"""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_:       List[Any]			     =       logging.get_logger(__name__)

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

lowerCAmelCase_:       Tuple			     =       {
    "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_:       Optional[Any]			     =       list("ARNDCQEGHILKMFPSTWYV")

lowerCAmelCase_:       Tuple			     =       restypes + ["X"]
lowerCAmelCase_:       Union[str, Any]			     =       restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]







def        __a (		):






     '''simple docstring'''



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







def        __a (		A     ,					A   ):






     '''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 (		A     ,					A     ,					A     ,					A     ,					A   ):






     '''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(A     ,					A   ) and key != "trunk":
                    setattr(A     ,					A     ,					A   )
          for key, val in esm.cfg.trunk.items():
               if hasattr(esmfold_config.trunk     ,					A   ) and key != "structure_module":
                    setattr(esmfold_config.trunk     ,					A     ,					A   )
          for key, val in esm.cfg.trunk.structure_module.items():
               if hasattr(esmfold_config.trunk.structure_module     ,					A   ):
                    setattr(esmfold_config.trunk.structure_module     ,					A     ,					A   )
     elif hasattr(A     ,					"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=A     ,					hidden_size=A     ,					num_hidden_layers=A     ,					num_attention_heads=A     ,					intermediate_size=A     ,					max_position_embeddings=10_26     ,					layer_norm_eps=1e-5     ,					attention_probs_dropout_prob=0.0     ,					hidden_dropout_prob=0.0     ,					pad_token_id=A     ,					emb_layer_norm_before=A     ,					token_dropout=A     ,					position_embedding_type=A     ,					is_folding_model=A     ,					esmfold_config=A     ,					vocab_list=A     ,					)
     if classification_head:
          lowercase__								=				esm.classification_heads["mnli"].out_proj.weight.shape[0]
     print("Our ESM config:"     ,					A   )

     if model.startswith("esmfold"   ):
          lowercase__								=				EsmForProteinFolding
     elif classification_head:
          lowercase__								=				EsmForSequenceClassification
     else:
          lowercase__								=				EsmForMaskedLM
     lowercase__								=				model_class(A   )
     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"     ,					A   ) 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=A     ,					add_special_tokens=A   )
          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(A   )
          # Prepare tokenizer and make sure it matches
          with TemporaryDirectory() as tempdir:
               lowercase__								=				"\n".join(alphabet.all_toks   )
               lowercase__								=				Path(A   ) / "vocab.txt"
               vocab_file.write_text(A   )
               lowercase__								=				EsmTokenizer(vocab_file=str(A   )   )

          lowercase__								=				hf_tokenizer([row[1] for row in sample_data]     ,					return_tensors="pt"     ,					padding=A   )
          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(**A     ,					output_hidden_states=A   )
               lowercase__								=				our_output["logits"]
               if classification_head:
                    lowercase__								=				esm.model.classification_heads["mnli"](esm.extract_features(A   )   )
               else:
                    lowercase__								=				esm(hf_tokens["input_ids"]     ,					repr_layers=list(range(9_99   )   )   )
                    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(A     ,					A     ,					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(A     ,					A     ,					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(A   ).mkdir(parents=A     ,					exist_ok=A   )
          print(f'''Saving model to {pytorch_dump_folder_path}'''   )
          model.save_pretrained(A   )

          del esm  # Free up some memory before continuing

     print(f'''Saving tokenizer to {pytorch_dump_folder_path}'''   )
     hf_tokenizer.save_pretrained(A   )

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


if __name__ == "__main__":
 lowerCAmelCase_:       int			     =       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_:       Union[str, Any]			     =       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"""


import qiskit
from qiskit.providers import Backend







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__			,    lowercase__								=				bin(A   )[2:], bin(A   )[2:]  # Remove leading '0b'

     # Ensure that both strings are of the same length
     if len(A   ) > len(A   ):
          lowercase__								=				y.zfill(len(A   )   )
     else:
          lowercase__								=				x.zfill(len(A   )   )

     # We need (3 * number of bits in the larger number)+1 qBits
     # The second parameter is the number of classical registers, to measure the result
     lowercase__								=				qiskit.QuantumCircuit((len(A   ) * 3) + 1     ,					len(A   ) + 1   )

     # We are essentially "not-ing" the bits that are 1
     # Reversed because it's easier to perform ops on more significant bits
     for i in range(len(A   )   ):
          if x[::-1][i] == "1":
               circuit.x(A   )
     for j in range(len(A   )   ):
          if y[::-1][j] == "1":
               circuit.x(len(A   ) + j   )

     return circuit, x, y







def        __a (		A     ,					A     ,					A     ,					A     ,					A     ,					):






     '''simple docstring'''



     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.cx(A     ,					A   )







def        __a (		A     ,					A     ,					A = qiskit.Aer.get_backend("aer_simulator"   )     ,					):






     '''simple docstring'''



     if vala < 0 or vala < 0:
          raise ValueError("Both Integers must be positive!"   )

     # Store the Integers
     lowercase__			,    lowercase__			,    lowercase__								=				store_two_classics(A     ,					A   )
     for i in range(len(A   )   ):
          full_adder(A     ,					A     ,					len(A   ) + i     ,					len(A   ) + len(A   ) + i     ,					len(A   ) + len(A   ) + i + 1   )
          circuit.barrier()  # Optional, just for aesthetics

     # Measure the resultant qBits
     for i in range(len(A   ) + 1   ):
          circuit.measure([(len(A   ) * 2) + i]     ,					[i]   )

     lowercase__								=				qiskit.execute(A     ,					A     ,					shots=1   ).result()

     # The result is in binary. Convert it back to int
     return int(list(res.get_counts()   )[0]     ,					2   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




