





"""simple docstring"""






import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


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


A       	=     "https://openaipublic.azureedge.net/jukebox/models/"
A       	=     {
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}

def 			__A					(	a_  :int)						->						List[str]:
				if key.endswith('''.model.1.bias''') and len(key.split('''.''')) > 10:
								__a       :  int					=     key.replace('''.model.1.bias'''      ,				'''.conv1d_1.bias''')
				elif key.endswith('''.model.1.weight''') and len(key.split('''.''')) > 10:
								__a       :  Tuple					=     key.replace('''.model.1.weight'''      ,				'''.conv1d_1.weight''')
				elif key.endswith('''.model.3.bias''') and len(key.split('''.''')) > 10:
								__a       :  Union[str, Any]					=     key.replace('''.model.3.bias'''      ,				'''.conv1d_2.bias''')
				elif key.endswith('''.model.3.weight''') and len(key.split('''.''')) > 10:
								__a       :  List[str]					=     key.replace('''.model.3.weight'''      ,				'''.conv1d_2.weight''')

				if "conditioner_blocks.0." in key:
								__a       :  Optional[int]					=     key.replace('''conditioner_blocks.0'''      ,				'''conditioner_blocks''')

				if "prime_prior" in key:
								__a       :  Union[str, Any]					=     key.replace('''prime_prior'''      ,				'''encoder''')

				if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
								__a       :  str					=     key.replace('''.emb.'''      ,				'''.''')

				if key.endswith('''k'''):  # replace vqvae.X.k with vqvae.X.codebook
								return key.replace('''.k'''      ,				'''.codebook''')
				if "y_emb." in key:
								return key.replace('''y_emb.'''      ,				'''metadata_embedding.''')

				if "x_emb.emb." in key:
								__a       :  Dict					=     key.replace('''0.x_emb.emb'''      ,				'''embed_tokens''')

				if "prime_state_ln" in key:
								return key.replace('''prime_state_ln'''      ,				'''encoder.final_layer_norm''')
				if ".ln" in key:
								return key.replace('''.ln'''      ,				'''.layer_norm''')
				if "_ln" in key:
								return key.replace('''_ln'''      ,				'''_layer_norm''')

				if "prime_state_proj" in key:
								return key.replace('''prime_state_proj'''      ,				'''encoder.proj_in''')
				if "prime_x_out" in key:
								return key.replace('''prime_x_out'''      ,				'''encoder.lm_head''')
				if "prior.x_out" in key:
								return key.replace('''x_out'''      ,				'''fc_proj_out''')
				if "x_emb" in key:
								return key.replace('''x_emb'''      ,				'''embed_tokens''')

				return key

def 			__A					(	a_  :str      ,				a_  :Dict      ,				a_  :Optional[Any]      ,				a_  :Tuple)						->						Dict:
				__a       :  Union[str, Any]					=     {}
				import re

				__a       :  List[Any]					=     re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''')
				__a       :  Optional[int]					=     re.compile(
				    R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''')
				__a       :  Any					=     re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''')

				__a       :  Any					=     re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''')
				__a       :  str					=     re.compile(
				    R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''')
				__a       :  Any					=     re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''')

				__a       :  List[Any]					=     re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)''')
				__a       :  List[Any]					=     re.compile(
				    R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''')
				__a       :  Any					=     re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)''')

				for original_key, value in state_dict.items():
								# rename vqvae.encoder keys
								if re_encoder_block_conv_in.fullmatch(__lowerCamelCase):
												__a       :  int					=     re_encoder_block_conv_in.match(__lowerCamelCase)
												__a       :  Optional[int]					=     regex_match.groups()
												__a       :  List[Any]					=     int(groups[2]) * 2 + int(groups[3])
												__a       :  Union[str, Any]					=     F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
												__a       :  int					=     re_encoder_block_conv_in.sub(__lowerCamelCase      ,				__lowerCamelCase)

								elif re_encoder_block_resnet.fullmatch(__lowerCamelCase):
												__a       :  List[Any]					=     re_encoder_block_resnet.match(__lowerCamelCase)
												__a       :  int					=     regex_match.groups()
												__a       :  List[str]					=     int(groups[2]) * 2 + int(groups[3])
												__a       :  int					=     {'''1''': 1, '''3''': 2}[groups[-2]]
												__a       :  List[Any]					=     F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
												__a       :  Tuple					=     F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
												__a       :  int					=     prefix + resnet_block
												__a       :  Optional[Any]					=     re_encoder_block_resnet.sub(__lowerCamelCase      ,				__lowerCamelCase)

								elif re_encoder_block_proj_out.fullmatch(__lowerCamelCase):
												__a       :  int					=     re_encoder_block_proj_out.match(__lowerCamelCase)
												__a       :  Any					=     regex_match.groups()
												__a       :  str					=     F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
												__a       :  Optional[Any]					=     re_encoder_block_proj_out.sub(__lowerCamelCase      ,				__lowerCamelCase)

								# rename vqvae.decoder keys
								elif re_decoder_block_conv_out.fullmatch(__lowerCamelCase):
												__a       :  Dict					=     re_decoder_block_conv_out.match(__lowerCamelCase)
												__a       :  Dict					=     regex_match.groups()
												__a       :  Optional[Any]					=     int(groups[2]) * 2 + int(groups[3]) - 2
												__a       :  List[Any]					=     F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
												__a       :  str					=     re_decoder_block_conv_out.sub(__lowerCamelCase      ,				__lowerCamelCase)

								elif re_decoder_block_resnet.fullmatch(__lowerCamelCase):
												__a       :  int					=     re_decoder_block_resnet.match(__lowerCamelCase)
												__a       :  Optional[Any]					=     regex_match.groups()
												__a       :  Union[str, Any]					=     int(groups[2]) * 2 + int(groups[3]) - 2
												__a       :  Optional[Any]					=     {'''1''': 1, '''3''': 2}[groups[-2]]
												__a       :  Optional[int]					=     F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
												__a       :  int					=     F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
												__a       :  Union[str, Any]					=     prefix + resnet_block
												__a       :  str					=     re_decoder_block_resnet.sub(__lowerCamelCase      ,				__lowerCamelCase)

								elif re_decoder_block_proj_in.fullmatch(__lowerCamelCase):
												__a       :  Any					=     re_decoder_block_proj_in.match(__lowerCamelCase)
												__a       :  Any					=     regex_match.groups()
												__a       :  List[str]					=     F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
												__a       :  Tuple					=     re_decoder_block_proj_in.sub(__lowerCamelCase      ,				__lowerCamelCase)

								# rename prior cond.model to upsampler.upsample_block and resnet
								elif re_prior_cond_conv_out.fullmatch(__lowerCamelCase):
												__a       :  Union[str, Any]					=     re_prior_cond_conv_out.match(__lowerCamelCase)
												__a       :  Tuple					=     regex_match.groups()
												__a       :  Dict					=     int(groups[1]) * 2 + int(groups[2]) - 2
												__a       :  Tuple					=     F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
												__a       :  List[Any]					=     re_prior_cond_conv_out.sub(__lowerCamelCase      ,				__lowerCamelCase)

								elif re_prior_cond_resnet.fullmatch(__lowerCamelCase):
												__a       :  Any					=     re_prior_cond_resnet.match(__lowerCamelCase)
												__a       :  Optional[int]					=     regex_match.groups()
												__a       :  Union[str, Any]					=     int(groups[1]) * 2 + int(groups[2]) - 2
												__a       :  Optional[Any]					=     {'''1''': 1, '''3''': 2}[groups[-2]]
												__a       :  Union[str, Any]					=     F"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
												__a       :  List[str]					=     F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
												__a       :  Optional[Any]					=     prefix + resnet_block
												__a       :  Optional[Any]					=     re_prior_cond_resnet.sub(__lowerCamelCase      ,				__lowerCamelCase)

								elif re_prior_cond_proj_in.fullmatch(__lowerCamelCase):
												__a       :  List[str]					=     re_prior_cond_proj_in.match(__lowerCamelCase)
												__a       :  Union[str, Any]					=     regex_match.groups()
												__a       :  Optional[Any]					=     F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
												__a       :  Tuple					=     re_prior_cond_proj_in.sub(__lowerCamelCase      ,				__lowerCamelCase)

								# keep original key
								else:
												__a       :  Union[str, Any]					=     original_key

								__a       :  str					=     replace_key(__lowerCamelCase)

								if F"""{key_prefix}.{key}""" not in model_state_dict or key is None:
												print(F"""failed converting {original_key} to {key}, does not match""")

								# handle missmatched shape
								elif value.shape != model_state_dict[F"""{key_prefix}.{key}"""].shape:
												__a       :  int					=     model_state_dict[F"""{key_prefix}.{key}"""]
												print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match""")
												__a       :  Dict					=     original_key

								__a       :  Tuple					=     original_key
								__a       :  int					=     value

				return new_dict

@torch.no_grad()
def 			__A					(	a_  :str=None      ,				a_  :List[str]=None)						->						Tuple:
				for file in MODEL_MAPPING[model_name]:
								if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/")[-1]}"""):
												__a       :  Tuple					=     requests.get(F"""{PREFIX}{file}"""      ,				allow_redirects=__lowerCamelCase)
												os.makedirs(F"""{pytorch_dump_folder_path}/"""      ,				exist_ok=__lowerCamelCase)
												open(F"""{pytorch_dump_folder_path}/{file.split("/")[-1]}"""      ,				'''wb''').write(r.content)

				__a       :  Optional[int]					=     MODEL_MAPPING[model_name.split('''/''')[-1]]

				__a       :  List[str]					=     JukeboxConfig.from_pretrained(__lowerCamelCase)
				__a       :  List[str]					=     JukeboxModel(__lowerCamelCase)

				__a       :  Union[str, Any]					=     []
				__a       :  Dict					=     {}
				for i, dict_name in enumerate(__lowerCamelCase):
								__a       :  Union[str, Any]					=     torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split("/")[-1]}""")['''model''']

								__a       :  Optional[int]					=     {}
								for k in old_dic.keys():
												if k.endswith('''.b'''):
																__a       :  Optional[int]					=     old_dic[k]
												elif k.endswith('''.w'''):
																__a       :  List[str]					=     old_dic[k]
												elif "level_2" not in dict_name and "cond.model." in k:
																__a       :  Any					=     old_dic[k]
												else:
																__a       :  Tuple					=     old_dic[k]

								__a       :  List[Any]					=     '''vqvae''' if i == 0 else F"""priors.{3 - i}"""
								__a       :  str					=     fix_jukebox_keys(__lowerCamelCase      ,				model.state_dict()      ,				__lowerCamelCase      ,				__lowerCamelCase)
								weight_dict.append(__lowerCamelCase)

				__a       :  Optional[int]					=     weight_dict.pop(0)
				model.vqvae.load_state_dict(__lowerCamelCase)
				for i in range(len(__lowerCamelCase)):
								model.priors[i].load_state_dict(weight_dict[2 - i])

				Path(__lowerCamelCase).mkdir(exist_ok=__lowerCamelCase)
				with open(F"""{pytorch_dump_folder_path}/mapping.json"""      ,				'''w''') as txtfile:
								json.dump(__lowerCamelCase      ,				__lowerCamelCase)

				print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""")
				model.save_pretrained(__lowerCamelCase)

				return weight_dict


if __name__ == "__main__":
		A       	=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''jukebox-5b-lyrics''',
		    type=str,
		    help='''Name of the model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default='''jukebox-5b-lyrics-converted''',
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		A       	=     parser.parse_args()
		convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





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()
__UpperCamelCase					:       Union[str, Any]    					=      logging.get_logger(__name__)

__UpperCamelCase					:       Any    					=      {
    "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",
}
__UpperCamelCase					:       Dict    					=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]





def 		__A		(	__lowerCamelCase    )      ->       List[str]:
	a										=							{}
	with open(__lowerCamelCase							,			"""r"""    ) as file:
		for line_number, line in enumerate(__lowerCamelCase    ):
			a										=							line.strip()
			if line:
				a										=							line.split()
				a										=							line_number
				a										=							words[0]
				a										=							value
	return result





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	for attribute in key.split("""."""    ):
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )

	a										=							None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(__lowerCamelCase    ):
			a										=							PARAM_MAPPING[full_name.split("""."""    )[-1]]
			a										=							"""param"""

	if weight_type is not None and weight_type != "param":
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    ).shape
	elif weight_type is not None and weight_type == "param":
		a										=							hf_pointer
		for attribute in hf_param_name.split("""."""    ):
			a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )
		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(__lowerCamelCase							,			__lowerCamelCase    )
		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		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Union[str, Any]:
	a										=							None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(__lowerCamelCase    ):
			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]


__UpperCamelCase					:       List[Any]    					=      {
    "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		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None    )      ->       Optional[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(__lowerCamelCase    )[0].split("""."""    )[-2]
				a										=							mapped_key.replace("""*"""							,			__lowerCamelCase    )
			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(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
			else:
				set_recursively(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
			return is_used
	return is_used





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       List[Any]:
	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(
			    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			hf_model.config.feat_extract_norm == """group"""							,			)
			a										=							True
		else:
			a										=							load_wavaveca_layer(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
		if not is_used:
			unused_weights.append(__lowerCamelCase    )

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





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       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(__lowerCamelCase    )





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=True							,			__lowerCamelCase=False    )      ->       List[Any]:
	if config_path is not None:
		a										=							WavaVecaConfig.from_pretrained(__lowerCamelCase    )
	else:
		a										=							WavaVecaConfig()

	if is_seq_class:
		a										=							read_txt_into_dict(__lowerCamelCase    )
		a										=							idalabel
		a										=							WavaVecaForSequenceClassification(__lowerCamelCase    )
		a										=							WavaVecaFeatureExtractor(
		    feature_size=1							,			sampling_rate=1_6000							,			padding_value=0							,			do_normalize=__lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			)
		feature_extractor.save_pretrained(__lowerCamelCase    )

	elif is_finetuned:
		if dict_path:
			a										=							Dictionary.load(__lowerCamelCase    )

			# 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(__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										=							target_dict.indices

			# fairseq has the <pad> and <s> switched
			a										=							0
			a										=							1
			with open(__lowerCamelCase							,			"""w"""							,			encoding="""utf-8"""    ) as vocab_handle:
				json.dump(__lowerCamelCase							,			__lowerCamelCase    )
			a										=							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										=							True if config.feat_extract_norm == """layer""" else False
			a										=							WavaVecaFeatureExtractor(
			    feature_size=1							,			sampling_rate=1_6000							,			padding_value=0							,			do_normalize=__lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			)
			a										=							WavaVecaProcessor(feature_extractor=__lowerCamelCase							,			tokenizer=__lowerCamelCase    )
			processor.save_pretrained(__lowerCamelCase    )

		a										=							WavaVecaForCTC(__lowerCamelCase    )
	else:
		a										=							WavaVecaForPreTraining(__lowerCamelCase    )

	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(__lowerCamelCase    )

		a				, a				, a										=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,			task=__lowerCamelCase    )

	a										=							model[0].eval()

	recursively_load_weights(__lowerCamelCase							,			__lowerCamelCase							,			not is_finetuned    )

	hf_wavavec.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       int    					=      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",
		)
		__UpperCamelCase					:       Union[str, Any]    					=      parser.parse_args()

		__UpperCamelCase					:       Any    					=      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,
		)

