




"""simple docstring"""





import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    """--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"""
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/








"""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()
_lowercase	      =    logging.get_logger(__name__)

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

_lowercase	      =    {
    """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,
}

_lowercase	      =    list("""ARNDCQEGHILKMFPSTWYV""")

_lowercase	      =    restypes + ["""X"""]
_lowercase	      =    restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]

def 		lowerCAmelCase							( ):
						'''simple docstring'''


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

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					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 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


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

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

						if is_folding_model:
												snake_case__       :					int										=					esm.esm.alphabet
						snake_case__       :					Tuple										=					tuple(alphabet.all_toks		)
						snake_case__       :					List[str]										=					alphabet.mask_idx
						snake_case__       :					str										=					alphabet.padding_idx

						if is_folding_model:
												snake_case__       :					Any										=					esm.esm
						else:
												snake_case__       :					List[str]										=					esm

						snake_case__       :					List[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:
												snake_case__       :					Any										=					esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
						print('''Our ESM config:'''      ,			lowerCamelCase		)

						if model.startswith('''esmfold'''		):
												snake_case__       :					List[str]										=					EsmForProteinFolding
						elif classification_head:
												snake_case__       :					str										=					EsmForSequenceClassification
						else:
												snake_case__       :					Dict										=					EsmForMaskedLM
						snake_case__       :					str										=					model_class(lowerCamelCase		)
						model.eval()

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

						if config.emb_layer_norm_before:
												snake_case__       :					Union[str, Any]										=					original_esm_model.emb_layer_norm_before.weight
												snake_case__       :					Union[str, Any]										=					original_esm_model.emb_layer_norm_before.bias

						snake_case__       :					str										=					original_esm_model.emb_layer_norm_after.weight
						snake_case__       :					Optional[int]										=					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__       :					Any										=					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__       :					Union[str, Any]										=					esm_layer.self_attn.q_proj.weight
												snake_case__       :					int										=					esm_layer.self_attn.q_proj.bias
												snake_case__       :					Tuple										=					esm_layer.self_attn.k_proj.weight
												snake_case__       :					Union[str, Any]										=					esm_layer.self_attn.k_proj.bias
												snake_case__       :					List[str]										=					esm_layer.self_attn.v_proj.weight
												snake_case__       :					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.
																		snake_case__       :					Optional[Any]										=					esm_layer.self_attn.rot_emb.inv_freq

												# LayerNorm changes for pre-activation
												snake_case__       :					int										=					esm_layer.self_attn_layer_norm.weight
												snake_case__       :					Tuple										=					esm_layer.self_attn_layer_norm.bias
												snake_case__       :					Union[str, Any]										=					esm_layer.final_layer_norm.weight
												snake_case__       :					Union[str, 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__       :					List[Any]										=					esm_layer.self_attn.out_proj.weight
												snake_case__       :					Dict										=					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__       :					Dict										=					esm_layer.fca.weight
												snake_case__       :					Optional[int]										=					esm_layer.fca.bias

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

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

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

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

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


