

"""simple docstring"""




import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

_UpperCamelCase			      =		logging.get_logger(__name__)

_UpperCamelCase			      =		{"vocab_file": "tokenizer.model"}

_UpperCamelCase			      =		{
    "vocab_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
    },
    "tokenizer_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
    },
}
_UpperCamelCase			      =		{
    "hf-internal-testing/llama-tokenizer": 2048,
}
_UpperCamelCase			      =		"▁"

_UpperCamelCase      ,      _UpperCamelCase			      =		"[INST]", "[/INST]"
_UpperCamelCase      ,      _UpperCamelCase			      =		"<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
_UpperCamelCase			      =		"You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."







class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  VOCAB_FILES_NAMES
 _UpperCamelCase        =  PRETRAINED_VOCAB_FILES_MAP
 _UpperCamelCase        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _UpperCamelCase        =  ["""input_ids""", """attention_mask"""]
 def __init__(							self				,							A_				,							A_="<unk>"				,							A_="<s>"				,							A_="</s>"				,							A_=None				,							A_ = None				,							A_=True				,							A_=False				,							A_=False				,							A_=True				,							**A_				,							) ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
       __lowerCAmelCase :						int = AddedToken(A_				,							lstrip=A_				,							rstrip=A_ ) if isinstance(A_				,							A_ ) else bos_token
       __lowerCAmelCase :						Optional[int] = AddedToken(A_				,							lstrip=A_				,							rstrip=A_ ) if isinstance(A_				,							A_ ) else eos_token
       __lowerCAmelCase :						int = AddedToken(A_				,							lstrip=A_				,							rstrip=A_ ) if isinstance(A_				,							A_ ) else unk_token
       __lowerCAmelCase :						Dict = AddedToken(A_				,							lstrip=A_				,							rstrip=A_ ) if isinstance(A_				,							A_ ) else pad_token
       super().__init__(
           bos_token=A_				,							eos_token=A_				,							unk_token=A_				,							pad_token=A_				,							add_bos_token=A_				,							add_eos_token=A_				,							sp_model_kwargs=self.sp_model_kwargs				,							clean_up_tokenization_spaces=A_				,							legacy=A_				,							**A_				,							)
       if legacy:
             logger.warning_once(
                 f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
                 ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
       __lowerCAmelCase :						List[str] = legacy
       __lowerCAmelCase :						Dict = vocab_file
       __lowerCAmelCase :						Tuple = add_bos_token
       __lowerCAmelCase :						Union[str, Any] = add_eos_token
       __lowerCAmelCase :						Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(A_ )
 def __getstate__(							self ) ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = self.__dict__.copy()
       __lowerCAmelCase :						Optional[Any] = None
       __lowerCAmelCase :						List[Any] = self.sp_model.serialized_model_proto()
       return state
 def __setstate__(							self				,							A_ ) ->Dict:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = d
       __lowerCAmelCase :						str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
 @property
 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       return self.sp_model.get_piece_size()
 def      UpperCamelCase__  (							self ) ->Dict:



       '''simple docstring'''





       __lowerCAmelCase :						List[str] = {self.convert_ids_to_tokens(A_ ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab
 def      UpperCamelCase__  (							self				,							A_				,							**A_ ) ->List[str]:



       '''simple docstring'''





       if not self.legacy:
             __lowerCAmelCase :						List[Any] = SPIECE_UNDERLINE + text.replace(A_				,							''' ''' )
       return super().tokenize(A_				,							**A_ )
 def      UpperCamelCase__  (							self				,							A_ ) ->int:



       '''simple docstring'''





       if not self.legacy:
             __lowerCAmelCase :						Optional[Any] = text.startswith(A_ )
             if is_first:
                   __lowerCAmelCase :						Any = text[1:]

       __lowerCAmelCase :						Optional[Any] = self.sp_model.encode(A_				,							out_type=A_ )

       if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(A_ ):
             __lowerCAmelCase :						List[str] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
       return tokens
 def      UpperCamelCase__  (							self				,							A_ ) ->List[Any]:



       '''simple docstring'''





       return self.sp_model.piece_to_id(A_ )
 def      UpperCamelCase__  (							self				,							A_ ) ->Dict:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = self.sp_model.IdToPiece(A_ )
       return token
 def      UpperCamelCase__  (							self				,							A_ ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						List[str] = []
       __lowerCAmelCase :						Dict = ''''''
       __lowerCAmelCase :						Any = False
       for i, token in enumerate(A_ ):
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   if not prev_is_special and i != 0:
                         out_string += " "
                   out_string += self.sp_model.decode(A_ ) + token
                   __lowerCAmelCase :						List[str] = True
                   __lowerCAmelCase :						List[str] = []
             else:
                   current_sub_tokens.append(A_ )
                   __lowerCAmelCase :						Optional[int] = False
       out_string += self.sp_model.decode(A_ )
       return out_string
 def      UpperCamelCase__  (							self				,							A_				,							A_ = None ) ->Tuple[str]:



       '''simple docstring'''





       if not os.path.isdir(A_ ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
             return
       __lowerCAmelCase :						List[str] = os.path.join(
           A_				,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

       if os.path.abspath(self.vocab_file ) != os.path.abspath(A_ ) and os.path.isfile(self.vocab_file ):
             copyfile(self.vocab_file				,							A_ )
       elif not os.path.isfile(self.vocab_file ):
             with open(A_				,							'''wb''' ) as fi:
                   __lowerCAmelCase :						Dict = self.sp_model.serialized_model_proto()
                   fi.write(A_ )

       return (out_vocab_file,)
 def      UpperCamelCase__  (							self				,							A_				,							A_=None ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						str = [self.bos_token_id] if self.add_bos_token else []
       __lowerCAmelCase :						Optional[int] = [self.eos_token_id] if self.add_eos_token else []

       __lowerCAmelCase :						int = bos_token_id + token_ids_a + eos_token_id

       if token_ids_a is not None:
             __lowerCAmelCase :						List[str] = output + bos_token_id + token_ids_a + eos_token_id

       return output
 def      UpperCamelCase__  (							self				,							A_				,							A_ = None				,							A_ = False ) ->List[int]:



       '''simple docstring'''





       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=A_				,							token_ids_a=A_				,							already_has_special_tokens=A_ )

       __lowerCAmelCase :						Union[str, Any] = [1] if self.add_bos_token else []
       __lowerCAmelCase :						Optional[int] = [1] if self.add_eos_token else []

       if token_ids_a is None:
             return bos_token_id + ([0] * len(A_ )) + eos_token_id
       return (
           bos_token_id
           + ([0] * len(A_ ))
           + eos_token_id
           + bos_token_id
           + ([0] * len(A_ ))
           + eos_token_id
       )
 def      UpperCamelCase__  (							self				,							A_				,							A_ = None ) ->List[int]:



       '''simple docstring'''





       __lowerCAmelCase :						str = [self.bos_token_id] if self.add_bos_token else []
       __lowerCAmelCase :						List[str] = [self.eos_token_id] if self.add_eos_token else []

       __lowerCAmelCase :						List[str] = [0] * len(bos_token_id + token_ids_a + eos_token_id )

       if token_ids_a is not None:
             output += [1] * len(bos_token_id + token_ids_a + eos_token_id )

       return output





 def      UpperCamelCase__  (							self				,							A_ ) ->List[int]:



       '''simple docstring'''





       __lowerCAmelCase :						Any = list(conversation.iter_texts() )
       if not all([is_user for is_user, msg in dialogue[::2]] ) or not all(
           [not is_user for is_user, msg in dialogue[1::2]] ):
             raise ValueError(
                 '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)''' )

       __lowerCAmelCase :						List[int] = []
       if len(conversation.past_user_inputs ) > 0:
             if not conversation.past_user_inputs[0].startswith(A_ ) or E_SYS not in conversation.past_user_inputs[0]:
                   __lowerCAmelCase :						Tuple = (
                       B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                   )
       elif not dialogue[0][1].startswith(A_ ) or E_SYS not in dialogue[0][1]:
             __lowerCAmelCase :						Optional[Any] = (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

       dialog_tokens += sum(
           [
               [self.bos_token_id]
               + self.encode(
                   f"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """				,							add_special_tokens=A_ )
               + [self.eos_token_id]
               for prompt, answer in zip(dialogue[::2]				,							dialogue[1::2] )
           ]				,							[]				,							)
       if not (dialogue[-1][0]):
             raise ValueError(f"""Last message must be from user, got {dialogue[-1]["role"]}""" )
       dialog_tokens += [self.bos_token_id] + self.encode(
           f"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""				,							add_special_tokens=A_ )
       return dialog_tokens





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			      =		logging.get_logger(__name__)

_UpperCamelCase			      =		[
    (
        "protein1",
        "MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
    ),
    ("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
    ("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
    ("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
]

_UpperCamelCase			      =		{
    "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			      =		list("ARNDCQEGHILKMFPSTWYV")

_UpperCamelCase			      =		restypes + ["X"]
_UpperCamelCase			      =		restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]







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







def 		_lowercase			(					lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						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 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):
      if model.startswith('''esmfold'''						):
            __lowerCAmelCase :						Tuple = MODEL_MAPPING[model]()
      else:
            __lowerCAmelCase,							__lowerCAmelCase :						str = MODEL_MAPPING[model]()
      esm.eval()  # disable dropout

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

      if is_folding_model:
            __lowerCAmelCase :						Dict = esm.esm.alphabet
      __lowerCAmelCase :						str = tuple(alphabet.all_toks						)
      __lowerCAmelCase :						Tuple = alphabet.mask_idx
      __lowerCAmelCase :						Optional[int] = alphabet.padding_idx

      if is_folding_model:
            __lowerCAmelCase :						List[Any] = esm.esm
      else:
            __lowerCAmelCase :						Tuple = esm

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

      if model.startswith('''esmfold'''						):
            __lowerCAmelCase :						Any = EsmForProteinFolding
      elif classification_head:
            __lowerCAmelCase :						Union[str, Any] = EsmForSequenceClassification
      else:
            __lowerCAmelCase :						Any = EsmForMaskedLM
      __lowerCAmelCase :						str = model_class(lowercase__						)
      model.eval()

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

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

      __lowerCAmelCase :						Union[str, Any] = original_esm_model.emb_layer_norm_after.weight
      __lowerCAmelCase :						Any = original_esm_model.emb_layer_norm_after.bias

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

            # self attention
            __lowerCAmelCase :						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)						)
            )

            __lowerCAmelCase :						str = esm_layer.self_attn.q_proj.weight
            __lowerCAmelCase :						Dict = esm_layer.self_attn.q_proj.bias
            __lowerCAmelCase :						Dict = esm_layer.self_attn.k_proj.weight
            __lowerCAmelCase :						Optional[Any] = esm_layer.self_attn.k_proj.bias
            __lowerCAmelCase :						Tuple = esm_layer.self_attn.v_proj.weight
            __lowerCAmelCase :						str = esm_layer.self_attn.v_proj.bias

            if getattr(esm_layer.self_attn       ,     '''rot_emb'''       ,     lowercase__						) 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 :						str = esm_layer.self_attn.rot_emb.inv_freq

            # LayerNorm changes for pre-activation
            __lowerCAmelCase :						Union[str, Any] = esm_layer.self_attn_layer_norm.weight
            __lowerCAmelCase :						List[Any] = esm_layer.self_attn_layer_norm.bias
            __lowerCAmelCase :						List[Any] = esm_layer.final_layer_norm.weight
            __lowerCAmelCase :						Union[str, Any] = esm_layer.final_layer_norm.bias

            # self-attention output
            __lowerCAmelCase :						EsmSelfOutput = layer.attention.output
            assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
            __lowerCAmelCase :						str = esm_layer.self_attn.out_proj.weight
            __lowerCAmelCase :						List[str] = esm_layer.self_attn.out_proj.bias

            # intermediate
            __lowerCAmelCase :						EsmIntermediate = layer.intermediate
            assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
            __lowerCAmelCase :						str = esm_layer.fca.weight
            __lowerCAmelCase :						int = esm_layer.fca.bias

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

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

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

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

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

            del esm  # Free up some memory before continuing

      print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""						)
      hf_tokenizer.save_pretrained(lowercase__						)

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


if __name__ == "__main__":
 _UpperCamelCase			      =		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			      =		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
 )


