
"""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()
_A      :	Dict       		=	logging.get_logger(__name__)

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

_A      :	List[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,
}

_A      :	Union[str, Any]       		=	list("""ARNDCQEGHILKMFPSTWYV""")

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


def   __magic_name__     (				)			->    int:
       with TemporaryDirectory() as tempdir:
              lowercase   :    List[str]			=							"\n".join(__snake_case  )
              lowercase   :    Dict			=							Path(__snake_case  ) / "vocab.txt"
              vocab_file.write_text(__snake_case  )
              lowercase   :    Optional[Any]			=							EsmTokenizer(vocab_file=str(__snake_case  )  )
       lowercase   :    List[Any]			=							0  # Overlaps with 'A' but that seems to be what they want
       return hf_tokenizer


def   __magic_name__     (				__snake_case    :	int					,			__snake_case    :	int  )			->    List[str]:
       lowercase   :    Any			=							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   __magic_name__     (				__snake_case    :	str					,			__snake_case    :	str					,			__snake_case    :	bool					,			__snake_case    :	str					,			__snake_case    :	str  )			->    str:
       if model.startswith("esmfold"  ):
              lowercase   :    List[str]			=							MODEL_MAPPING[model]()
       else:
              lowercase		,   lowercase   :    Union[str, Any]			=							MODEL_MAPPING[model]()
       esm.eval()  # disable dropout

       if model.startswith("esmfold"  ):
              lowercase   :    List[Any]			=							esm.esm.embed_dim
              lowercase   :    Tuple			=							esm.esm.num_layers
              lowercase   :    int			=							esm.esm.attention_heads
              lowercase   :    Optional[Any]			=							4 * embed_dim
              lowercase   :    List[Any]			=							esm.esm.token_dropout
              lowercase   :    Dict			=							False  # This code path does not exist in ESM-2
              lowercase   :    Dict			=							"rotary"
              lowercase   :    int			=							True
              lowercase   :    Union[str, Any]			=							EsmFoldConfig()
              for key, val in esm.cfg.items():
                     if hasattr(__snake_case					,			__snake_case  ) and key != "trunk":
                            setattr(__snake_case					,			__snake_case					,			__snake_case  )
              for key, val in esm.cfg.trunk.items():
                     if hasattr(esmfold_config.trunk					,			__snake_case  ) and key != "structure_module":
                            setattr(esmfold_config.trunk					,			__snake_case					,			__snake_case  )
              for key, val in esm.cfg.trunk.structure_module.items():
                     if hasattr(esmfold_config.trunk.structure_module					,			__snake_case  ):
                            setattr(esmfold_config.trunk.structure_module					,			__snake_case					,			__snake_case  )
       elif hasattr(__snake_case					,			"args"  ):
              # Indicates an ESM-1b or ESM-1v model
              lowercase   :    Optional[Any]			=							esm.args.embed_dim
              lowercase   :    Tuple			=							esm.args.layers
              lowercase   :    int			=							esm.args.attention_heads
              lowercase   :    Union[str, Any]			=							esm.args.ffn_embed_dim
              lowercase   :    List[str]			=							esm.args.token_dropout
              lowercase   :    Dict			=							True if esm.emb_layer_norm_before else False
              lowercase   :    Tuple			=							"absolute"
              lowercase   :    List[Any]			=							False
              lowercase   :    Union[str, Any]			=							None
       else:
              # Indicates an ESM-2 model
              lowercase   :    Tuple			=							esm.embed_dim
              lowercase   :    Dict			=							esm.num_layers
              lowercase   :    str			=							esm.attention_heads
              lowercase   :    int			=							4 * embed_dim  # This is hardcoded in ESM-2
              lowercase   :    Optional[int]			=							esm.token_dropout
              lowercase   :    str			=							False  # This code path does not exist in ESM-2
              lowercase   :    Union[str, Any]			=							"rotary"
              lowercase   :    Dict			=							False
              lowercase   :    Optional[int]			=							None

       if is_folding_model:
              lowercase   :    Dict			=							esm.esm.alphabet
       lowercase   :    Union[str, Any]			=							tuple(alphabet.all_toks  )
       lowercase   :    Optional[Any]			=							alphabet.mask_idx
       lowercase   :    Dict			=							alphabet.padding_idx

       if is_folding_model:
              lowercase   :    Union[str, Any]			=							esm.esm
       else:
              lowercase   :    Any			=							esm

       lowercase   :    List[Any]			=							EsmConfig(
           vocab_size=original_esm_model.embed_tokens.num_embeddings					,			mask_token_id=__snake_case					,			hidden_size=__snake_case					,			num_hidden_layers=__snake_case					,			num_attention_heads=__snake_case					,			intermediate_size=__snake_case					,			max_position_embeddings=1026					,			layer_norm_eps=1E-5					,			attention_probs_dropout_prob=0.0					,			hidden_dropout_prob=0.0					,			pad_token_id=__snake_case					,			emb_layer_norm_before=__snake_case					,			token_dropout=__snake_case					,			position_embedding_type=__snake_case					,			is_folding_model=__snake_case					,			esmfold_config=__snake_case					,			vocab_list=__snake_case					,			)
       if classification_head:
              lowercase   :    Any			=							esm.classification_heads["mnli"].out_proj.weight.shape[0]
       print("Our ESM config:"					,			__snake_case  )

       if model.startswith("esmfold"  ):
              lowercase   :    List[Any]			=							EsmForProteinFolding
       elif classification_head:
              lowercase   :    Optional[int]			=							EsmForSequenceClassification
       else:
              lowercase   :    str			=							EsmForMaskedLM
       lowercase   :    Union[str, Any]			=							model_class(__snake_case  )
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       lowercase   :    Union[str, Any]			=							original_esm_model.embed_tokens.weight
       if position_embedding_type == "absolute":
              lowercase   :    Tuple			=							original_esm_model.embed_positions.weight

       if config.emb_layer_norm_before:
              lowercase   :    List[Any]			=							original_esm_model.emb_layer_norm_before.weight
              lowercase   :    Optional[int]			=							original_esm_model.emb_layer_norm_before.bias

       lowercase   :    Any			=							original_esm_model.emb_layer_norm_after.weight
       lowercase   :    List[str]			=							original_esm_model.emb_layer_norm_after.bias

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

              # self attention
              lowercase   :    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)  )
              )

              lowercase   :    str			=							esm_layer.self_attn.q_proj.weight
              lowercase   :    Optional[int]			=							esm_layer.self_attn.q_proj.bias
              lowercase   :    List[str]			=							esm_layer.self_attn.k_proj.weight
              lowercase   :    str			=							esm_layer.self_attn.k_proj.bias
              lowercase   :    str			=							esm_layer.self_attn.v_proj.weight
              lowercase   :    str			=							esm_layer.self_attn.v_proj.bias

              if getattr(esm_layer.self_attn					,			"rot_emb"					,			__snake_case  ) 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   :    List[Any]			=							esm_layer.self_attn.rot_emb.inv_freq

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

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

              # intermediate
              lowercase   :    EsmIntermediate			=							layer.intermediate
              assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
              lowercase   :    int			=							esm_layer.fca.weight
              lowercase   :    Dict			=							esm_layer.fca.bias

              # output
              lowercase   :    EsmOutput			=							layer.output
              assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
              lowercase   :    Tuple			=							esm_layer.fca.weight
              lowercase   :    Union[str, Any]			=							esm_layer.fca.bias
              # end of layer

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

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

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

              if is_folding_model:
                     lowercase   :    Any			=							torch.max(torch.abs(our_output["positions"] - their_output["positions"]  )  ).item()
                     lowercase   :    List[str]			=							torch.allclose(our_output["positions"]					,			their_output["positions"]					,			atol=1E-5  )
              else:
                     lowercase   :    Any			=							torch.max(torch.abs(our_output - their_output  )  ).item()
                     lowercase   :    Any			=							torch.allclose(__snake_case					,			__snake_case					,			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   :    Tuple			=							model.predict_contacts(hf_tokens["input_ids"]					,			hf_tokens["attention_mask"]  )
                     lowercase   :    Optional[int]			=							esm.predict_contacts(hf_tokens["input_ids"]  )
                     lowercase   :    Optional[Any]			=							torch.max(torch.abs(our_output - their_output  )  ).item()
                     lowercase   :    Union[str, Any]			=							torch.allclose(__snake_case					,			__snake_case					,			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(__snake_case  ).mkdir(parents=__snake_case					,			exist_ok=__snake_case  )
              print(f"""Saving model to {pytorch_dump_folder_path}"""  )
              model.save_pretrained(__snake_case  )

              del esm  # Free up some memory before continuing

       print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""  )
       hf_tokenizer.save_pretrained(__snake_case  )

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


if __name__ == "__main__":
   _A      :	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.""")
   _A      :	Tuple       		=	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 math
from collections.abc import Generator


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    Generator			=							(i for i in range(1					,			(max_n + 1)  ))
       for i in (n for n in numbers if n > 1):
              for j in range(2					,			__snake_case  ):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    Generator			=							(i for i in range(1					,			(max_n + 1)  ))
       for i in (n for n in numbers if n > 1):
              # only need to check for factors up to sqrt(i)
              lowercase   :    Dict			=							int(math.sqrt(__snake_case  )  ) + 1
              for j in range(2					,			__snake_case  ):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    Generator			=							(i for i in range(1					,			(max_n + 1)					,			2  ))
       # It's useless to test even numbers as they will not be prime
       if max_n > 2:
              yield 2  # Because 2 will not be tested, it's necessary to yield it now
       for i in (n for n in numbers if n > 1):
              lowercase   :    int			=							int(math.sqrt(__snake_case  )  ) + 1
              for j in range(3					,			__snake_case					,			2  ):
                     # As we removed the even numbers, we don't need them now
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				)			->    Tuple:
       from timeit import timeit

       lowercase   :    List[str]			=							"from __main__ import slow_primes, primes, fast_primes"
       print(timeit("slow_primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )
       print(timeit("primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )
       print(timeit("fast_primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )


if __name__ == "__main__":
   _A      :	List[Any]       		=	int(input("""Calculate primes up to:\n>> """).strip())
   for ret in primes(number):
      print(ret)
   benchmark()


