





'''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()
SCREAMING_SNAKE_CASE_      :			Optional[Any]		  =      logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE_      :			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,
}

SCREAMING_SNAKE_CASE_      :			int		  =      list('''ARNDCQEGHILKMFPSTWYV''')

SCREAMING_SNAKE_CASE_      :			Any		  =      restypes + ["X"]
SCREAMING_SNAKE_CASE_      :			List[Any]		  =      restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]



def    SCREAMING_SNAKE_CASE							(			)		->						Optional[Any]:






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



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Optional[Any]:
		__lowercase					=    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    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						Dict:






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

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

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

		if is_folding_model:
				__lowercase					=    esm.esm
		else:
				__lowercase					=    esm

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

		if model.startswith('esmfold'       ):
				__lowercase					=    EsmForProteinFolding
		elif classification_head:
				__lowercase					=    EsmForSequenceClassification
		else:
				__lowercase					=    EsmForMaskedLM
		__lowercase					=    model_class(UpperCamelCase_       )
		model.eval()

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

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

		__lowercase					=    original_esm_model.emb_layer_norm_after.weight
		__lowercase					=    original_esm_model.emb_layer_norm_after.bias

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

				# self attention
				__lowercase					=    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)       )
				)

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

				if getattr(esm_layer.self_attn	,      'rot_emb'	,      UpperCamelCase_       ) 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.
						__lowercase					=    esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

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

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

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

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

				del esm  # Free up some memory before continuing

		print(F"Saving tokenizer to {pytorch_dump_folder_path}"       )
		hf_tokenizer.save_pretrained(UpperCamelCase_       )

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			Dict		  =      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.''')
		SCREAMING_SNAKE_CASE_      :			Dict		  =      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    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    str(snake_case       ).strip()
		if not bin_string:
				raise ValueError('Empty string was passed to the function'       )
		__lowercase					=    bin_string[0] == '-'
		if is_negative:
				__lowercase					=    bin_string[1:]
		if not all(char in '01' for char in bin_string       ):
				raise ValueError('Non-binary value was passed to the function'       )
		__lowercase					=    0
		for char in bin_string:
				__lowercase					=    2 * decimal_number + int(snake_case       )
		return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
		from doctest import testmod

		testmod()




