




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:		Dict          =						logging.get_logger(__name__)

__A						:		int          =						{}

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= "fsmt_decoder"




					def __init__(   self:		Optional[Any],     _lowercase:		Optional[Any]=0,     _lowercase:		Dict=0):


									'''simple docstring'''


									super().__init__()
									__lowerCAmelCase					=  vocab_size
									__lowerCAmelCase					=  bos_token_id





class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= "fsmt"
					__UpperCamelCase     	= {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(   self:		str,     _lowercase:		Optional[Any]=["en", "de"],     _lowercase:		List[str]=42024,     _lowercase:		List[str]=42024,     _lowercase:		Optional[int]="relu",     _lowercase:		Union[str, Any]=1024,     _lowercase:		Dict=200,     _lowercase:		Dict=1024,     _lowercase:		List[str]=4096,     _lowercase:		str=12,     _lowercase:		Optional[Any]=16,     _lowercase:		List[str]=0.0,     _lowercase:		List[Any]=4096,     _lowercase:		Dict=12,     _lowercase:		Optional[int]=16,     _lowercase:		Optional[int]=0.0,     _lowercase:		Union[str, Any]=0.0,     _lowercase:		Dict=0.1,     _lowercase:		List[Any]=0.0,     _lowercase:		Optional[Any]=0.02,     _lowercase:		Optional[int]=2,     _lowercase:		int=True,     _lowercase:		List[str]=True,     _lowercase:		Any=False,     _lowercase:		Union[str, Any]=5,     _lowercase:		Tuple=1.0,     _lowercase:		List[str]=False,     _lowercase:		Tuple=True,     _lowercase:		Union[str, Any]=1,     _lowercase:		Union[str, Any]=0,     _lowercase:		Optional[int]=2,     _lowercase:		Dict=2,     **_lowercase:		Optional[Any],     ):


									'''simple docstring'''


									__lowerCAmelCase					=  langs
									__lowerCAmelCase					=  src_vocab_size
									__lowerCAmelCase					=  tgt_vocab_size
									__lowerCAmelCase					=  d_model  # encoder_embed_dim and decoder_embed_dim

									__lowerCAmelCase					=  encoder_ffn_dim
									__lowerCAmelCase					=  __lowerCAmelCase					=  encoder_layers
									__lowerCAmelCase					=  encoder_attention_heads
									__lowerCAmelCase					=  encoder_layerdrop
									__lowerCAmelCase					=  decoder_layerdrop
									__lowerCAmelCase					=  decoder_ffn_dim
									__lowerCAmelCase					=  decoder_layers
									__lowerCAmelCase					=  decoder_attention_heads
									__lowerCAmelCase					=  max_position_embeddings
									__lowerCAmelCase					=  init_std  # Normal(0, this parameter)
									__lowerCAmelCase					=  activation_function

									__lowerCAmelCase					=  DecoderConfig(vocab_size=_lowercase,     bos_token_id=_lowercase)
									if "decoder" in common_kwargs:
													del common_kwargs["decoder"]

									__lowerCAmelCase					=  scale_embedding  # scale factor will be sqrt(d_model) if True

									# 3 Types of Dropout
									__lowerCAmelCase					=  attention_dropout
									__lowerCAmelCase					=  activation_dropout
									__lowerCAmelCase					=  dropout

									__lowerCAmelCase					=  use_cache
									super().__init__(
									    pad_token_id=_lowercase,     bos_token_id=_lowercase,     eos_token_id=_lowercase,     decoder_start_token_id=_lowercase,     is_encoder_decoder=_lowercase,     tie_word_embeddings=_lowercase,     forced_eos_token_id=_lowercase,     max_length=_lowercase,     num_beams=_lowercase,     length_penalty=_lowercase,     early_stopping=_lowercase,     **_lowercase,     )







					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									__lowerCAmelCase					=  copy.deepcopy(self.__dict__)
									__lowerCAmelCase					=  self.decoder.to_dict()
									__lowerCAmelCase					=  self.__class__.model_type
									return output






"""simple docstring"""




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase       :    int									=			logging.get_logger(__name__)

UpperCAmelCase       :    Optional[int]									=			["UtteranceLevel"]



@torch.no_grad()
def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   List[str]  				=					torch.load(_lowercase							, map_location='''cpu'''						)
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

					lowerCamelCase__		:   List[Any]  				=					checkpoint['''Downstream''']

					lowerCamelCase__		:   int  				=					HubertConfig.from_pretrained(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					HubertForSequenceClassification.from_pretrained(_lowercase							, config=_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					WavaVecaFeatureExtractor.from_pretrained(
					    _lowercase							, return_attention_mask=_lowercase							, do_normalize=_lowercase						)

					if hf_congfig.use_weighted_layer_sum:
										lowerCamelCase__		:   Dict  				=					checkpoint['''Featurizer''']['''weights''']

					lowerCamelCase__		:   Any  				=					downstream_dict['''projector.weight''']
					lowerCamelCase__		:   Optional[int]  				=					downstream_dict['''projector.bias''']
					lowerCamelCase__		:   Dict  				=					downstream_dict['''model.post_net.linear.weight''']
					lowerCamelCase__		:   str  				=					downstream_dict['''model.post_net.linear.bias''']

					hf_feature_extractor.save_pretrained(_lowercase						)
					hf_model.save_pretrained(_lowercase						)


if __name__ == "__main__":
					UpperCAmelCase       :    int									=			argparse.ArgumentParser()
					parser.add_argument(
					    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
					)
					parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
					parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
					UpperCAmelCase       :    Dict									=			parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






