


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


def 					a__    (				UpperCamelCase_	:			Dict,      UpperCamelCase_	:			Union[str, Any]	):
   UpperCAmelCase__  :Optional[int]            =			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 					a__    (				UpperCamelCase_	:			str,      UpperCamelCase_	:			str,      UpperCamelCase_	:			bool,      UpperCamelCase_	:			str,      UpperCamelCase_	:			str	):
   if model.startswith('''esmfold'''	):
      UpperCAmelCase__  :Optional[Any]            =			MODEL_MAPPING[model]()
   else:
      UpperCAmelCase__  :List[Any]            =			MODEL_MAPPING[model]()
   esm.eval()  # disable dropout

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

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

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

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

   if model.startswith('''esmfold'''	):
      UpperCAmelCase__  :int            =			EsmForProteinFolding
   elif classification_head:
      UpperCAmelCase__  :str            =			EsmForSequenceClassification
   else:
      UpperCAmelCase__  :int            =			EsmForMaskedLM
   UpperCAmelCase__  :Any            =			model_class(UpperCamelCase_	)
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   UpperCAmelCase__  :Optional[Any]            =			original_esm_model.embed_tokens.weight
   if position_embedding_type == "absolute":
      UpperCAmelCase__  :Optional[int]            =			original_esm_model.embed_positions.weight

   if config.emb_layer_norm_before:
      UpperCAmelCase__  :Optional[Any]            =			original_esm_model.emb_layer_norm_before.weight
      UpperCAmelCase__  :int            =			original_esm_model.emb_layer_norm_before.bias

   UpperCAmelCase__  :int            =			original_esm_model.emb_layer_norm_after.weight
   UpperCAmelCase__  :str            =			original_esm_model.emb_layer_norm_after.bias

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

      # self attention
      UpperCAmelCase__  :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)	)
      )

      UpperCAmelCase__  :Union[str, Any]            =			esm_layer.self_attn.q_proj.weight
      UpperCAmelCase__  :List[str]            =			esm_layer.self_attn.q_proj.bias
      UpperCAmelCase__  :str            =			esm_layer.self_attn.k_proj.weight
      UpperCAmelCase__  :List[str]            =			esm_layer.self_attn.k_proj.bias
      UpperCAmelCase__  :int            =			esm_layer.self_attn.v_proj.weight
      UpperCAmelCase__  :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.
         UpperCAmelCase__  :Any            =			esm_layer.self_attn.rot_emb.inv_freq

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

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

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

      # output
      UpperCAmelCase__  :EsmOutput            =			layer.output
      assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
      UpperCAmelCase__  :Tuple            =			esm_layer.fca.weight
      UpperCAmelCase__  :Optional[int]            =			esm_layer.fca.bias
      # end of layer

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

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

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

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







'''simple docstring'''





def 					a__    (				UpperCamelCase_	:			int = 100	):
   UpperCAmelCase__  :List[str]            =			1
   UpperCAmelCase__  :Dict            =			0
   for i in range(1,      num + 1	):
      fact *= i

   for j in str(UpperCamelCase_	):
      result += int(UpperCamelCase_	)

   return result


if __name__ == "__main__":
    print(solution(int(input('''Enter the Number: ''').strip())))



