






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__						:      List[Any]								=					logging.get_logger(__name__)

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

UpperCamelCase__						:      Tuple								=					{
    """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__						:      Any								=					list("""ARNDCQEGHILKMFPSTWYV""")

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





def   SCREAMING_SNAKE_CASE__						(   )   ->  Tuple:


 """simple docstring"""



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





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  List[str]:


 """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   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_,  snake_case_,  snake_case_  )   ->  Union[str, Any]:


 """simple docstring"""



 if model.startswith('''esmfold'''  ):
  a		=			MODEL_MAPPING[model]()
 else:
  a	, 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(snake_case_,  snake_case_  ) and key != "trunk":
    setattr(snake_case_,  snake_case_,  snake_case_  )
  for key, val in esm.cfg.trunk.items():
   if hasattr(esmfold_config.trunk,  snake_case_  ) and key != "structure_module":
    setattr(esmfold_config.trunk,  snake_case_,  snake_case_  )
  for key, val in esm.cfg.trunk.structure_module.items():
   if hasattr(esmfold_config.trunk.structure_module,  snake_case_  ):
    setattr(esmfold_config.trunk.structure_module,  snake_case_,  snake_case_  )
 elif hasattr(snake_case_,  '''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=snake_case_,  hidden_size=snake_case_,  num_hidden_layers=snake_case_,  num_attention_heads=snake_case_,  intermediate_size=snake_case_,  max_position_embeddings=1_0_2_6,  layer_norm_eps=1e-5,  attention_probs_dropout_prob=0.0,  hidden_dropout_prob=0.0,  pad_token_id=snake_case_,  emb_layer_norm_before=snake_case_,  token_dropout=snake_case_,  position_embedding_type=snake_case_,  is_folding_model=snake_case_,  esmfold_config=snake_case_,  vocab_list=snake_case_,  )
 if classification_head:
  a		=			esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
 print('''Our ESM config:''',  snake_case_  )

 if model.startswith('''esmfold'''  ):
  a		=			EsmForProteinFolding
 elif classification_head:
  a		=			EsmForSequenceClassification
 else:
  a		=			EsmForMaskedLM
 a		=			model_class(snake_case_  )
 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''',  snake_case_  ) 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=snake_case_,  add_special_tokens=snake_case_  )
  a	, a	, a	, 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	, a	, a		=			batch_converter(snake_case_  )
  # Prepare tokenizer and make sure it matches
  with TemporaryDirectory() as tempdir:
   a		=			'''\n'''.join(alphabet.all_toks  )
   a		=			Path(snake_case_  ) / '''vocab.txt'''
   vocab_file.write_text(snake_case_  )
   a		=			EsmTokenizer(vocab_file=str(snake_case_  )  )

  a		=			hf_tokenizer([row[1] for row in sample_data],  return_tensors='''pt''',  padding=snake_case_  )
  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(**snake_case_,  output_hidden_states=snake_case_  )
   a		=			our_output['''logits''']
   if classification_head:
    a		=			esm.model.classification_heads['''mnli'''](esm.extract_features(snake_case_  )  )
   else:
    a		=			esm(hf_tokens['''input_ids'''],  repr_layers=list(range(9_9_9  )  )  )
    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(snake_case_,  snake_case_,  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(snake_case_,  snake_case_,  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(snake_case_  ).mkdir(parents=snake_case_,  exist_ok=snake_case_  )
  print(f"""Saving model to {pytorch_dump_folder_path}"""  )
  model.save_pretrained(snake_case_  )

  del esm  # Free up some memory before continuing

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

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


if __name__ == "__main__":
    UpperCamelCase__						:      int								=					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__						:      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
    )













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__						:      int								=					{
    """configuration_fsmt""": ["""FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FSMTConfig"""],
    """tokenization_fsmt""": ["""FSMTTokenizer"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						:      Any								=					["""FSMTForConditionalGeneration""", """FSMTModel""", """PretrainedFSMTModel"""]


if TYPE_CHECKING:
				from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
				from .tokenization_fsmt import FSMTTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
				import sys

				UpperCamelCase__						:      List[Any]								=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





