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






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict ,	lowerCAmelCase_: Union[str, Any]     ):
      snake_case_     :			Tuple       =       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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: str ,	lowerCAmelCase_: str ,	lowerCAmelCase_: bool ,	lowerCAmelCase_: str ,	lowerCAmelCase_: str     ):
      if model.startswith("esmfold"     ):
            snake_case_     :			List[str]       =       MODEL_MAPPING[model]()
      else:
            snake_case_     :			Optional[Any]       =       MODEL_MAPPING[model]()
      esm.eval()  # disable dropout

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

      if is_folding_model:
            snake_case_     :			str       =       esm.esm.alphabet
      snake_case_     :			Optional[int]       =       tuple(alphabet.all_toks     )
      snake_case_     :			Optional[Any]       =       alphabet.mask_idx
      snake_case_     :			Optional[int]       =       alphabet.padding_idx

      if is_folding_model:
            snake_case_     :			Optional[Any]       =       esm.esm
      else:
            snake_case_     :			List[str]       =       esm

      snake_case_     :			Optional[int]       =       EsmConfig(
          vocab_size=original_esm_model.embed_tokens.num_embeddings ,	mask_token_id=lowerCAmelCase_ ,	hidden_size=lowerCAmelCase_ ,	num_hidden_layers=lowerCAmelCase_ ,	num_attention_heads=lowerCAmelCase_ ,	intermediate_size=lowerCAmelCase_ ,	max_position_embeddings=1_0_2_6 ,	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:
            snake_case_     :			Tuple       =       esm.classification_heads["mnli"].out_proj.weight.shape[0]
      print("Our ESM config:" ,	lowerCAmelCase_     )

      if model.startswith("esmfold"     ):
            snake_case_     :			Optional[Any]       =       EsmForProteinFolding
      elif classification_head:
            snake_case_     :			Any       =       EsmForSequenceClassification
      else:
            snake_case_     :			Optional[Any]       =       EsmForMaskedLM
      snake_case_     :			Dict       =       model_class(lowerCAmelCase_     )
      model.eval()

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

      if config.emb_layer_norm_before:
            snake_case_     :			Optional[Any]       =       original_esm_model.emb_layer_norm_before.weight
            snake_case_     :			List[str]       =       original_esm_model.emb_layer_norm_before.bias

      snake_case_     :			Tuple       =       original_esm_model.emb_layer_norm_after.weight
      snake_case_     :			int       =       original_esm_model.emb_layer_norm_after.bias

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

            # self attention
            snake_case_     :			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)     )
            )

            snake_case_     :			Dict       =       esm_layer.self_attn.q_proj.weight
            snake_case_     :			Dict       =       esm_layer.self_attn.q_proj.bias
            snake_case_     :			int       =       esm_layer.self_attn.k_proj.weight
            snake_case_     :			Any       =       esm_layer.self_attn.k_proj.bias
            snake_case_     :			Any       =       esm_layer.self_attn.v_proj.weight
            snake_case_     :			int       =       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.
                  snake_case_     :			int       =       esm_layer.self_attn.rot_emb.inv_freq

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

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

            # intermediate
            snake_case_     :			EsmIntermediate       =       layer.intermediate
            assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
            snake_case_     :			Optional[int]       =       esm_layer.fca.weight
            snake_case_     :			Optional[int]       =       esm_layer.fca.bias

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

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

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

            snake_case_     :			Tuple       =       hf_tokenizer([row[1] for row in sample_data] ,	return_tensors="pt" ,	padding=lowerCAmelCase_     )
            snake_case_     :			Optional[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.
                  snake_case_     :			Dict       =       esm.cuda().infer([row[1] for row in sample_data]     )
                  snake_case_     :			int       =       model.cuda()(
                      input_ids=hf_tokens["input_ids"].cuda() ,	attention_mask=hf_tokens["attention_mask"].cuda()     )
            else:
                  snake_case_     :			Dict       =       model(**lowerCAmelCase_ ,	output_hidden_states=lowerCAmelCase_     )
                  snake_case_     :			Dict       =       our_output["logits"]
                  if classification_head:
                        snake_case_     :			Tuple       =       esm.model.classification_heads["mnli"](esm.extract_features(lowerCAmelCase_     )     )
                  else:
                        snake_case_     :			Optional[Any]       =       esm(hf_tokens["input_ids"] ,	repr_layers=list(range(9_9_9     )     )     )
                        snake_case_     :			Optional[int]       =       their_output["logits"]

            if is_folding_model:
                  snake_case_     :			Dict       =       torch.max(torch.abs(our_output["positions"] - their_output["positions"]     )     ).item()
                  snake_case_     :			Tuple       =       torch.allclose(our_output["positions"] ,	their_output["positions"] ,	atol=1e-5     )
            else:
                  snake_case_     :			Tuple       =       torch.max(torch.abs(our_output - their_output     )     ).item()
                  snake_case_     :			int       =       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
                  snake_case_     :			Any       =       model.predict_contacts(hf_tokens["input_ids"] ,	hf_tokens["attention_mask"]     )
                  snake_case_     :			Optional[int]       =       esm.predict_contacts(hf_tokens["input_ids"]     )
                  snake_case_     :			int       =       torch.max(torch.abs(our_output - their_output     )     ).item()
                  snake_case_     :			Optional[int]       =       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__":
     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
     )




import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
     import torch

     from transformers import GPTaLMHeadModel



@require_torch
@require_sentencepiece
@require_tokenizers
class 					snake_case__       (				unittest.TestCase							):



   @slow
   @unittest.skip("Model is not available."   )
   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			str       =       "nvidia/megatron-gpt2-345m/"
         if "MYDIR" in os.environ:
               snake_case_     :			int       =       os.path.join(os.environ["MYDIR"]			,	A__   )
         snake_case_     :			str       =       GPTaLMHeadModel.from_pretrained(A__   )
         model.to(A__   )
         model.half()

         snake_case_     :			Optional[int]       =       torch.tensor(
             [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]			,	device=A__			,	dtype=torch.long			,	)

         with torch.no_grad():
               snake_case_     :			Union[str, Any]       =       model(A__   ).logits

         snake_case_     :			List[str]       =       torch.Size((1, 9, 5_02_57)   )
         self.assertEqual(output.shape			,	A__   )

         snake_case_     :			Dict       =       torch.tensor(
             [
                 4.9414,
                 -0.2920,
                 -1.2148,
                 -4.0273,
                 -0.5161,
                 -5.2109,
                 -1.2412,
                 -1.8301,
                 -1.7734,
                 -4.7148,
                 -0.2317,
                 -1.0811,
                 -2.1777,
                 0.4141,
                 -3.7969,
                 -4.0586,
                 -2.5332,
                 -3.3809,
                 4.3867,
             ]			,	device=A__			,	dtype=torch.half			,	)

         for i in range(19   ):
               snake_case_  ,snake_case_     :			Dict       =       8 * i // 17, 27_92 * i  # along the diagonal
               snake_case_  ,snake_case_     :			Any       =       output[0, r, c], expected_diag[i]
               snake_case_     :			Dict       =       f"row={r} col={c} computed={computed} expected={expected}"
               self.assertAlmostEqual(A__			,	A__			,	delta=1E-4			,	msg=A__   )



