



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							=       logging.get_logger(__name__)

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

UpperCAmelCase							=       {
    '''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							=       list("""ARNDCQEGHILKMFPSTWYV""")

UpperCAmelCase							=       restypes + ['''X''']
UpperCAmelCase							=       restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']

def       A_   (						):

				"""simple docstring"""
				with TemporaryDirectory() as tempdir:
								a__					   =		'\n'.join(A__		)
								a__					   =		Path(A__		) / 'vocab.txt'
								vocab_file.write_text(A__		)
								a__					   =		EsmTokenizer(vocab_file=str(A__		)		)
				a__					   =		0  # Overlaps with 'A' but that seems to be what they want
				return hf_tokenizer

def       A_   (						__a  :					Any			,		__a  :					Optional[int]		):

				"""simple docstring"""
				a__					   =		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_   (						__a  :					Union[str, Any]			,		__a  :					List[Any]			,		__a  :					Union[str, Any]			,		__a  :					Union[str, Any]			,		__a  :					str		):

				"""simple docstring"""
				if model.startswith("""esmfold"""		):
								a__					   =		MODEL_MAPPING[model]()
				else:
								a__					   =		MODEL_MAPPING[model]()
				esm.eval()  # disable dropout

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

				if is_folding_model:
								a__					   =		esm.esm.alphabet
				a__					   =		tuple(alphabet.all_toks		)
				a__					   =		alphabet.mask_idx
				a__					   =		alphabet.padding_idx

				if is_folding_model:
								a__					   =		esm.esm
				else:
								a__					   =		esm

				a__					   =		EsmConfig(
				    vocab_size=original_esm_model.embed_tokens.num_embeddings			,		mask_token_id=A__			,		hidden_size=A__			,		num_hidden_layers=A__			,		num_attention_heads=A__			,		intermediate_size=A__			,		max_position_embeddings=1_026			,		layer_norm_eps=1e-5			,		attention_probs_dropout_prob=0.0			,		hidden_dropout_prob=0.0			,		pad_token_id=A__			,		emb_layer_norm_before=A__			,		token_dropout=A__			,		position_embedding_type=A__			,		is_folding_model=A__			,		esmfold_config=A__			,		vocab_list=A__			,		)
				if classification_head:
								a__					   =		esm.classification_heads['mnli'].out_proj.weight.shape[0]
				print("""Our ESM config:"""			,		A__		)

				if model.startswith("""esmfold"""		):
								a__					   =		EsmForProteinFolding
				elif classification_head:
								a__					   =		EsmForSequenceClassification
				else:
								a__					   =		EsmForMaskedLM
				a__					   =		model_class(A__		)
				model.eval()

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

				if config.emb_layer_norm_before:
								a__					   =		original_esm_model.emb_layer_norm_before.weight
								a__					   =		original_esm_model.emb_layer_norm_before.bias

				a__					   =		original_esm_model.emb_layer_norm_after.weight
				a__					   =		original_esm_model.emb_layer_norm_after.bias

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

								# self attention
								a__					   =		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)		)
								)

								a__					   =		esm_layer.self_attn.q_proj.weight
								a__					   =		esm_layer.self_attn.q_proj.bias
								a__					   =		esm_layer.self_attn.k_proj.weight
								a__					   =		esm_layer.self_attn.k_proj.bias
								a__					   =		esm_layer.self_attn.v_proj.weight
								a__					   =		esm_layer.self_attn.v_proj.bias

								if getattr(esm_layer.self_attn			,		"""rot_emb"""			,		A__		) 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.
												a__					   =		esm_layer.self_attn.rot_emb.inv_freq

								# LayerNorm changes for pre-activation
								a__					   =		esm_layer.self_attn_layer_norm.weight
								a__					   =		esm_layer.self_attn_layer_norm.bias
								a__					   =		esm_layer.final_layer_norm.weight
								a__					   =		esm_layer.final_layer_norm.bias

								# self-attention output
								a__					   =		layer.attention.output
								assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
								a__					   =		esm_layer.self_attn.out_proj.weight
								a__					   =		esm_layer.self_attn.out_proj.bias

								# intermediate
								a__					   =		layer.intermediate
								assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
								a__					   =		esm_layer.fca.weight
								a__					   =		esm_layer.fca.bias

								# output
								a__					   =		layer.output
								assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
								a__					   =		esm_layer.fca.weight
								a__					   =		esm_layer.fca.bias
								# end of layer

				if is_folding_model:
								a__					   =		esm.esm_s_combine.data
								a__					   =		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:
								a__					   =		esm.esm.classification_heads['mnli'].dense.weight
								a__					   =		esm.classification_heads['mnli'].dense.bias
								a__					   =		esm.classification_heads['mnli'].out_proj.weight
								a__					   =		esm.classification_heads['mnli'].out_proj.bias
				else:
								# LM Head
								a__					   =		esm.lm_head.dense.weight
								a__					   =		esm.lm_head.dense.bias
								a__					   =		esm.lm_head.layer_norm.weight
								a__					   =		esm.lm_head.layer_norm.bias
								a__					   =		esm.lm_head.weight
								a__					   =		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.
								a__					   =		SAMPLE_DATA[:2]
				else:
								a__					   =		SAMPLE_DATA

				if is_folding_model:
								a__					   =		get_esmfold_tokenizer()
								a__					   =		hf_tokenizer(
								    [row[1] for row in sample_data]			,		return_tensors="""pt"""			,		padding=A__			,		add_special_tokens=A__		)
								a__					   =		esmfold_encode_sequences([row[1] for row in sample_data]		)
								a__					   =		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.
								a__					   =		alphabet.get_batch_converter()
								a__					   =		batch_converter(A__		)
								# Prepare tokenizer and make sure it matches
								with TemporaryDirectory() as tempdir:
												a__					   =		'\n'.join(alphabet.all_toks		)
												a__					   =		Path(A__		) / 'vocab.txt'
												vocab_file.write_text(A__		)
												a__					   =		EsmTokenizer(vocab_file=str(A__		)		)

								a__					   =		hf_tokenizer([row[1] for row in sample_data]			,		return_tensors="""pt"""			,		padding=A__		)
								a__					   =		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.
												a__					   =		esm.cuda().infer([row[1] for row in sample_data]		)
												a__					   =		model.cuda()(
												    input_ids=hf_tokens["""input_ids"""].cuda()			,		attention_mask=hf_tokens["""attention_mask"""].cuda()		)
								else:
												a__					   =		model(**A__			,		output_hidden_states=A__		)
												a__					   =		our_output['logits']
												if classification_head:
																a__					   =		esm.model.classification_heads['mnli'](esm.extract_features(A__		)		)
												else:
																a__					   =		esm(hf_tokens["""input_ids"""]			,		repr_layers=list(range(999		)		)		)
																a__					   =		their_output['logits']

								if is_folding_model:
												a__					   =		torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]		)		).item()
												a__					   =		torch.allclose(our_output["""positions"""]			,		their_output["""positions"""]			,		atol=1e-5		)
								else:
												a__					   =		torch.max(torch.abs(our_output - their_output		)		).item()
												a__					   =		torch.allclose(A__			,		A__			,		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
												a__					   =		model.predict_contacts(hf_tokens["""input_ids"""]			,		hf_tokens["""attention_mask"""]		)
												a__					   =		esm.predict_contacts(hf_tokens["""input_ids"""]		)
												a__					   =		torch.max(torch.abs(our_output - their_output		)		).item()
												a__					   =		torch.allclose(A__			,		A__			,		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(A__		).mkdir(parents=A__			,		exist_ok=A__		)
								print(F'''Saving model to {pytorch_dump_folder_path}'''		)
								model.save_pretrained(A__		)

								del esm  # Free up some memory before continuing

				print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''		)
				hf_tokenizer.save_pretrained(A__		)

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


if __name__ == "__main__":
	UpperCAmelCase							=       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							=       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
	)


def 					UpperCamelCase__		(							A__     ,					A__       )  ->   Any:
				if len(str(A__       )       ) == 1 or len(str(A__       )       ) == 1:
								return a * b

				snake_case__   :      Union[str, Any]   				= max(len(str(A__       )       )     ,					len(str(A__       )       )       )
				snake_case__   :      str   				= ma // 2

				snake_case__				,    snake_case__   :      Tuple   				= divmod(A__     ,					10**ma       )
				snake_case__				,    snake_case__   :      str   				= divmod(A__     ,					10**ma       )

				snake_case__   :      Optional[int]   				= karatsuba(A__     ,					A__       )
				snake_case__   :      List[Any]   				= karatsuba((aa + aa)     ,					(ba + ba)       )
				snake_case__   :      Tuple   				= karatsuba(A__     ,					A__       )

				return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)
def 					UpperCamelCase__		(							)  ->   Optional[int]:
				print(karatsuba(1_5463     ,					2_3489       )       )


if __name__ == "__main__":
				main()


