






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

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

UpperCamelCase__						:      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,
}

UpperCamelCase__						:      Any								=					list("""ARNDCQEGHILKMFPSTWYV""")

UpperCamelCase__						:      str								=					restypes + ["""X"""]
UpperCamelCase__						:      List[Any]								=					restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]





def   SCREAMING_SNAKE_CASE__						(   )   ->  Tuple:


	"""simple docstring"""



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





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  List[str]:


	"""simple docstring"""



	a		=			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   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_,  snake_case_,  snake_case_  )   ->  Union[str, Any]:


	"""simple docstring"""



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

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

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

	if is_folding_model:
		a		=			esm.esm
	else:
		a		=			esm

	a		=			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=1_0_2_6,  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:
		a		=			esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
	print('''Our ESM config:''',  snake_case_  )

	if model.startswith('''esmfold'''  ):
		a		=			EsmForProteinFolding
	elif classification_head:
		a		=			EsmForSequenceClassification
	else:
		a		=			EsmForMaskedLM
	a		=			model_class(snake_case_  )
	model.eval()

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

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

	a		=			original_esm_model.emb_layer_norm_after.weight
	a		=			original_esm_model.emb_layer_norm_after.bias

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

		# self attention
		a		=			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)  )
		)

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

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

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

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

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

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

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

		a		=			hf_tokenizer([row[1] for row in sample_data],  return_tensors='''pt''',  padding=snake_case_  )
		a		=			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.
			a		=			esm.cuda().infer([row[1] for row in sample_data]  )
			a		=			model.cuda()(
			    input_ids=hf_tokens['''input_ids'''].cuda(),  attention_mask=hf_tokens['''attention_mask'''].cuda()  )
		else:
			a		=			model(**snake_case_,  output_hidden_states=snake_case_  )
			a		=			our_output['''logits''']
			if classification_head:
				a		=			esm.model.classification_heads['''mnli'''](esm.extract_features(snake_case_  )  )
			else:
				a		=			esm(hf_tokens['''input_ids'''],  repr_layers=list(range(9_9_9  )  )  )
				a		=			their_output['''logits''']

		if is_folding_model:
			a		=			torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']  )  ).item()
			a		=			torch.allclose(our_output['''positions'''],  their_output['''positions'''],  atol=1e-5  )
		else:
			a		=			torch.max(torch.abs(our_output - their_output  )  ).item()
			a		=			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
			a		=			model.predict_contacts(hf_tokens['''input_ids'''],  hf_tokens['''attention_mask''']  )
			a		=			esm.predict_contacts(hf_tokens['''input_ids''']  )
			a		=			torch.max(torch.abs(our_output - their_output  )  ).item()
			a		=			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__":
				UpperCamelCase__						:      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.""")
				UpperCamelCase__						:      Dict								=					parser.parse_args()
				convert_esm_checkpoint_to_pytorch(
				    args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
				)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__						:      Tuple								=					{
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						:      Any								=					[
				    """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """NystromformerForMaskedLM""",
				    """NystromformerForMultipleChoice""",
				    """NystromformerForQuestionAnswering""",
				    """NystromformerForSequenceClassification""",
				    """NystromformerForTokenClassification""",
				    """NystromformerLayer""",
				    """NystromformerModel""",
				    """NystromformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nystromformer import (
								    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NystromformerForMaskedLM,
								    NystromformerForMultipleChoice,
								    NystromformerForQuestionAnswering,
								    NystromformerForSequenceClassification,
								    NystromformerForTokenClassification,
								    NystromformerLayer,
								    NystromformerModel,
								    NystromformerPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__						:      str								=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





