



'''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()
__lowerCAmelCase		    =     logging.get_logger(__name__)

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

__lowerCAmelCase		    =     {
    '''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,
}

__lowerCAmelCase		    =     list('''ARNDCQEGHILKMFPSTWYV''')

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



def   __lowerCamelCase  (   )    ->   Any:







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



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Optional[Any]:
		_a  :   int				 =							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   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Tuple:







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

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

		if is_folding_model:
				_a  :   Union[str, Any]				 =							esm.esm.alphabet
		_a  :   Optional[Any]				 =							tuple(alphabet.all_toks						)
		_a  :   Optional[int]				 =							alphabet.mask_idx
		_a  :   List[Any]				 =							alphabet.padding_idx

		if is_folding_model:
				_a  :   Union[str, Any]				 =							esm.esm
		else:
				_a  :   List[str]				 =							esm

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

		if model.startswith('esmfold'						):
				_a  :   int				 =							EsmForProteinFolding
		elif classification_head:
				_a  :   Union[str, Any]				 =							EsmForSequenceClassification
		else:
				_a  :   List[Any]				 =							EsmForMaskedLM
		_a  :   int				 =							model_class(lowerCAmelCase_						)
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		_a  :   int				 =							original_esm_model.embed_tokens.weight
		if position_embedding_type == "absolute":
				_a  :   Union[str, Any]				 =							original_esm_model.embed_positions.weight

		if config.emb_layer_norm_before:
				_a  :   Optional[Any]				 =							original_esm_model.emb_layer_norm_before.weight
				_a  :   int				 =							original_esm_model.emb_layer_norm_before.bias

		_a  :   Union[str, Any]				 =							original_esm_model.emb_layer_norm_after.weight
		_a  :   List[Any]				 =							original_esm_model.emb_layer_norm_after.bias

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

				# self attention
				_a  :   Dict				 =							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  :   str				 =							esm_layer.self_attn.q_proj.weight
				_a  :   Any				 =							esm_layer.self_attn.q_proj.bias
				_a  :   Optional[int]				 =							esm_layer.self_attn.k_proj.weight
				_a  :   List[str]				 =							esm_layer.self_attn.k_proj.bias
				_a  :   Union[str, Any]				 =							esm_layer.self_attn.v_proj.weight
				_a  :   Union[str, Any]				 =							esm_layer.self_attn.v_proj.bias

				if getattr(esm_layer.self_attn		,    'rot_emb'		,    lowerCAmelCase_						) 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  :   int				 =							esm_layer.self_attn.rot_emb.inv_freq

				# LayerNorm changes for pre-activation
				_a  :   int				 =							esm_layer.self_attn_layer_norm.weight
				_a  :   Any				 =							esm_layer.self_attn_layer_norm.bias
				_a  :   Optional[Any]				 =							esm_layer.final_layer_norm.weight
				_a  :   Any				 =							esm_layer.final_layer_norm.bias

				# self-attention output
				_a  :   int				 =							layer.attention.output
				assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
				_a  :   Tuple				 =							esm_layer.self_attn.out_proj.weight
				_a  :   int				 =							esm_layer.self_attn.out_proj.bias

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

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

		if is_folding_model:
				_a  :   Dict				 =							esm.esm_s_combine.data
				_a  :   Any				 =							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  :   Optional[Any]				 =							esm.esm.classification_heads['mnli'].dense.weight
				_a  :   Any				 =							esm.classification_heads['mnli'].dense.bias
				_a  :   List[str]				 =							esm.classification_heads['mnli'].out_proj.weight
				_a  :   List[str]				 =							esm.classification_heads['mnli'].out_proj.bias
		else:
				# LM Head
				_a  :   List[Any]				 =							esm.lm_head.dense.weight
				_a  :   List[str]				 =							esm.lm_head.dense.bias
				_a  :   str				 =							esm.lm_head.layer_norm.weight
				_a  :   str				 =							esm.lm_head.layer_norm.bias
				_a  :   Union[str, Any]				 =							esm.lm_head.weight
				_a  :   List[str]				 =							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  :   Union[str, Any]				 =							SAMPLE_DATA[:2]
		else:
				_a  :   Tuple				 =							SAMPLE_DATA

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

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

				if is_folding_model:
						_a  :   Union[str, Any]				 =							torch.max(torch.abs(our_output['positions'] - their_output['positions']						)						).item()
						_a  :   int				 =							torch.allclose(our_output['positions']		,    their_output['positions']		,    atol=1E-5						)
				else:
						_a  :   int				 =							torch.max(torch.abs(our_output - their_output						)						).item()
						_a  :   str				 =							torch.allclose(lowerCAmelCase_		,    lowerCAmelCase_		,    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  :   Any				 =							model.predict_contacts(hf_tokens['input_ids']		,    hf_tokens['attention_mask']						)
						_a  :   str				 =							esm.predict_contacts(hf_tokens['input_ids']						)
						_a  :   int				 =							torch.max(torch.abs(our_output - their_output						)						).item()
						_a  :   Optional[Any]				 =							torch.allclose(lowerCAmelCase_		,    lowerCAmelCase_		,    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(lowerCAmelCase_						).mkdir(parents=lowerCAmelCase_		,    exist_ok=lowerCAmelCase_						)
				print(f"""Saving model to {pytorch_dump_folder_path}"""						)
				model.save_pretrained(lowerCAmelCase_						)

				del esm  # Free up some memory before continuing

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

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


if __name__ == "__main__":
							__lowerCAmelCase		    =     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.''')
							__lowerCAmelCase		    =     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'''

def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   tuple[int, int]:
		# base case
		if len(lowerCAmelCase_						) == 1:
				return a[0], a[0]
		else:
				# split A into half.
				_a  :   Union[str, Any]				 =							a[: len(lowerCAmelCase_						) // 2]
				_a  :   List[Any]				 =							a[len(lowerCAmelCase_						) // 2 :]

				# 2 sub problems, 1/2 of original size.
				_a ,      _a  :   Any				 =							max_difference(lowerCAmelCase_						)
				_a ,      _a  :   Any				 =							max_difference(lowerCAmelCase_						)

				# get min of first and max of second
				# linear time
				_a  :   Union[str, Any]				 =							min(lowerCAmelCase_						)
				_a  :   Optional[int]				 =							max(lowerCAmelCase_						)

				# 3 cases, either (small1, big1),
				# (min_first, max_second), (small2, big2)
				# constant comparisons
				if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
						return smalla, biga
				elif biga - smalla > max_second - min_first:
						return smalla, biga
				else:
						return min_first, max_second


if __name__ == "__main__":
							import doctest

							doctest.testmod()




