





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	:str  		=     logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE	:int  		=     {
    '''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	:List[Any]  		=     list('''ARNDCQEGHILKMFPSTWYV''')

SCREAMING_SNAKE_CASE	:Any  		=     restypes + ['''X''']
SCREAMING_SNAKE_CASE	:Optional[int]  		=     restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']



def 			_lowerCAmelCase						(							)->Any:
   '''simple docstring'''






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



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Dict		,						lowerCAmelCase_							:List[Any]							)->Optional[Any]:
   '''simple docstring'''






   snake_case_					    =					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_							:str		,						lowerCAmelCase_							:List[str]		,						lowerCAmelCase_							:Dict		,						lowerCAmelCase_							:Dict		,						lowerCAmelCase_							:Any							)->str:
   '''simple docstring'''






   if model.startswith("esmfold"							):
      snake_case_					    =					MODEL_MAPPING[model]()
   else:
      snake_case_					    =					MODEL_MAPPING[model]()
   esm.eval()  # disable dropout

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

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

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

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

   if model.startswith("esmfold"							):
      snake_case_					    =					EsmForProteinFolding
   elif classification_head:
      snake_case_					    =					EsmForSequenceClassification
   else:
      snake_case_					    =					EsmForMaskedLM
   snake_case_					    =					model_class(SCREAMING_SNAKE_CASE__							)
   model.eval()

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

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

   snake_case_					    =					original_esm_model.emb_layer_norm_after.weight
   snake_case_					    =					original_esm_model.emb_layer_norm_after.bias

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

      # self attention
      snake_case_					    =					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_					    =					esm_layer.self_attn.q_proj.weight
      snake_case_					    =					esm_layer.self_attn.q_proj.bias
      snake_case_					    =					esm_layer.self_attn.k_proj.weight
      snake_case_					    =					esm_layer.self_attn.k_proj.bias
      snake_case_					    =					esm_layer.self_attn.v_proj.weight
      snake_case_					    =					esm_layer.self_attn.v_proj.bias

      if getattr(esm_layer.self_attn		,						"rot_emb"		,						SCREAMING_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.
         snake_case_					    =					esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

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

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

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

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

      del esm  # Free up some memory before continuing

   print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''							)
   hf_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE	:str  		=     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	:List[Any]  		=     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 List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  def __init__(     self       :       List[str]    ,				_lowerCAmelCase       :       Optional[Any]    ,				_lowerCAmelCase       :       Tuple  )   -> str:




     """simple docstring"""
     super().__init__()
     self.register_modules(unet=_lowerCAmelCase    ,				scheduler=_lowerCAmelCase  )



  @torch.no_grad()
  def __call__(     self       :       Tuple    ,				_lowerCAmelCase       :       int = 1    ,				_lowerCAmelCase       :       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,				_lowerCAmelCase       :       int = 1_0_0_0    ,				_lowerCAmelCase       :       Optional[str] = "pil"    ,				_lowerCAmelCase       :       bool = True    ,				)   -> Union[ImagePipelineOutput, Tuple]:




     """simple docstring"""
     # Sample gaussian noise to begin loop
     if isinstance(self.unet.config.sample_size    ,				_lowerCAmelCase  ):
        snake_case_					    =					(
            batch_size,
            self.unet.config.in_channels,
            self.unet.config.sample_size,
            self.unet.config.sample_size,
        )
     else:
        snake_case_					    =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if self.device.type == "mps":
        # randn does not work reproducibly on mps
        snake_case_					    =					randn_tensor(_lowerCAmelCase    ,				generator=_lowerCAmelCase  )
        snake_case_					    =					image.to(self.device  )
     else:
        snake_case_					    =					randn_tensor(_lowerCAmelCase    ,				generator=_lowerCAmelCase    ,				device=self.device  )

     # set step values
     self.scheduler.set_timesteps(_lowerCAmelCase  )

     for t in self.progress_bar(self.scheduler.timesteps  ):
        # 1. predict noise model_output
        snake_case_					    =					self.unet(_lowerCAmelCase    ,				_lowerCAmelCase  ).sample

        # 2. compute previous image: x_t -> x_t-1
        snake_case_					    =					self.scheduler.step(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    ,				generator=_lowerCAmelCase  ).prev_sample

     snake_case_					    =					(image / 2 + 0.5).clamp(0    ,				1  )
     snake_case_					    =					image.cpu().permute(0    ,				2    ,				3    ,				1  ).numpy()
     if output_type == "pil":
        snake_case_					    =					self.numpy_to_pil(_lowerCAmelCase  )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowerCAmelCase  )




