
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()
snake_case__  							=							logging.get_logger(__name__)

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

snake_case__  							=							{
    """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,
}

snake_case__  							=							list("""ARNDCQEGHILKMFPSTWYV""")

snake_case__  							=							restypes + ["""X"""]
snake_case__  							=							restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]

def        lowerCamelCase_      (			):
						with TemporaryDirectory() as tempdir:
												lowercase       :    str											=  '''\n'''.join(UpperCAmelCase_       )
												lowercase       :    Dict											=  Path(UpperCAmelCase_       ) / '''vocab.txt'''
												vocab_file.write_text(UpperCAmelCase_       )
												lowercase       :    int											=  EsmTokenizer(vocab_file=str(UpperCAmelCase_       )       )
						lowercase       :    Union[str, Any]											=  0  # Overlaps with 'A' but that seems to be what they want
						return hf_tokenizer

def        lowerCamelCase_      (			UpperCAmelCase_							:				Union[str, Any]							,			UpperCAmelCase_							:				Tuple       ):
						lowercase       :    Optional[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        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				bool							,			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				str       ):
						if model.startswith('''esmfold'''       ):
												lowercase       :    Union[str, Any]											=  MODEL_MAPPING[model]()
						else:
												lowercase				,				lowercase       :    List[Any]											=  MODEL_MAPPING[model]()
						esm.eval()  # disable dropout

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

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

						if is_folding_model:
												lowercase       :    List[Any]											=  esm.esm
						else:
												lowercase       :    Dict											=  esm

						lowercase       :    List[str]											=  EsmConfig(
						    vocab_size=original_esm_model.embed_tokens.num_embeddings							,			mask_token_id=UpperCAmelCase_							,			hidden_size=UpperCAmelCase_							,			num_hidden_layers=UpperCAmelCase_							,			num_attention_heads=UpperCAmelCase_							,			intermediate_size=UpperCAmelCase_							,			max_position_embeddings=10_26							,			layer_norm_eps=1E-5							,			attention_probs_dropout_prob=0.0							,			hidden_dropout_prob=0.0							,			pad_token_id=UpperCAmelCase_							,			emb_layer_norm_before=UpperCAmelCase_							,			token_dropout=UpperCAmelCase_							,			position_embedding_type=UpperCAmelCase_							,			is_folding_model=UpperCAmelCase_							,			esmfold_config=UpperCAmelCase_							,			vocab_list=UpperCAmelCase_							,			)
						if classification_head:
												lowercase       :    List[str]											=  esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
						print('''Our ESM config:'''							,			UpperCAmelCase_       )

						if model.startswith('''esmfold'''       ):
												lowercase       :    Optional[Any]											=  EsmForProteinFolding
						elif classification_head:
												lowercase       :    Dict											=  EsmForSequenceClassification
						else:
												lowercase       :    List[str]											=  EsmForMaskedLM
						lowercase       :    List[str]											=  model_class(UpperCAmelCase_       )
						model.eval()

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

						if config.emb_layer_norm_before:
												lowercase       :    List[str]											=  original_esm_model.emb_layer_norm_before.weight
												lowercase       :    Union[str, Any]											=  original_esm_model.emb_layer_norm_before.bias

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

												if getattr(esm_layer.self_attn							,			'''rot_emb'''							,			UpperCAmelCase_       ) 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       :    Union[str, Any]											=  esm_layer.self_attn.rot_emb.inv_freq

												# LayerNorm changes for pre-activation
												lowercase       :    Optional[Any]											=  esm_layer.self_attn_layer_norm.weight
												lowercase       :    List[Any]											=  esm_layer.self_attn_layer_norm.bias
												lowercase       :    Any											=  esm_layer.final_layer_norm.weight
												lowercase       :    Optional[Any]											=  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       :    str											=  esm_layer.self_attn.out_proj.weight
												lowercase       :    Union[str, Any]											=  esm_layer.self_attn.out_proj.bias

												# intermediate
												lowercase       :    EsmIntermediate											=  layer.intermediate
												assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
												lowercase       :    List[str]											=  esm_layer.fca.weight
												lowercase       :    int											=  esm_layer.fca.bias

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

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

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

												lowercase       :    List[Any]											=  hf_tokenizer([row[1] for row in sample_data]							,			return_tensors='''pt'''							,			padding=UpperCAmelCase_       )
												lowercase       :    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.
																		lowercase       :    Union[str, Any]											=  esm.cuda().infer([row[1] for row in sample_data]       )
																		lowercase       :    str											=  model.cuda()(
																		    input_ids=hf_tokens['''input_ids'''].cuda()							,			attention_mask=hf_tokens['''attention_mask'''].cuda()       )
												else:
																		lowercase       :    List[Any]											=  model(**UpperCAmelCase_							,			output_hidden_states=UpperCAmelCase_       )
																		lowercase       :    Optional[Any]											=  our_output['''logits''']
																		if classification_head:
																								lowercase       :    Optional[int]											=  esm.model.classification_heads['''mnli'''](esm.extract_features(UpperCAmelCase_       )       )
																		else:
																								lowercase       :    List[str]											=  esm(hf_tokens['''input_ids''']							,			repr_layers=list(range(9_99       )       )       )
																								lowercase       :    List[Any]											=  their_output['''logits''']

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

												del esm  # Free up some memory before continuing

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

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


if __name__ == "__main__":
				snake_case__  							=							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.""")
				snake_case__  							=							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
				)




class 							UpperCAmelCase	:

					def __init__(			self      :					Any ,      lowerCAmelCase      :					int					):
											lowercase       :    Tuple											=  data
											lowercase       :    BinaryTreeNode | None											=  None
											lowercase       :    BinaryTreeNode | None											=  None





def        lowerCamelCase_      (			UpperCAmelCase_							:				BinaryTreeNode | None							,			UpperCAmelCase_							:				int       ):
						if node is None:
												lowercase       :    Union[str, Any]											=  BinaryTreeNode(UpperCAmelCase_       )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												lowercase       :    List[Any]											=  insert(node.left_child							,			UpperCAmelCase_       )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												lowercase       :    Dict											=  insert(node.right_child							,			UpperCAmelCase_       )
						return node

def        lowerCamelCase_      (			UpperCAmelCase_							:				None | BinaryTreeNode       ):  # if node is None,return
						if node:
												lowercase       :    Optional[Any]											=  inorder(node.left_child       )
												lowercase       :    Tuple											=  [*inorder_array, node.data]
												lowercase       :    Optional[Any]											=  inorder_array + inorder(node.right_child       )
						else:
												lowercase       :    Optional[int]											=  []
						return inorder_array

def        lowerCamelCase_      (			):
						lowercase       :    Any											=  insert(UpperCAmelCase_							,			15       )
						insert(UpperCAmelCase_							,			10       )
						insert(UpperCAmelCase_							,			25       )
						insert(UpperCAmelCase_							,			6       )
						insert(UpperCAmelCase_							,			14       )
						insert(UpperCAmelCase_							,			20       )
						insert(UpperCAmelCase_							,			60       )
						return root

def        lowerCamelCase_      (			):
						# main function
						lowercase       :    Any											=  make_tree()
						print('''Printing values of binary search tree in Inorder Traversal.'''       )
						inorder(UpperCAmelCase_       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()


