


'''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()
lowercase__					  =		logging.get_logger(__name__)

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

lowercase__					  =		{
    "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,
}

lowercase__					  =		list("ARNDCQEGHILKMFPSTWYV")

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






def __UpperCamelCase (      )   ->  Optional[Any]:







     '''simple docstring'''






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






def __UpperCamelCase (      __lowerCamelCase		:			List[Any]   ,	__lowerCamelCase		:			Union[str, Any]					)   ->  Union[str, Any]:







     '''simple docstring'''






     _a            =		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					)   ->  List[str]:







     '''simple docstring'''






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

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

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

     if is_folding_model:
          _a            =		esm.esm
     else:
          _a            =		esm

     _a            =		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            =		esm.classification_heads["mnli"].out_proj.weight.shape[0]
     print("Our ESM config:"   ,	__lowerCamelCase					)

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

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

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

     _a            =		original_esm_model.emb_layer_norm_after.weight
     _a            =		original_esm_model.emb_layer_norm_after.bias

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

          # self attention
          _a            =		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            =		esm_layer.self_attn.q_proj.weight
          _a            =		esm_layer.self_attn.q_proj.bias
          _a            =		esm_layer.self_attn.k_proj.weight
          _a            =		esm_layer.self_attn.k_proj.bias
          _a            =		esm_layer.self_attn.v_proj.weight
          _a            =		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            =		esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

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

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

          _a            =		hf_tokenizer([row[1] for row in sample_data]   ,	return_tensors="pt"   ,	padding=__lowerCamelCase					)
          _a            =		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            =		esm.cuda().infer([row[1] for row in sample_data]					)
               _a            =		model.cuda()(
                   input_ids=hf_tokens["input_ids"].cuda()   ,	attention_mask=hf_tokens["attention_mask"].cuda()					)
          else:
               _a            =		model(**__lowerCamelCase   ,	output_hidden_states=__lowerCamelCase					)
               _a            =		our_output["logits"]
               if classification_head:
                    _a            =		esm.model.classification_heads["mnli"](esm.extract_features(__lowerCamelCase					)					)
               else:
                    _a            =		esm(hf_tokens["input_ids"]   ,	repr_layers=list(range(999					)					)					)
                    _a            =		their_output["logits"]

          if is_folding_model:
               _a            =		torch.max(torch.abs(our_output["positions"] - their_output["positions"]					)					).item()
               _a            =		torch.allclose(our_output["positions"]   ,	their_output["positions"]   ,	atol=1E-5					)
          else:
               _a            =		torch.max(torch.abs(our_output - their_output					)					).item()
               _a            =		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            =		model.predict_contacts(hf_tokens["input_ids"]   ,	hf_tokens["attention_mask"]					)
               _a            =		esm.predict_contacts(hf_tokens["input_ids"]					)
               _a            =		torch.max(torch.abs(our_output - their_output					)					).item()
               _a            =		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__":
   lowercase__					  =		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.")
   lowercase__					  =		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'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__					  =		{
    "configuration_swin2sr": ["SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swin2SRConfig"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__					  =		[
       "SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST",
       "Swin2SRForImageSuperResolution",
       "Swin2SRModel",
       "Swin2SRPreTrainedModel",
   ]


try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__					  =		["Swin2SRImageProcessor"]


if TYPE_CHECKING:
   from .configuration_swinasr import SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinaSRConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swinasr import (
          SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinaSRForImageSuperResolution,
          SwinaSRModel,
          SwinaSRPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_swinasr import SwinaSRImageProcessor


else:
   import sys

   lowercase__					  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

