



'''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()
UpperCamelCase__    :     str			  =   logging.get_logger(__name__)

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

UpperCamelCase__    :     Optional[Any]			  =   {
    '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__    :     Union[str, Any]			  =   list('ARNDCQEGHILKMFPSTWYV')

UpperCamelCase__    :     int			  =   restypes + ['X']
UpperCamelCase__    :     Union[str, Any]			  =   restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']



def     UpperCAmelCase     (    )   ->       Tuple:



   """simple docstring"""






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



def     UpperCAmelCase     (    a_      ,		a_		)   ->       Dict:



   """simple docstring"""






   A_  :		Tuple						=					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     UpperCAmelCase     (    a_      ,		a_      ,		a_      ,		a_      ,		a_		)   ->       Optional[Any]:



   """simple docstring"""






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

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

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

   if is_folding_model:
      A_  :		int						=					esm.esm
   else:
      A_  :		Dict						=					esm

   A_  :		Optional[int]						=					EsmConfig(
       vocab_size=original_esm_model.embed_tokens.num_embeddings      ,		mask_token_id=a_      ,		hidden_size=a_      ,		num_hidden_layers=a_      ,		num_attention_heads=a_      ,		intermediate_size=a_      ,		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=a_      ,		emb_layer_norm_before=a_      ,		token_dropout=a_      ,		position_embedding_type=a_      ,		is_folding_model=a_      ,		esmfold_config=a_      ,		vocab_list=a_      ,		)
   if classification_head:
      A_  :		Dict						=					esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
   print("""Our ESM config:"""      ,		a_		)

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

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

   if config.emb_layer_norm_before:
      A_  :		List[str]						=					original_esm_model.emb_layer_norm_before.weight
      A_  :		List[Any]						=					original_esm_model.emb_layer_norm_before.bias

   A_  :		Optional[Any]						=					original_esm_model.emb_layer_norm_after.weight
   A_  :		Optional[int]						=					original_esm_model.emb_layer_norm_after.bias

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

      # self attention
      A_  :		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)		)
      )

      A_  :		Any						=					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_  :		int						=					esm_layer.self_attn.k_proj.bias
      A_  :		Union[str, Any]						=					esm_layer.self_attn.v_proj.weight
      A_  :		List[Any]						=					esm_layer.self_attn.v_proj.bias

      if getattr(esm_layer.self_attn      ,		"""rot_emb"""      ,		a_		) 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_  :		Dict						=					esm_layer.self_attn.rot_emb.inv_freq

      # LayerNorm changes for pre-activation
      A_  :		Optional[Any]						=					esm_layer.self_attn_layer_norm.weight
      A_  :		Tuple						=					esm_layer.self_attn_layer_norm.bias
      A_  :		Any						=					esm_layer.final_layer_norm.weight
      A_  :		List[str]						=					esm_layer.final_layer_norm.bias

      # self-attention output
      A_  :		EsmSelfOutput						=					layer.attention.output
      assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
      A_  :		Dict						=					esm_layer.self_attn.out_proj.weight
      A_  :		Union[str, Any]						=					esm_layer.self_attn.out_proj.bias

      # intermediate
      A_  :		EsmIntermediate						=					layer.intermediate
      assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
      A_  :		List[Any]						=					esm_layer.fca.weight
      A_  :		Any						=					esm_layer.fca.bias

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

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

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

      A_  :		Any						=					hf_tokenizer([row[1] for row in sample_data]      ,		return_tensors="""pt"""      ,		padding=a_		)
      A_  :		str						=					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_  :		Optional[Any]						=					esm.cuda().infer([row[1] for row in sample_data]		)
         A_  :		List[Any]						=					model.cuda()(
             input_ids=hf_tokens["""input_ids"""].cuda()      ,		attention_mask=hf_tokens["""attention_mask"""].cuda()		)
      else:
         A_  :		Dict						=					model(**a_      ,		output_hidden_states=a_		)
         A_  :		Optional[Any]						=					our_output["""logits"""]
         if classification_head:
            A_  :		Any						=					esm.model.classification_heads["""mnli"""](esm.extract_features(a_		)		)
         else:
            A_  :		List[str]						=					esm(hf_tokens["""input_ids"""]      ,		repr_layers=list(range(9_9_9		)		)		)
            A_  :		int						=					their_output["""logits"""]

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

      del esm  # Free up some memory before continuing

   print(F"Saving tokenizer to {pytorch_dump_folder_path}"		)
   hf_tokenizer.save_pretrained(a_		)

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


if __name__ == "__main__":
  UpperCamelCase__    :     List[Any]			  =   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
  )





'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
