






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   			=						logging.get_logger(__name__)

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

_a   			=						{
    '''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   			=						list('''ARNDCQEGHILKMFPSTWYV''')

_a   			=						restypes + ['''X''']
_a   			=						restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']





def __A					(   )->						Optional[Any]:





						"""simple docstring"""



						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 __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						int:





						"""simple docstring"""



						_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 __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						Tuple:





						"""simple docstring"""



						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=1_026				,	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(999			)			)			)
																								_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__":
		_a   			=						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   			=						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_torch_available


_a   			=						{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a   			=						[
		    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''QDQBertForMaskedLM''',
		    '''QDQBertForMultipleChoice''',
		    '''QDQBertForNextSentencePrediction''',
		    '''QDQBertForQuestionAnswering''',
		    '''QDQBertForSequenceClassification''',
		    '''QDQBertForTokenClassification''',
		    '''QDQBertLayer''',
		    '''QDQBertLMHeadModel''',
		    '''QDQBertModel''',
		    '''QDQBertPreTrainedModel''',
		    '''load_tf_weights_in_qdqbert''',
		]


if TYPE_CHECKING:
		from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_qdqbert import (
				    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    QDQBertForMaskedLM,
				    QDQBertForMultipleChoice,
				    QDQBertForNextSentencePrediction,
				    QDQBertForQuestionAnswering,
				    QDQBertForSequenceClassification,
				    QDQBertForTokenClassification,
				    QDQBertLayer,
				    QDQBertLMHeadModel,
				    QDQBertModel,
				    QDQBertPreTrainedModel,
				    load_tf_weights_in_qdqbert,
				)


else:
		import sys

		_a   			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
