"""simple docstring"""




import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


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

UpperCAmelCase       :    Optional[int]									=			[
    (
        "protein1",
        "MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
    ),
    ("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
    ("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
    ("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
]

UpperCAmelCase       :    Optional[Any]									=			{
    "esm1b_t33_650M_UR50S": esm_module.pretrained.esm1b_t33_650M_UR50S,
    "esm1v_t33_650M_UR90S_1": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    "esm1v_t33_650M_UR90S_2": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    "esm1v_t33_650M_UR90S_3": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    "esm1v_t33_650M_UR90S_4": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    "esm1v_t33_650M_UR90S_5": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    "esm2_t48_15B_UR50D": esm_module.pretrained.esm2_t48_15B_UR50D,
    "esm2_t36_3B_UR50D": esm_module.pretrained.esm2_t36_3B_UR50D,
    "esm2_t33_650M_UR50D": esm_module.pretrained.esm2_t33_650M_UR50D,
    "esm2_t30_150M_UR50D": esm_module.pretrained.esm2_t30_150M_UR50D,
    "esm2_t12_35M_UR50D": esm_module.pretrained.esm2_t12_35M_UR50D,
    "esm2_t6_8M_UR50D": esm_module.pretrained.esm2_t6_8M_UR50D,
    "esmfold_v1": esmfold_va,
}

UpperCAmelCase       :    Dict									=			list("ARNDCQEGHILKMFPSTWYV")

UpperCAmelCase       :    Dict									=			restypes + ["X"]
UpperCAmelCase       :    Optional[int]									=			restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]



def 				__a       (			):







					"""simple docstring"""





					with TemporaryDirectory() as tempdir:
										lowerCamelCase__		:   Optional[Any]  				=					'''\n'''.join(_lowercase						)
										lowerCamelCase__		:   Union[str, Any]  				=					Path(_lowercase						) / '''vocab.txt'''
										vocab_file.write_text(_lowercase						)
										lowerCamelCase__		:   Tuple  				=					EsmTokenizer(vocab_file=str(_lowercase						)						)
					lowerCamelCase__		:   int  				=					0  # Overlaps with 'A' but that seems to be what they want
					return hf_tokenizer



def 				__a       (			_lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   Union[str, Any]  				=					our_module.load_state_dict(original_module.state_dict()						)
					if status.missing_keys:
										raise ValueError(f"""Missing keys: {status.missing_keys}"""						)
					if status.unexpected_keys:
										raise ValueError(f"""Unexpected keys: {status.unexpected_keys}"""						)



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					if model.startswith('''esmfold'''						):
										lowerCamelCase__		:   str  				=					MODEL_MAPPING[model]()
					else:
										lowerCamelCase__				,						lowerCamelCase__		:   Any  				=					MODEL_MAPPING[model]()
					esm.eval()  # disable dropout

					if model.startswith('''esmfold'''						):
										lowerCamelCase__		:   int  				=					esm.esm.embed_dim
										lowerCamelCase__		:   str  				=					esm.esm.num_layers
										lowerCamelCase__		:   List[str]  				=					esm.esm.attention_heads
										lowerCamelCase__		:   Tuple  				=					4 * embed_dim
										lowerCamelCase__		:   Dict  				=					esm.esm.token_dropout
										lowerCamelCase__		:   str  				=					False  # This code path does not exist in ESM-2
										lowerCamelCase__		:   Optional[Any]  				=					'''rotary'''
										lowerCamelCase__		:   Optional[int]  				=					True
										lowerCamelCase__		:   Optional[int]  				=					EsmFoldConfig()
										for key, val in esm.cfg.items():
															if hasattr(_lowercase							, _lowercase						) and key != "trunk":
																				setattr(_lowercase							, _lowercase							, _lowercase						)
										for key, val in esm.cfg.trunk.items():
															if hasattr(esmfold_config.trunk							, _lowercase						) and key != "structure_module":
																				setattr(esmfold_config.trunk							, _lowercase							, _lowercase						)
										for key, val in esm.cfg.trunk.structure_module.items():
															if hasattr(esmfold_config.trunk.structure_module							, _lowercase						):
																				setattr(esmfold_config.trunk.structure_module							, _lowercase							, _lowercase						)
					elif hasattr(_lowercase							, '''args'''						):
										# Indicates an ESM-1b or ESM-1v model
										lowerCamelCase__		:   Optional[int]  				=					esm.args.embed_dim
										lowerCamelCase__		:   List[str]  				=					esm.args.layers
										lowerCamelCase__		:   Any  				=					esm.args.attention_heads
										lowerCamelCase__		:   int  				=					esm.args.ffn_embed_dim
										lowerCamelCase__		:   List[Any]  				=					esm.args.token_dropout
										lowerCamelCase__		:   Optional[int]  				=					True if esm.emb_layer_norm_before else False
										lowerCamelCase__		:   str  				=					'''absolute'''
										lowerCamelCase__		:   List[str]  				=					False
										lowerCamelCase__		:   Optional[int]  				=					None
					else:
										# Indicates an ESM-2 model
										lowerCamelCase__		:   Any  				=					esm.embed_dim
										lowerCamelCase__		:   List[str]  				=					esm.num_layers
										lowerCamelCase__		:   Optional[Any]  				=					esm.attention_heads
										lowerCamelCase__		:   List[str]  				=					4 * embed_dim  # This is hardcoded in ESM-2
										lowerCamelCase__		:   Any  				=					esm.token_dropout
										lowerCamelCase__		:   Union[str, Any]  				=					False  # This code path does not exist in ESM-2
										lowerCamelCase__		:   Optional[Any]  				=					'''rotary'''
										lowerCamelCase__		:   Optional[int]  				=					False
										lowerCamelCase__		:   Optional[Any]  				=					None

					if is_folding_model:
										lowerCamelCase__		:   str  				=					esm.esm.alphabet
					lowerCamelCase__		:   Optional[int]  				=					tuple(alphabet.all_toks						)
					lowerCamelCase__		:   Dict  				=					alphabet.mask_idx
					lowerCamelCase__		:   Dict  				=					alphabet.padding_idx

					if is_folding_model:
										lowerCamelCase__		:   Optional[int]  				=					esm.esm
					else:
										lowerCamelCase__		:   List[Any]  				=					esm

					lowerCamelCase__		:   List[str]  				=					EsmConfig(
					    vocab_size=original_esm_model.embed_tokens.num_embeddings							, mask_token_id=_lowercase							, hidden_size=_lowercase							, num_hidden_layers=_lowercase							, num_attention_heads=_lowercase							, intermediate_size=_lowercase							, max_position_embeddings=1026							, layer_norm_eps=1e-5							, attention_probs_dropout_prob=0.0							, hidden_dropout_prob=0.0							, pad_token_id=_lowercase							, emb_layer_norm_before=_lowercase							, token_dropout=_lowercase							, position_embedding_type=_lowercase							, is_folding_model=_lowercase							, esmfold_config=_lowercase							, vocab_list=_lowercase							, )
					if classification_head:
										lowerCamelCase__		:   Optional[int]  				=					esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
					print('''Our ESM config:'''							, _lowercase						)

					if model.startswith('''esmfold'''						):
										lowerCamelCase__		:   Tuple  				=					EsmForProteinFolding
					elif classification_head:
										lowerCamelCase__		:   Union[str, Any]  				=					EsmForSequenceClassification
					else:
										lowerCamelCase__		:   str  				=					EsmForMaskedLM
					lowerCamelCase__		:   Any  				=					model_class(_lowercase						)
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					lowerCamelCase__		:   Tuple  				=					original_esm_model.embed_tokens.weight
					if position_embedding_type == "absolute":
										lowerCamelCase__		:   Any  				=					original_esm_model.embed_positions.weight

					if config.emb_layer_norm_before:
										lowerCamelCase__		:   Dict  				=					original_esm_model.emb_layer_norm_before.weight
										lowerCamelCase__		:   Tuple  				=					original_esm_model.emb_layer_norm_before.bias

					lowerCamelCase__		:   str  				=					original_esm_model.emb_layer_norm_after.weight
					lowerCamelCase__		:   int  				=					original_esm_model.emb_layer_norm_after.bias

					for i in range(config.num_hidden_layers						):
										# Encoder: start of layer
										lowerCamelCase__		:   EsmLayer  				=					model.esm.encoder.layer[i]
										# esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
										lowerCamelCase__		:   int  				=					original_esm_model.layers[i]

										# self attention
										lowerCamelCase__		:   EsmSelfAttention  				=					layer.attention.self
										assert (
										    esm_layer.self_attn.k_proj.weight.data.shape
										    == esm_layer.self_attn.q_proj.weight.data.shape
										    == esm_layer.self_attn.v_proj.weight.data.shape
										    == torch.Size((config.hidden_size, config.hidden_size)						)
										)

										lowerCamelCase__		:   Optional[int]  				=					esm_layer.self_attn.q_proj.weight
										lowerCamelCase__		:   str  				=					esm_layer.self_attn.q_proj.bias
										lowerCamelCase__		:   Tuple  				=					esm_layer.self_attn.k_proj.weight
										lowerCamelCase__		:   str  				=					esm_layer.self_attn.k_proj.bias
										lowerCamelCase__		:   List[str]  				=					esm_layer.self_attn.v_proj.weight
										lowerCamelCase__		:   Dict  				=					esm_layer.self_attn.v_proj.bias

										if getattr(esm_layer.self_attn							, '''rot_emb'''							, _lowercase						) is not None:
															# Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
															# During the training of ESM-2 the model was converted to float16 precision, which also converts
															# the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
															# If we recompute inv_freq without this loss of precision then we will get subtly different rotary
															# embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
															# we make sure the new model copies the data from the old inv_freq.
															lowerCamelCase__		:   Optional[Any]  				=					esm_layer.self_attn.rot_emb.inv_freq

										# LayerNorm changes for pre-activation
										lowerCamelCase__		:   Any  				=					esm_layer.self_attn_layer_norm.weight
										lowerCamelCase__		:   str  				=					esm_layer.self_attn_layer_norm.bias
										lowerCamelCase__		:   str  				=					esm_layer.final_layer_norm.weight
										lowerCamelCase__		:   Tuple  				=					esm_layer.final_layer_norm.bias

										# self-attention output
										lowerCamelCase__		:   EsmSelfOutput  				=					layer.attention.output
										assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
										lowerCamelCase__		:   List[str]  				=					esm_layer.self_attn.out_proj.weight
										lowerCamelCase__		:   int  				=					esm_layer.self_attn.out_proj.bias

										# intermediate
										lowerCamelCase__		:   EsmIntermediate  				=					layer.intermediate
										assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
										lowerCamelCase__		:   Union[str, Any]  				=					esm_layer.fca.weight
										lowerCamelCase__		:   Union[str, Any]  				=					esm_layer.fca.bias

										# output
										lowerCamelCase__		:   EsmOutput  				=					layer.output
										assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
										lowerCamelCase__		:   Union[str, Any]  				=					esm_layer.fca.weight
										lowerCamelCase__		:   str  				=					esm_layer.fca.bias
										# end of layer

					if is_folding_model:
										lowerCamelCase__		:   List[str]  				=					esm.esm_s_combine.data
										lowerCamelCase__		:   List[str]  				=					esm.afa_to_esm.data
										transfer_and_check_weights(esm.embedding							, model.embedding						)
										transfer_and_check_weights(esm.esm_s_mlp							, model.esm_s_mlp						)
										transfer_and_check_weights(esm.trunk							, model.trunk						)
										transfer_and_check_weights(esm.distogram_head							, model.distogram_head						)
										transfer_and_check_weights(esm.ptm_head							, model.ptm_head						)
										transfer_and_check_weights(esm.lm_head							, model.lm_head						)
										transfer_and_check_weights(esm.lddt_head							, model.lddt_head						)

					elif classification_head:
										lowerCamelCase__		:   Optional[int]  				=					esm.esm.classification_heads['''mnli'''].dense.weight
										lowerCamelCase__		:   str  				=					esm.classification_heads['''mnli'''].dense.bias
										lowerCamelCase__		:   List[Any]  				=					esm.classification_heads['''mnli'''].out_proj.weight
										lowerCamelCase__		:   Optional[Any]  				=					esm.classification_heads['''mnli'''].out_proj.bias
					else:
										# LM Head
										lowerCamelCase__		:   Tuple  				=					esm.lm_head.dense.weight
										lowerCamelCase__		:   Optional[Any]  				=					esm.lm_head.dense.bias
										lowerCamelCase__		:   Optional[int]  				=					esm.lm_head.layer_norm.weight
										lowerCamelCase__		:   Tuple  				=					esm.lm_head.layer_norm.bias
										lowerCamelCase__		:   Tuple  				=					esm.lm_head.weight
										lowerCamelCase__		:   Dict  				=					esm.lm_head.bias

					# Contact prediction head
					transfer_and_check_weights(esm.contact_head							, model.esm.contact_head						)

					# Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
					if is_folding_model:
										# Folding models aren't trained on masked inputs and don't like mask tokens.
										lowerCamelCase__		:   List[str]  				=					SAMPLE_DATA[:2]
					else:
										lowerCamelCase__		:   Any  				=					SAMPLE_DATA

					if is_folding_model:
										lowerCamelCase__		:   Dict  				=					get_esmfold_tokenizer()
										lowerCamelCase__		:   List[str]  				=					hf_tokenizer(
										    [row[1] for row in sample_data]							, return_tensors='''pt'''							, padding=_lowercase							, add_special_tokens=_lowercase						)
										lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__		:   str  				=					esmfold_encode_sequences([row[1] for row in sample_data]						)
										lowerCamelCase__		:   Dict  				=					torch.all(hf_tokens['''input_ids'''] == esmfold_aas						) and torch.all(
										    hf_tokens['''attention_mask'''] == esmfold_mask						)
					else:
										# Let's check that we get the same results.
										lowerCamelCase__		:   Optional[int]  				=					alphabet.get_batch_converter()
										lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__		:   List[Any]  				=					batch_converter(_lowercase						)
										# Prepare tokenizer and make sure it matches
										with TemporaryDirectory() as tempdir:
															lowerCamelCase__		:   Optional[Any]  				=					'''\n'''.join(alphabet.all_toks						)
															lowerCamelCase__		:   List[Any]  				=					Path(_lowercase						) / '''vocab.txt'''
															vocab_file.write_text(_lowercase						)
															lowerCamelCase__		:   Tuple  				=					EsmTokenizer(vocab_file=str(_lowercase						)						)

										lowerCamelCase__		:   Tuple  				=					hf_tokenizer([row[1] for row in sample_data]							, return_tensors='''pt'''							, padding=_lowercase						)
										lowerCamelCase__		:   List[str]  				=					torch.all(hf_tokens['''input_ids'''] == batch_tokens						)

					print('''Do both models tokenizers output the same tokens?'''							, '''🔥''' if success else '''💩'''						)
					if not success:
										raise Exception('''Tokenization does not match!'''						)

					with torch.no_grad():
										if is_folding_model:
															# Let's test the model in parts
															# ESMFold always converts the ESM stem to float16, which requires float16 ops
															# that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
															# ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
															# original and the converted model on the GPU at the same time.
															lowerCamelCase__		:   int  				=					esm.cuda().infer([row[1] for row in sample_data]						)
															lowerCamelCase__		:   List[str]  				=					model.cuda()(
															    input_ids=hf_tokens['''input_ids'''].cuda()							, attention_mask=hf_tokens['''attention_mask'''].cuda()						)
										else:
															lowerCamelCase__		:   Dict  				=					model(**_lowercase							, output_hidden_states=_lowercase						)
															lowerCamelCase__		:   Union[str, Any]  				=					our_output['''logits''']
															if classification_head:
																				lowerCamelCase__		:   Tuple  				=					esm.model.classification_heads['''mnli'''](esm.extract_features(_lowercase						)						)
															else:
																				lowerCamelCase__		:   Optional[Any]  				=					esm(hf_tokens['''input_ids''']							, repr_layers=list(range(999						)						)						)
																				lowerCamelCase__		:   Union[str, Any]  				=					their_output['''logits''']

										if is_folding_model:
															lowerCamelCase__		:   int  				=					torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']						)						).item()
															lowerCamelCase__		:   Any  				=					torch.allclose(our_output['''positions''']							, their_output['''positions''']							, atol=1e-5						)
										else:
															lowerCamelCase__		:   str  				=					torch.max(torch.abs(our_output - their_output						)						).item()
															lowerCamelCase__		:   Any  				=					torch.allclose(_lowercase							, _lowercase							, atol=1e-5						)

										print(f"""max_absolute_diff = {max_absolute_diff}"""						)  # ~ 1e-5
										print('''Do both models output the same tensors?'''							, '''🔥''' if success else '''💩'''						)

										if not success:
															raise Exception('''Something went wRoNg'''						)

										if not is_folding_model:
															# Let's check contact prediction too
															lowerCamelCase__		:   Tuple  				=					model.predict_contacts(hf_tokens['''input_ids''']							, hf_tokens['''attention_mask''']						)
															lowerCamelCase__		:   int  				=					esm.predict_contacts(hf_tokens['''input_ids''']						)
															lowerCamelCase__		:   Union[str, Any]  				=					torch.max(torch.abs(our_output - their_output						)						).item()
															lowerCamelCase__		:   Optional[int]  				=					torch.allclose(_lowercase							, _lowercase							, atol=1e-5						)

															print('''Contact prediction testing:'''						)
															print(f"""max_absolute_diff = {max_absolute_diff}"""						)  # ~ 1e-5
															print('''Do both models output the same tensors?'''							, '''🔥''' if success else '''💩'''						)

															if not success:
																				raise Exception('''Something went wRoNg'''						)

										pathlib.Path(_lowercase						).mkdir(parents=_lowercase							, exist_ok=_lowercase						)
										print(f"""Saving model to {pytorch_dump_folder_path}"""						)
										model.save_pretrained(_lowercase						)

										del esm  # Free up some memory before continuing

					print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""						)
					hf_tokenizer.save_pretrained(_lowercase						)

					if push_to_repo:
										model.push_to_hub(repo_id=_lowercase							, use_auth_token=_lowercase						)
										hf_tokenizer.push_to_hub(repo_id=_lowercase							, use_auth_token=_lowercase						)


if __name__ == "__main__":
					UpperCAmelCase       :    Tuple									=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--pytorch_dump_folder_path", type=str, required=True, help="Path to the output PyTorch model."
					)
					parser.add_argument(
					    "--classification_head", action="store_true", help="Whether to convert a final classification head."
					)
					parser.add_argument("--model", default=None, type=str, required=True, help="Name of model to convert.")
					parser.add_argument("--push_to_repo", type=str, help="Repo to upload to (including username!).")
					parser.add_argument("--auth_token", type=str, help="HuggingFace auth token.")
					UpperCAmelCase       :    int									=			parser.parse_args()
					convert_esm_checkpoint_to_pytorch(
					    args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
					)







"""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)






