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()
lowerCAmelCase__	:  Optional[int]						=  logging.get_logger(__name__)

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

lowerCAmelCase__	:  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,
}

lowerCAmelCase__	:  Optional[Any]						=  list('''ARNDCQEGHILKMFPSTWYV''')

lowerCAmelCase__	:  List[Any]						=  restypes + ['''X''']
lowerCAmelCase__	:  Tuple						=  restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']
def 					UpperCamelCase__		(							)  ->   str:
				with TemporaryDirectory() as tempdir:
								snake_case__   :      int   				= '\n'.join(A__       )
								snake_case__   :      Union[str, Any]   				= Path(A__       ) / 'vocab.txt'
								vocab_file.write_text(A__       )
								snake_case__   :      int   				= EsmTokenizer(vocab_file=str(A__       )       )
				snake_case__   :      str   				= 0  # Overlaps with 'A' but that seems to be what they want
				return hf_tokenizer
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   List[str]:
				snake_case__   :      int   				= 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 					UpperCamelCase__		(							A__     ,					A__     ,					A__     ,					A__     ,					A__       )  ->   int:
				if model.startswith('esmfold'       ):
								snake_case__   :      Union[str, Any]   				= MODEL_MAPPING[model]()
				else:
								snake_case__				,    snake_case__   :      List[str]   				= MODEL_MAPPING[model]()
				esm.eval()  # disable dropout

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

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

				if is_folding_model:
								snake_case__   :      List[Any]   				= esm.esm
				else:
								snake_case__   :      List[str]   				= esm

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

				if model.startswith('esmfold'       ):
								snake_case__   :      str   				= EsmForProteinFolding
				elif classification_head:
								snake_case__   :      str   				= EsmForSequenceClassification
				else:
								snake_case__   :      Dict   				= EsmForMaskedLM
				snake_case__   :      Any   				= model_class(A__       )
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__   :      Any   				= original_esm_model.embed_tokens.weight
				if position_embedding_type == "absolute":
								snake_case__   :      List[Any]   				= original_esm_model.embed_positions.weight

				if config.emb_layer_norm_before:
								snake_case__   :      Any   				= original_esm_model.emb_layer_norm_before.weight
								snake_case__   :      List[str]   				= original_esm_model.emb_layer_norm_before.bias

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

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

								# self attention
								snake_case__   :      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)       )
								)

								snake_case__   :      List[Any]   				= esm_layer.self_attn.q_proj.weight
								snake_case__   :      str   				= esm_layer.self_attn.q_proj.bias
								snake_case__   :      Optional[Any]   				= esm_layer.self_attn.k_proj.weight
								snake_case__   :      Optional[Any]   				= esm_layer.self_attn.k_proj.bias
								snake_case__   :      Union[str, Any]   				= esm_layer.self_attn.v_proj.weight
								snake_case__   :      Any   				= esm_layer.self_attn.v_proj.bias

								if getattr(esm_layer.self_attn     ,					'rot_emb'     ,					A__       ) 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.
												snake_case__   :      Tuple   				= esm_layer.self_attn.rot_emb.inv_freq

								# LayerNorm changes for pre-activation
								snake_case__   :      Union[str, Any]   				= esm_layer.self_attn_layer_norm.weight
								snake_case__   :      Union[str, Any]   				= esm_layer.self_attn_layer_norm.bias
								snake_case__   :      str   				= esm_layer.final_layer_norm.weight
								snake_case__   :      Union[str, Any]   				= esm_layer.final_layer_norm.bias

								# self-attention output
								snake_case__   :      EsmSelfOutput   				= layer.attention.output
								assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
								snake_case__   :      Optional[Any]   				= esm_layer.self_attn.out_proj.weight
								snake_case__   :      Optional[Any]   				= esm_layer.self_attn.out_proj.bias

								# intermediate
								snake_case__   :      EsmIntermediate   				= layer.intermediate
								assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
								snake_case__   :      Any   				= esm_layer.fca.weight
								snake_case__   :      Optional[Any]   				= esm_layer.fca.bias

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

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

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

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

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

								del esm  # Free up some memory before continuing

				print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""       )
				hf_tokenizer.save_pretrained(A__       )

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


if __name__ == "__main__":
				lowerCAmelCase__	:  List[Any]						=  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.''')
				lowerCAmelCase__	:  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
				)



from typing import TYPE_CHECKING

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


lowerCAmelCase__	:  int						=  {
    '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Union[str, Any]						=  ['''DetaImageProcessor''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Optional[int]						=  [
				    '''DETA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DetaForObjectDetection''',
				    '''DetaModel''',
				    '''DetaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

				lowerCAmelCase__	:  int						=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


