"""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      : Tuple       =logging.get_logger(__name__)

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

lowerCamelCase      : List[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,
}

lowerCamelCase      : int       =list('''ARNDCQEGHILKMFPSTWYV''')

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




def 						_lowercase				(  )    ->     int:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[str]  ,       _SCREAMING_SNAKE_CASE				:				List[str]						)    ->     List[Any]:







     '''simple docstring'''


     __A					:					str								=		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 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Optional[int]  ,       _SCREAMING_SNAKE_CASE				:				Optional[int]  ,       _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				Optional[Any]						)    ->     Optional[Any]:







     '''simple docstring'''


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

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

     if is_folding_model:
          __A					:					List[str]								=		esm.esm.alphabet
     __A					:					List[Any]								=		tuple(alphabet.all_toks						)
     __A					:					str								=		alphabet.mask_idx
     __A					:					Any								=		alphabet.padding_idx

     if is_folding_model:
          __A					:					Tuple								=		esm.esm
     else:
          __A					:					Dict								=		esm

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

     if model.startswith('esmfold'						):
          __A					:					List[Any]								=		EsmForProteinFolding
     elif classification_head:
          __A					:					Dict								=		EsmForSequenceClassification
     else:
          __A					:					Optional[Any]								=		EsmForMaskedLM
     __A					:					Optional[int]								=		model_class(lowerCAmelCase_						)
     model.eval()

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

     if config.emb_layer_norm_before:
          __A					:					Tuple								=		original_esm_model.emb_layer_norm_before.weight
          __A					:					List[Any]								=		original_esm_model.emb_layer_norm_before.bias

     __A					:					str								=		original_esm_model.emb_layer_norm_after.weight
     __A					:					Dict								=		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					:					int								=		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					:					Optional[Any]								=		esm_layer.self_attn.q_proj.weight
          __A					:					List[Any]								=		esm_layer.self_attn.q_proj.bias
          __A					:					List[Any]								=		esm_layer.self_attn.k_proj.weight
          __A					:					Dict								=		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'  ,       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.
               __A					:					Optional[int]								=		esm_layer.self_attn.rot_emb.inv_freq

          # LayerNorm changes for pre-activation
          __A					:					Optional[int]								=		esm_layer.self_attn_layer_norm.weight
          __A					:					int								=		esm_layer.self_attn_layer_norm.bias
          __A					:					str								=		esm_layer.final_layer_norm.weight
          __A					:					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					:					Optional[int]								=		esm_layer.self_attn.out_proj.weight
          __A					:					Dict								=		esm_layer.self_attn.out_proj.bias

          # intermediate
          __A					:					EsmIntermediate								=		layer.intermediate
          assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
          __A					:					Tuple								=		esm_layer.fca.weight
          __A					:					str								=		esm_layer.fca.bias

          # output
          __A					:					EsmOutput								=		layer.output
          assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
          __A					:					Tuple								=		esm_layer.fca.weight
          __A					:					Dict								=		esm_layer.fca.bias
          # end of layer

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

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

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

          if is_folding_model:
               __A					:					Any								=		torch.max(torch.abs(our_output['positions'] - their_output['positions']						)						).item()
               __A					:					Tuple								=		torch.allclose(our_output['positions']  ,       their_output['positions']  ,       atol=1E-5						)
          else:
               __A					:					str								=		torch.max(torch.abs(our_output - their_output						)						).item()
               __A					:					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
               __A					:					str								=		model.predict_contacts(hf_tokens['input_ids']  ,       hf_tokens['attention_mask']						)
               __A					:					Any								=		esm.predict_contacts(hf_tokens['input_ids']						)
               __A					:					Any								=		torch.max(torch.abs(our_output - their_output						)						).item()
               __A					:					Union[str, 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__":
       lowerCamelCase      : 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.''')
       lowerCamelCase      : Optional[int]       =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 math




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5      ,   int(math.sqrt(lowerCAmelCase_						) + 1						)      ,   6						):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True




def  _a     (				lowerCAmelCase_ = 2_000_000						):





      """simple docstring"""



      return sum(num for num in range(3      ,   lowerCAmelCase_      ,   2						) if is_prime(lowerCAmelCase_						)						) + 2 if n > 2 else 0


if __name__ == "__main__":
   print(F"""{solution() = }""")


