'''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()
lowerCAmelCase_			:			int    	=   logging.get_logger(__name__)

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

lowerCAmelCase_			:			Any    	=   {
    "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,
}

lowerCAmelCase_			:			Any    	=   list("ARNDCQEGHILKMFPSTWYV")

lowerCAmelCase_			:			int    	=   restypes + ["X"]
lowerCAmelCase_			:			Optional[int]    	=   restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]


def __a			(						)						->				Union[str, Any]:
      '''simple docstring'''


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


def __a			(						__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    Dict					)						->				int:
      '''simple docstring'''


      lowercase_     			=   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 __a			(						__lowerCamelCase      :    str			,		__lowerCamelCase      :    str			,		__lowerCamelCase      :    bool			,		__lowerCamelCase      :    str			,		__lowerCamelCase      :    str					)						->				Optional[int]:
      '''simple docstring'''


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

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

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

      if is_folding_model:
            lowercase_     			=   esm.esm
      else:
            lowercase_     			=   esm

      lowercase_     			=   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_026			,		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:
            lowercase_     			=   esm.classification_heads["mnli"].out_proj.weight.shape[0]
      print("Our ESM config:"			,		_lowerCAmelCase					)

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

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

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

      lowercase_     			=   original_esm_model.emb_layer_norm_after.weight
      lowercase_     			=   original_esm_model.emb_layer_norm_after.bias

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

            # self attention
            lowercase_     			=   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)					)
            )

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

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

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

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

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

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

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

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

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






def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     if len(_lowerCAmelCase							) < len(_lowerCAmelCase							):
          return levenshtein_distance(_lowerCAmelCase,  _lowerCAmelCase							)

     if len(_lowerCAmelCase							) == 0:
          return len(_lowerCAmelCase							)

     _a     						=					list(range(len(_lowerCAmelCase							) + 1							)							)

     for i, ca in enumerate(_lowerCAmelCase							):
          _a     						=					[i + 1]

          for j, ca in enumerate(_lowerCAmelCase							):
               # Calculate insertions, deletions and substitutions
               _a     						=					previous_row[j + 1] + 1
               _a     						=					current_row[j] + 1
               _a     						=					previous_row[j] + (ca != ca)

               # Get the minimum to append to the current row
               current_row.append(min(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)							)

          # Store the previous row
          _a     						=					current_row

     # Returns the last element (distance)
     return previous_row[-1]


if __name__ == "__main__":
   __snake_case												=							input('''Enter the first word:\n''').strip()
   __snake_case												=							input('''Enter the second word:\n''').strip()

   __snake_case												=							levenshtein_distance(first_word, second_word)
   print(f'Levenshtein distance between {first_word} and {second_word} is {result}')