





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_		(       )	->		Union[str, Any]:






				"""simple docstring"""


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






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Any:






				"""simple docstring"""


				snake_case_		:					Dict			    =						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_		(       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				)	->		List[str]:






				"""simple docstring"""


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

				if model.startswith('''esmfold'''				):
								snake_case_		:					List[str]			    =						esm.esm.embed_dim
								snake_case_		:					Any			    =						esm.esm.num_layers
								snake_case_		:					Optional[Any]			    =						esm.esm.attention_heads
								snake_case_		:					int			    =						4 * embed_dim
								snake_case_		:					Any			    =						esm.esm.token_dropout
								snake_case_		:					List[Any]			    =						False  # This code path does not exist in ESM-2
								snake_case_		:					Optional[int]			    =						'''rotary'''
								snake_case_		:					Dict			    =						True
								snake_case_		:					Dict			    =						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
								snake_case_		:					Union[str, Any]			    =						esm.args.embed_dim
								snake_case_		:					List[Any]			    =						esm.args.layers
								snake_case_		:					Dict			    =						esm.args.attention_heads
								snake_case_		:					Tuple			    =						esm.args.ffn_embed_dim
								snake_case_		:					Any			    =						esm.args.token_dropout
								snake_case_		:					Optional[int]			    =						True if esm.emb_layer_norm_before else False
								snake_case_		:					int			    =						'''absolute'''
								snake_case_		:					Tuple			    =						False
								snake_case_		:					List[Any]			    =						None
				else:
								# Indicates an ESM-2 model
								snake_case_		:					str			    =						esm.embed_dim
								snake_case_		:					Optional[int]			    =						esm.num_layers
								snake_case_		:					Union[str, Any]			    =						esm.attention_heads
								snake_case_		:					Optional[int]			    =						4 * embed_dim  # This is hardcoded in ESM-2
								snake_case_		:					int			    =						esm.token_dropout
								snake_case_		:					Optional[Any]			    =						False  # This code path does not exist in ESM-2
								snake_case_		:					Dict			    =						'''rotary'''
								snake_case_		:					str			    =						False
								snake_case_		:					Union[str, Any]			    =						None

				if is_folding_model:
								snake_case_		:					Tuple			    =						esm.esm.alphabet
				snake_case_		:					Dict			    =						tuple(alphabet.all_toks				)
				snake_case_		:					List[str]			    =						alphabet.mask_idx
				snake_case_		:					Union[str, Any]			    =						alphabet.padding_idx

				if is_folding_model:
								snake_case_		:					Any			    =						esm.esm
				else:
								snake_case_		:					int			    =						esm

				snake_case_		:					int			    =						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:
								snake_case_		:					Dict			    =						esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our ESM config:'''				,       _UpperCamelCase				)

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

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

				if config.emb_layer_norm_before:
								snake_case_		:					List[Any]			    =						original_esm_model.emb_layer_norm_before.weight
								snake_case_		:					Tuple			    =						original_esm_model.emb_layer_norm_before.bias

				snake_case_		:					Any			    =						original_esm_model.emb_layer_norm_after.weight
				snake_case_		:					Dict			    =						original_esm_model.emb_layer_norm_after.bias

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

								# self attention
								snake_case_		:					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)				)
								)

								snake_case_		:					str			    =						esm_layer.self_attn.q_proj.weight
								snake_case_		:					Optional[Any]			    =						esm_layer.self_attn.q_proj.bias
								snake_case_		:					List[str]			    =						esm_layer.self_attn.k_proj.weight
								snake_case_		:					Any			    =						esm_layer.self_attn.k_proj.bias
								snake_case_		:					Optional[int]			    =						esm_layer.self_attn.v_proj.weight
								snake_case_		:					Optional[Any]			    =						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.
												snake_case_		:					Optional[Any]			    =						esm_layer.self_attn.rot_emb.inv_freq

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

								# self-attention output
								snake_case_		:					EsmSelfOutput			    =						layer.attention.output
								assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
								snake_case_		:					int			    =						esm_layer.self_attn.out_proj.weight
								snake_case_		:					Optional[Any]			    =						esm_layer.self_attn.out_proj.bias

								# intermediate
								snake_case_		:					EsmIntermediate			    =						layer.intermediate
								assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
								snake_case_		:					List[str]			    =						esm_layer.fca.weight
								snake_case_		:					Dict			    =						esm_layer.fca.bias

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

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

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

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

								if is_folding_model:
												snake_case_		:					List[str]			    =						torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']				)				).item()
												snake_case_		:					Optional[Any]			    =						torch.allclose(our_output['''positions''']				,       their_output['''positions''']				,       atol=1E-5				)
								else:
												snake_case_		:					List[str]			    =						torch.max(torch.abs(our_output - their_output				)				).item()
												snake_case_		:					int			    =						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
												snake_case_		:					int			    =						model.predict_contacts(hf_tokens['''input_ids''']				,       hf_tokens['''attention_mask''']				)
												snake_case_		:					List[str]			    =						esm.predict_contacts(hf_tokens['''input_ids''']				)
												snake_case_		:					List[Any]			    =						torch.max(torch.abs(our_output - their_output				)				).item()
												snake_case_		:					Tuple			    =						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__":
	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
	)







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowerCAmelCase_												=       logging.get_logger(__name__)



class       __lowerCAmelCase (    _a  ):






			def __init__(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     None:


							'''simple docstring'''



							warnings.warn(
							    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
							    ''' Please use MobileNetV2ImageProcessor instead.'''      ,							__magic_name__      ,							)
							super().__init__(*__magic_name__      ,							**__magic_name__					)
