






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					:						Dict         =     logging.get_logger(__name__)

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

_UpperCAmelCase					:						Optional[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,
}

_UpperCAmelCase					:						str         =     list('ARNDCQEGHILKMFPSTWYV')

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






def  UpperCAmelCase__						(    ):


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






def  UpperCAmelCase__						(    lowerCamelCase	:			Any			,lowerCamelCase	:			Tuple    ):
   lowerCamelCase									=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__						(    lowerCamelCase	:			str			,lowerCamelCase	:			str			,lowerCamelCase	:			bool			,lowerCamelCase	:			str			,lowerCamelCase	:			str    ):


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

   if model.startswith("""esmfold"""    ):
      lowerCamelCase									=esm.esm.embed_dim
      lowerCamelCase									=esm.esm.num_layers
      lowerCamelCase									=esm.esm.attention_heads
      lowerCamelCase									=4 * embed_dim
      lowerCamelCase									=esm.esm.token_dropout
      lowerCamelCase									=False  # This code path does not exist in ESM-2
      lowerCamelCase									="""rotary"""
      lowerCamelCase									=True
      lowerCamelCase									=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
      lowerCamelCase									=esm.args.embed_dim
      lowerCamelCase									=esm.args.layers
      lowerCamelCase									=esm.args.attention_heads
      lowerCamelCase									=esm.args.ffn_embed_dim
      lowerCamelCase									=esm.args.token_dropout
      lowerCamelCase									=True if esm.emb_layer_norm_before else False
      lowerCamelCase									="""absolute"""
      lowerCamelCase									=False
      lowerCamelCase									=None
   else:
      # Indicates an ESM-2 model
      lowerCamelCase									=esm.embed_dim
      lowerCamelCase									=esm.num_layers
      lowerCamelCase									=esm.attention_heads
      lowerCamelCase									=4 * embed_dim  # This is hardcoded in ESM-2
      lowerCamelCase									=esm.token_dropout
      lowerCamelCase									=False  # This code path does not exist in ESM-2
      lowerCamelCase									="""rotary"""
      lowerCamelCase									=False
      lowerCamelCase									=None

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

   if is_folding_model:
      lowerCamelCase									=esm.esm
   else:
      lowerCamelCase									=esm

   lowerCamelCase									=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=10_26			,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:
      lowerCamelCase									=esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
   print("""Our ESM config:"""			,__UpperCamelCase    )

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

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

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

   lowerCamelCase									=original_esm_model.emb_layer_norm_after.weight
   lowerCamelCase									=original_esm_model.emb_layer_norm_after.bias

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

      # self attention
      lowerCamelCase									=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)    )
      )

      lowerCamelCase									=esm_layer.self_attn.q_proj.weight
      lowerCamelCase									=esm_layer.self_attn.q_proj.bias
      lowerCamelCase									=esm_layer.self_attn.k_proj.weight
      lowerCamelCase									=esm_layer.self_attn.k_proj.bias
      lowerCamelCase									=esm_layer.self_attn.v_proj.weight
      lowerCamelCase									=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.
         lowerCamelCase									=esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

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

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

      lowerCamelCase									=hf_tokenizer([row[1] for row in sample_data]			,return_tensors="""pt"""			,padding=__UpperCamelCase    )
      lowerCamelCase									=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.
         lowerCamelCase									=esm.cuda().infer([row[1] for row in sample_data]    )
         lowerCamelCase									=model.cuda()(
             input_ids=hf_tokens["""input_ids"""].cuda()			,attention_mask=hf_tokens["""attention_mask"""].cuda()    )
      else:
         lowerCamelCase									=model(**__UpperCamelCase			,output_hidden_states=__UpperCamelCase    )
         lowerCamelCase									=our_output["""logits"""]
         if classification_head:
            lowerCamelCase									=esm.model.classification_heads["""mnli"""](esm.extract_features(__UpperCamelCase    )    )
         else:
            lowerCamelCase									=esm(hf_tokens["""input_ids"""]			,repr_layers=list(range(9_99    )    )    )
            lowerCamelCase									=their_output["""logits"""]

      if is_folding_model:
         lowerCamelCase									=torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]    )    ).item()
         lowerCamelCase									=torch.allclose(our_output["""positions"""]			,their_output["""positions"""]			,atol=1E-5    )
      else:
         lowerCamelCase									=torch.max(torch.abs(our_output - their_output    )    ).item()
         lowerCamelCase									=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
         lowerCamelCase									=model.predict_contacts(hf_tokens["""input_ids"""]			,hf_tokens["""attention_mask"""]    )
         lowerCamelCase									=esm.predict_contacts(hf_tokens["""input_ids"""]    )
         lowerCamelCase									=torch.max(torch.abs(our_output - their_output    )    ).item()
         lowerCamelCase									=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__":
   _UpperCAmelCase					:						Dict         =     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					:						Optional[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
   )
"""simple docstring"""

import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info


def    lowerCAmelCase			(__UpperCamelCase							:    Optional[Any]			):
							"""simple docstring"""


							class 		_lowercase :



													"""simple docstring"""




													def __init__(				self							:    int     , UpperCamelCase__							:    Union[str, Any]				)				->				None:
																				'''simple docstring'''



																				assert isinstance(UpperCamelCase__     , (bytes, str)				)
																				__UpperCamelCase							=content





													def   UpperCAmelCase_ (				self							:    Dict				)				->				Any:
																				'''simple docstring'''



																				return json.loads(self.content				)

							def mock_response(*__UpperCamelCase							:    Any							,					**__UpperCamelCase							:    Union[str, Any]			):
														assert args[0] == AUTHENTICATED_USER_ENDPOINT
														assert "Authorization" in kwargs["headers"]
														assert kwargs["headers"]["Authorization"].startswith('''token '''			)
														assert "Accept" in kwargs["headers"]
														return FakeResponse(b'''{"login":"test","id":1}'''			)

							monkeypatch.setattr(__UpperCamelCase							,					'''get'''							,					__UpperCamelCase			)
							__UpperCamelCase							=fetch_github_info('''token'''			)
							assert result["login"] == "test"
							assert result["id"] == 1



