

from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin







@dataclass
class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  42







class   __lowercase				(_UpperCAmelCase		,       _UpperCAmelCase			):
 @register_to_config
 def __init__(							self				,							A_ = 16				,							A_ = 88				,							A_ = None				,							A_ = None				,							A_ = 1				,							A_ = 0.0				,							A_ = 32				,							A_ = None				,							A_ = False				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = "geglu"				,							A_ = None				,							A_ = False				,							A_ = False				,							A_ = False				,							A_ = "layer_norm"				,							A_ = True				,							) ->int:



       '''simple docstring'''





       super().__init__()
       __lowerCAmelCase :						List[Any] = use_linear_projection
       __lowerCAmelCase :						Any = num_attention_heads
       __lowerCAmelCase :						Optional[int] = attention_head_dim
       __lowerCAmelCase :						List[str] = num_attention_heads * attention_head_dim

       # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
       # Define whether input is continuous or discrete depending on configuration
       __lowerCAmelCase :						Union[str, Any] = (in_channels is not None) and (patch_size is None)
       __lowerCAmelCase :						List[str] = num_vector_embeds is not None
       __lowerCAmelCase :						str = in_channels is not None and patch_size is not None

       if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
             __lowerCAmelCase :						Any = (
                 f"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
                 ''' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'''
                 ''' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'''
                 ''' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'''
                 ''' would be very nice if you could open a Pull request for the `transformer/config.json` file'''
             )
             deprecate('''norm_type!=num_embeds_ada_norm'''				,							'''1.0.0'''				,							A_				,							standard_warn=A_ )
             __lowerCAmelCase :						int = '''ada_norm'''

       if self.is_input_continuous and self.is_input_vectorized:
             raise ValueError(
                 f"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
                 ''' sure that either `in_channels` or `num_vector_embeds` is None.''' )
       elif self.is_input_vectorized and self.is_input_patches:
             raise ValueError(
                 f"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
                 ''' sure that either `num_vector_embeds` or `num_patches` is None.''' )
       elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
             raise ValueError(
                 f"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
                 f""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.""" )

       # 2. Define input layers
       if self.is_input_continuous:
             __lowerCAmelCase :						Tuple = in_channels

             __lowerCAmelCase :						Optional[Any] = torch.nn.GroupNorm(num_groups=A_				,							num_channels=A_				,							eps=1e-6				,							affine=A_ )
             if use_linear_projection:
                   __lowerCAmelCase :						Any = nn.Linear(A_				,							A_ )
             else:
                   __lowerCAmelCase :						str = nn.Convad(A_				,							A_				,							kernel_size=1				,							stride=1				,							padding=0 )
       elif self.is_input_vectorized:
             assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
             assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

             __lowerCAmelCase :						Dict = sample_size
             __lowerCAmelCase :						str = sample_size
             __lowerCAmelCase :						str = num_vector_embeds
             __lowerCAmelCase :						Union[str, Any] = self.height * self.width

             __lowerCAmelCase :						int = ImagePositionalEmbeddings(
                 num_embed=A_				,							embed_dim=A_				,							height=self.height				,							width=self.width )
       elif self.is_input_patches:
             assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

             __lowerCAmelCase :						List[Any] = sample_size
             __lowerCAmelCase :						List[Any] = sample_size

             __lowerCAmelCase :						Union[str, Any] = patch_size
             __lowerCAmelCase :						Optional[int] = PatchEmbed(
                 height=A_				,							width=A_				,							patch_size=A_				,							in_channels=A_				,							embed_dim=A_				,							)

       # 3. Define transformers blocks
       __lowerCAmelCase :						Optional[int] = nn.ModuleList(
           [
               BasicTransformerBlock(
                   A_				,							A_				,							A_				,							dropout=A_				,							cross_attention_dim=A_				,							activation_fn=A_				,							num_embeds_ada_norm=A_				,							attention_bias=A_				,							only_cross_attention=A_				,							upcast_attention=A_				,							norm_type=A_				,							norm_elementwise_affine=A_				,							)
               for d in range(A_ )
           ] )

       # 4. Define output layers
       __lowerCAmelCase :						Any = in_channels if out_channels is None else out_channels
       if self.is_input_continuous:
             # TODO: should use out_channels for continuous projections
             if use_linear_projection:
                   __lowerCAmelCase :						Union[str, Any] = nn.Linear(A_				,							A_ )
             else:
                   __lowerCAmelCase :						List[str] = nn.Convad(A_				,							A_				,							kernel_size=1				,							stride=1				,							padding=0 )
       elif self.is_input_vectorized:
             __lowerCAmelCase :						Any = nn.LayerNorm(A_ )
             __lowerCAmelCase :						Dict = nn.Linear(A_				,							self.num_vector_embeds - 1 )
       elif self.is_input_patches:
             __lowerCAmelCase :						Dict = nn.LayerNorm(A_				,							elementwise_affine=A_				,							eps=1e-6 )
             __lowerCAmelCase :						Any = nn.Linear(A_				,							2 * inner_dim )
             __lowerCAmelCase :						Union[str, Any] = nn.Linear(A_				,							patch_size * patch_size * self.out_channels )





 def      UpperCamelCase__  (							self				,							A_				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = True				,							) ->Optional[int]:



       '''simple docstring'''





       if attention_mask is not None and attention_mask.ndim == 2:
             # assume that mask is expressed as:
             #   (1 = keep,      0 = discard)
             # convert mask into a bias that can be added to attention scores:
             #       (keep = +0,     discard = -10000.0)
             __lowerCAmelCase :						Dict = (1 - attention_mask.to(hidden_states.dtype )) * -10_000.0
             __lowerCAmelCase :						Dict = attention_mask.unsqueeze(1 )

       # convert encoder_attention_mask to a bias the same way we do for attention_mask
       if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
             __lowerCAmelCase :						List[Any] = (1 - encoder_attention_mask.to(hidden_states.dtype )) * -10_000.0
             __lowerCAmelCase :						List[Any] = encoder_attention_mask.unsqueeze(1 )

       # 1. Input
       if self.is_input_continuous:
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						Union[str, Any] = hidden_states.shape
             __lowerCAmelCase :						Optional[Any] = hidden_states

             __lowerCAmelCase :						Optional[Any] = self.norm(A_ )
             if not self.use_linear_projection:
                   __lowerCAmelCase :						Optional[Any] = self.proj_in(A_ )
                   __lowerCAmelCase :						int = hidden_states.shape[1]
                   __lowerCAmelCase :						int = hidden_states.permute(0				,							2				,							3				,							1 ).reshape(A_				,							height * width				,							A_ )
             else:
                   __lowerCAmelCase :						Dict = hidden_states.shape[1]
                   __lowerCAmelCase :						Optional[Any] = hidden_states.permute(0				,							2				,							3				,							1 ).reshape(A_				,							height * width				,							A_ )
                   __lowerCAmelCase :						Optional[int] = self.proj_in(A_ )
       elif self.is_input_vectorized:
             __lowerCAmelCase :						str = self.latent_image_embedding(A_ )
       elif self.is_input_patches:
             __lowerCAmelCase :						Union[str, Any] = self.pos_embed(A_ )

       # 2. Blocks
       for block in self.transformer_blocks:
             __lowerCAmelCase :						Tuple = block(
                 A_				,							attention_mask=A_				,							encoder_hidden_states=A_				,							encoder_attention_mask=A_				,							timestep=A_				,							cross_attention_kwargs=A_				,							class_labels=A_				,							)

       # 3. Output
       if self.is_input_continuous:
             if not self.use_linear_projection:
                   __lowerCAmelCase :						List[Any] = hidden_states.reshape(A_				,							A_				,							A_				,							A_ ).permute(0				,							3				,							1				,							2 ).contiguous()
                   __lowerCAmelCase :						Optional[Any] = self.proj_out(A_ )
             else:
                   __lowerCAmelCase :						int = self.proj_out(A_ )
                   __lowerCAmelCase :						List[str] = hidden_states.reshape(A_				,							A_				,							A_				,							A_ ).permute(0				,							3				,							1				,							2 ).contiguous()

             __lowerCAmelCase :						int = hidden_states + residual
       elif self.is_input_vectorized:
             __lowerCAmelCase :						List[str] = self.norm_out(A_ )
             __lowerCAmelCase :						Any = self.out(A_ )
             # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
             __lowerCAmelCase :						Union[str, Any] = logits.permute(0				,							2				,							1 )

             # log(p(x_0))
             __lowerCAmelCase :						Dict = F.log_softmax(logits.double()				,							dim=1 ).float()
       elif self.is_input_patches:
             # TODO: cleanup!
             __lowerCAmelCase :						Any = self.transformer_blocks[0].norma.emb(
                 A_				,							A_				,							hidden_dtype=hidden_states.dtype )
             __lowerCAmelCase,							__lowerCAmelCase :						Dict = self.proj_out_a(F.silu(A_ ) ).chunk(2				,							dim=1 )
             __lowerCAmelCase :						Any = self.norm_out(A_ ) * (1 + scale[:, None]) + shift[:, None]
             __lowerCAmelCase :						Tuple = self.proj_out_a(A_ )

             # unpatchify
             __lowerCAmelCase :						Tuple = int(hidden_states.shape[1] ** 0.5 )
             __lowerCAmelCase :						List[str] = hidden_states.reshape(
                 shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) )
             __lowerCAmelCase :						Tuple = torch.einsum('''nhwpqc->nchpwq'''				,							A_ )
             __lowerCAmelCase :						List[str] = hidden_states.reshape(
                 shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) )

       if not return_dict:
             return (output,)

       return TransformeraDModelOutput(sample=A_ )





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
 )


