


'''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__     :  Dict    					=			logging.get_logger(__name__)

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

lowercase__     :  Optional[int]    					=			{
    '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__     :  int    					=			list('ARNDCQEGHILKMFPSTWYV')

lowercase__     :  List[Any]    					=			restypes + ['X']
lowercase__     :  Any    					=			restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']





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





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

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

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

    if is_folding_model:
        UpperCamelCase__      =			esm.esm
    else:
        UpperCamelCase__      =			esm

    UpperCamelCase__      =			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=10_26					,  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:
        UpperCamelCase__      =			esm.classification_heads["mnli"].out_proj.weight.shape[0]
    print("Our ESM config:"					,  lowerCAmelCase_	)

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

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

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

    UpperCamelCase__      =			original_esm_model.emb_layer_norm_after.weight
    UpperCamelCase__      =			original_esm_model.emb_layer_norm_after.bias

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

        # self attention
        UpperCamelCase__      =			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)	)
        )

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

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

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

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

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

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

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

        UpperCamelCase__      =			hf_tokenizer([row[1] for row in sample_data]					,  return_tensors="pt"					,  padding=lowerCAmelCase_	)
        UpperCamelCase__      =			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.
            UpperCamelCase__      =			esm.cuda().infer([row[1] for row in sample_data]	)
            UpperCamelCase__      =			model.cuda()(
                input_ids=hf_tokens["input_ids"].cuda()					,  attention_mask=hf_tokens["attention_mask"].cuda()	)
        else:
            UpperCamelCase__      =			model(**lowerCAmelCase_					,  output_hidden_states=lowerCAmelCase_	)
            UpperCamelCase__      =			our_output["logits"]
            if classification_head:
                UpperCamelCase__      =			esm.model.classification_heads["mnli"](esm.extract_features(lowerCAmelCase_	)	)
            else:
                UpperCamelCase__      =			esm(hf_tokens["input_ids"]					,  repr_layers=list(range(9_99	)	)	)
                UpperCamelCase__      =			their_output["logits"]

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







from math import radians as angle_to_radians
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
lowercase__     :  str    					=			9.8_0_6_6_5





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):

    # Ensure valid instance
    if not isinstance(lowerCAmelCase_					,  (int, float)	):
        raise TypeError("Invalid velocity. Should be a positive number."	)

    if not isinstance(lowerCAmelCase_					,  (int, float)	):
        raise TypeError("Invalid angle. Range is 1-90 degrees."	)

    # Ensure valid angle
    if angle > 90 or angle < 1:
        raise ValueError("Invalid angle. Range is 1-90 degrees."	)

    # Ensure valid velocity
    if init_velocity < 0:
        raise ValueError("Invalid velocity. Should be a positive number."	)





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):
    check_args(lowerCAmelCase_					,  lowerCAmelCase_	)
    UpperCamelCase__      =			angle_to_radians(2 * angle	)
    return round(init_velocity**2 * sin(lowerCAmelCase_	) / g					,  2	)





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):
    check_args(lowerCAmelCase_					,  lowerCAmelCase_	)
    UpperCamelCase__      =			angle_to_radians(lowerCAmelCase_	)
    return round(init_velocity**2 * sin(lowerCAmelCase_	) ** 2 / (2 * g)					,  2	)





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):
    check_args(lowerCAmelCase_					,  lowerCAmelCase_	)
    UpperCamelCase__      =			angle_to_radians(lowerCAmelCase_	)
    return round(2 * init_velocity * sin(lowerCAmelCase_	) / g					,  2	)





def 						snake_case__ ():
    UpperCamelCase__    ,  UpperCamelCase__      =			25, 20
    assert horizontal_distance(lowerCAmelCase_					,  lowerCAmelCase_	) == 40.97
    assert max_height(lowerCAmelCase_					,  lowerCAmelCase_	) == 3.73
    assert total_time(lowerCAmelCase_					,  lowerCAmelCase_	) == 1.74


if __name__ == "__main__":
      from doctest import testmod

      testmod()

      # Get input from user
      lowercase__     :  Union[str, Any]    					=			float(input('Initial Velocity: ').strip())

      # Get input from user
      lowercase__     :  Any    					=			float(input('angle: ').strip())

      # Print results
      print()
      print('Results: ')
      print(F'''Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]''')
      print(F'''Maximum Height: {max_height(init_vel, angle)!s} [m]''')
      print(F'''Total Time: {total_time(init_vel, angle)!s} [s]''')




