						A__	  =     value
			elif weight_type == "param":
						for attribute in hf_param_name.split('.'       ):
									A__	  =     getattr(__UpperCamelCase			,						__UpperCamelCase       )
						A__	  =     value
			else:
						A__	  =     value

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


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Tuple:
			A__	  =     None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(__UpperCamelCase       ):
									A__	  =     PARAM_MAPPING[full_name.split('.'       )[-1]]
									A__	  =     'param'

			if weight_type is not None and weight_type != "param":
						A__	  =     '.'.join([key, weight_type]       )
			elif weight_type is not None and weight_type == "param":
						A__	  =     '.'.join([key, hf_param_name]       )
			else:
						A__	  =     key

			A__	  =     value if 'lm_head' in full_key else value[0]


SCREAMING_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 A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=None			,						__UpperCamelCase=None       )    ->   Union[str, Any]:
			A__	  =     False
			for key, mapped_key in MAPPING.items():
						A__	  =     '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__	  =     True
									if "*" in mapped_key:
												A__	  =     name.split(__UpperCamelCase       )[0].split('.'       )[-2]
												A__	  =     mapped_key.replace('*'			,						__UpperCamelCase       )
									if "weight_g" in name:
												A__	  =     'weight_g'
									elif "weight_v" in name:
												A__	  =     'weight_v'
									elif "bias" in name:
												A__	  =     'bias'
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												A__	  =     'weight'
									else:
												A__	  =     None
									if hf_dict is not None:
												rename_dict(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
									else:
												set_recursively(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
									return is_used
			return is_used


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[str]:
			A__	  =     []
			A__	  =     fairseq_model.state_dict()

			A__	  =     hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						A__	  =     False
						if "conv_layers" in name:
									load_conv_layer(
									    __UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						hf_model.config.feat_extract_norm == 'group'			,						)
									A__	  =     True
						else:
									A__	  =     load_wavaveca_layer(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
						if not is_used:
									unused_weights.append(__UpperCamelCase       )

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


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     full_name.split('conv_layers.'       )[-1]
			A__	  =     name.split('.'       )
			A__	  =     int(items[0]       )
			A__	  =     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__	  =     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__	  =     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__	  =     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__	  =     value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
			else:
						unused_weights.append(__UpperCamelCase       )


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=None			,						__UpperCamelCase=None			,						__UpperCamelCase=True			,						__UpperCamelCase=False       )    ->   int:
			if config_path is not None:
						A__	  =     WavaVecaConfig.from_pretrained(__UpperCamelCase       )
			else:
						A__	  =     WavaVecaConfig()

			if is_seq_class:
						A__	  =     read_txt_into_dict(__UpperCamelCase       )
						A__	  =     idalabel
						A__	  =     WavaVecaForSequenceClassification(__UpperCamelCase       )
						A__	  =     WavaVecaFeatureExtractor(
						    feature_size=1			,						sampling_rate=16_000			,						padding_value=0			,						do_normalize=__UpperCamelCase			,						return_attention_mask=__UpperCamelCase			,						)
						feature_extractor.save_pretrained(__UpperCamelCase       )

			elif is_finetuned:
						if dict_path:
									A__	  =     Dictionary.load(__UpperCamelCase       )

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

									# fairseq has the <pad> and <s> switched
									A__	  =     0
									A__	  =     1
									with open(__UpperCamelCase			,						'w'			,						encoding='utf-8'       ) as vocab_handle:
												json.dump(__UpperCamelCase			,						__UpperCamelCase       )
									A__	  =     WavaVecaCTCTokenizer(
									    __UpperCamelCase			,						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=__UpperCamelCase			,						)
									A__	  =     True if config.feat_extract_norm == 'layer' else False
									A__	  =     WavaVecaFeatureExtractor(
									    feature_size=1			,						sampling_rate=16_000			,						padding_value=0			,						do_normalize=__UpperCamelCase			,						return_attention_mask=__UpperCamelCase			,						)
									A__	  =     WavaVecaProcessor(feature_extractor=__UpperCamelCase			,						tokenizer=__UpperCamelCase       )
									processor.save_pretrained(__UpperCamelCase       )

						A__	  =     WavaVecaForCTC(__UpperCamelCase       )
			else:
						A__	  =     WavaVecaForPreTraining(__UpperCamelCase       )

			if is_finetuned or is_seq_class:
						A__  ,			A__  ,			A__	  =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]			,						arg_overrides={'data': '/'.join(dict_path.split('/'       )[:-1]       )}       )
			else:
						A__	  =     argparse.Namespace(task='audio_pretraining'       )
						A__	  =     fairseq.tasks.setup_task(__UpperCamelCase       )

						A__  ,			A__  ,			A__	  =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,						task=__UpperCamelCase       )

			A__	  =     model[0].eval()

			recursively_load_weights(__UpperCamelCase			,						__UpperCamelCase			,						not is_finetuned       )

			hf_wavavec.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_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''',
							)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							SCREAMING_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,
							)








"""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
					)

