





import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_=False       ,		UpperCamelCase_=False       ,		UpperCamelCase_="data2vec."	)  ->		Any:
       UpperCamelCase_ 							=	"backbone." if is_semantic else ""

       UpperCamelCase_ 							=	[]
       for i in range(config.num_hidden_layers	):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append(
                  (F'''{prefix}blocks.{i}.norm1.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')	)
              rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')	)
              rename_keys.append(
                  (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')	)
              rename_keys.append(
                  (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')	)
              rename_keys.append(
                  (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')	)
              rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')	)
              rename_keys.append(
                  (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')	)
              rename_keys.append(
                  (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')	)
              rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')	)
              rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')	)

       # projection layer + position embeddings
       rename_keys.extend(
           [
               (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
               (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
               (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
           ]	)

       if has_lm_head:
              # mask token + shared relative position bias + layernorm
              rename_keys.extend(
                  [
                      ("mask_token", F'''{hf_prefix}embeddings.mask_token'''),
                      (
                          "rel_pos_bias.relative_position_bias_table",
                          F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
                      ),
                      (
                          "rel_pos_bias.relative_position_index",
                          F'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
                      ),
                      ("norm.weight", "layernorm.weight"),
                      ("norm.bias", "layernorm.bias"),
                  ]	)
       elif is_semantic:
              # semantic segmentation classification heads
              rename_keys.extend(
                  [
                      ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
                      ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
                      ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
                      ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
                  ]	)
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ("fc_norm.weight", F'''{hf_prefix}pooler.layernorm.weight'''),
                      ("fc_norm.bias", F'''{hf_prefix}pooler.layernorm.bias'''),
                      ("head.weight", "classifier.weight"),
                      ("head.bias", "classifier.bias"),
                  ]	)

       return rename_keys




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=False       ,		UpperCamelCase_=False       ,		UpperCamelCase_="data2vec_vision."	)  ->		int:
       for i in range(config.num_hidden_layers	):
              UpperCamelCase_ 							=	"backbone." if is_semantic else ""
              # queries, keys and values
              UpperCamelCase_ 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''	)
              UpperCamelCase_ 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''	)
              UpperCamelCase_ 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''	)

              UpperCamelCase_ 							=	in_proj_weight[
                  : config.hidden_size, :
              ]
              UpperCamelCase_ 							=	q_bias
              UpperCamelCase_ 							=	in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              UpperCamelCase_ 							=	in_proj_weight[
                  -config.hidden_size :, :
              ]
              UpperCamelCase_ 							=	v_bias

              # gamma_1 and gamma_2
              # we call them lambda because otherwise they are renamed when using .from_pretrained
              UpperCamelCase_ 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''	)
              UpperCamelCase_ 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''	)

              UpperCamelCase_ 							=	gamma_a
              UpperCamelCase_ 							=	gamma_a

              # relative_position bias table + index
              if not has_lm_head:
                     # each layer has its own relative position bias
                     UpperCamelCase_ 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''	)
                     UpperCamelCase_ 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''	)

                     UpperCamelCase_ 							=	table
                     UpperCamelCase_ 							=	index




def 			lowerCAmelCase_			(				)  ->		List[str]:
       UpperCamelCase_ 							=	argparse.ArgumentParser(
           "Convert Data2VecVision to HF for image classification and pretraining"       ,		add_help=UpperCamelCase_	)
       parser.add_argument("--hf_checkpoint_name"       ,		type=UpperCamelCase_	)
       parser.add_argument("--input_size"       ,		default=224       ,		type=UpperCamelCase_       ,		help="images input size"	)
       parser.add_argument("--beit_checkpoint"       ,		default=""       ,		help="beit checkpoint"	)

       return parser.parse_args()




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Dict:
       def load_state_dict(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=""       ,		UpperCamelCase_="relative_position_index"	):
              UpperCamelCase_ 							=	[]
              UpperCamelCase_ 							=	[]
              UpperCamelCase_ 							=	[]
              # copy state_dict so _load_from_state_dict can modify it
              UpperCamelCase_ 							=	getattr(UpperCamelCase_       ,		"_metadata"       ,		UpperCamelCase_	)
              UpperCamelCase_ 							=	state_dict.copy()
              if metadata is not None:
                     UpperCamelCase_ 							=	metadata

              def load(UpperCamelCase_       ,		UpperCamelCase_=""	):
                     UpperCamelCase_ 							=	{} if metadata is None else metadata.get(prefix[:-1]       ,		{}	)
                     module._load_from_state_dict(
                         UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)
                     for name, child in module._modules.items():
                            if child is not None:
                                   load(UpperCamelCase_       ,		prefix + name + "."	)

              load(UpperCamelCase_       ,		prefix=UpperCamelCase_	)

              UpperCamelCase_ 							=	[]
              UpperCamelCase_ 							=	[]
              for key in missing_keys:
                     UpperCamelCase_ 							=	True
                     for ignore_key in ignore_missing.split("|"	):
                            if ignore_key in key:
                                   UpperCamelCase_ 							=	False
                                   break
                     if keep_flag:
                            warn_missing_keys.append(UpperCamelCase_	)
                     else:
                            ignore_missing_keys.append(UpperCamelCase_	)

              UpperCamelCase_ 							=	warn_missing_keys

              if len(UpperCamelCase_	) > 0:
                     print(
                         "Weights of {} not initialized from pretrained model: {}".format(
                             model.__class__.__name__       ,		UpperCamelCase_	)	)
              if len(UpperCamelCase_	) > 0:
                     print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__       ,		UpperCamelCase_	)	)
              if len(UpperCamelCase_	) > 0:
                     print(
                         "Ignored weights of {} not initialized from pretrained model: {}".format(
                             model.__class__.__name__       ,		UpperCamelCase_	)	)
              if len(UpperCamelCase_	) > 0:
                     print("\n".join(UpperCamelCase_	)	)

       UpperCamelCase_ 							=	{
           "pretrained": False,
           "use_shared_rel_pos_bias": True,
           "use_abs_pos_emb": False,
           "init_values": 0.1,
       }

       if is_finetuned:
              model_kwargs.update(
                  {
                      "num_classes": 1000,
                      "use_mean_pooling": True,
                      "init_scale": 0.0_01,
                      "use_rel_pos_bias": True,
                  }	)

       UpperCamelCase_ 							=	create_model(
           "beit_large_patch16_224" if is_large else "beit_base_patch16_224"       ,		**UpperCamelCase_       ,		)
       UpperCamelCase_ 							=	model.patch_embed.patch_size
       UpperCamelCase_ 							=	(args.input_size // patch_size[0], args.input_size // patch_size[1])
       UpperCamelCase_ 							=	torch.load(args.beit_checkpoint       ,		map_location="cpu"	)

       print(F'''Load ckpt from {args.beit_checkpoint}'''	)
       UpperCamelCase_ 							=	None
       for model_key in ("model", "module"):
              if model_key in checkpoint:
                     UpperCamelCase_ 							=	checkpoint[model_key]
                     print(F'''Load state_dict by model_key = {model_key}'''	)
                     break

       UpperCamelCase_ 							=	list(checkpoint_model.keys()	)
       for key in all_keys:
              if "relative_position_index" in key:
                     checkpoint_model.pop(UpperCamelCase_	)

              if "relative_position_bias_table" in key:
                     UpperCamelCase_ 							=	checkpoint_model[key]
                     UpperCamelCase_							,			UpperCamelCase_ 							=	rel_pos_bias.size()
                     UpperCamelCase_							,			UpperCamelCase_ 							=	model.state_dict()[key].size()
                     UpperCamelCase_ 							=	model.patch_embed.patch_shape
                     if dst_patch_shape[0] != dst_patch_shape[1]:
                            raise NotImplementedError()

       load_state_dict(UpperCamelCase_       ,		UpperCamelCase_       ,		prefix=""	)

       return model




def 			lowerCAmelCase_			(				)  ->		Union[str, Any]:
       UpperCamelCase_ 							=	get_args()

       UpperCamelCase_ 							=	"ft1k" in args.hf_checkpoint_name
       UpperCamelCase_ 							=	"large" in args.hf_checkpoint_name

       if is_finetuned:
              # To convert Beit's data2vec_vision to HF you need to copy
              # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
              # into this folder.
              import modeling_finetune  # noqa: F401
       else:
              # To convert Beit's data2vec_vision to HF you need to copy
              # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
              # into this folder
              # IMPORTANT: Note that for now we've only converted the down-stream
              # model and not the full pretrained model. This means for the integration
              # test you need to add a `return x` after the following line:
              # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
              # to make the integration test pass.
              import modeling_cyclical  # noqa: F401

       # 1. Create model config
       UpperCamelCase_ 							=	DataaVecVisionConfig()
       if is_finetuned:
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	1000

              UpperCamelCase_ 							=	"huggingface/label-files"
              UpperCamelCase_ 							=	"imagenet-1k-id2label.json"
              UpperCamelCase_ 							=	json.load(open(hf_hub_download(UpperCamelCase_       ,		UpperCamelCase_       ,		repo_type="dataset"	)       ,		"r"	)	)
              UpperCamelCase_ 							=	{int(UpperCamelCase_	): v for k, v in idalabel.items()}
              UpperCamelCase_ 							=	idalabel
              UpperCamelCase_ 							=	{v: k for k, v in idalabel.items()}
       else:
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	False

       if is_large:
              UpperCamelCase_ 							=	1024
              UpperCamelCase_ 							=	4096
              UpperCamelCase_ 							=	24
              UpperCamelCase_ 							=	16

       # 2. Load Beit model
       UpperCamelCase_ 							=	load_beit_model(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)
       orig_model.eval()

       # 3. Forward Beit model
       UpperCamelCase_ 							=	BeitImageProcessor(size=config.image_size       ,		do_center_crop=UpperCamelCase_	)
       UpperCamelCase_ 							=	Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"	)
       UpperCamelCase_ 							=	image_processor(images=UpperCamelCase_       ,		return_tensors="pt"	)
       UpperCamelCase_ 							=	encoding["pixel_values"]

       UpperCamelCase_ 							=	(pixel_values,) if is_finetuned else (pixel_values, None)
       with torch.no_grad():
              UpperCamelCase_ 							=	orig_model(*UpperCamelCase_	)

       # 4. Load HF Data2VecVision model
       if is_finetuned:
              UpperCamelCase_ 							=	DataaVecVisionForImageClassification(UpperCamelCase_	)
              hf_model.eval()
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	"data2vec_vision."
       else:
              UpperCamelCase_ 							=	DataaVecVisionModel(UpperCamelCase_	)
              hf_model.eval()
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	""

       UpperCamelCase_ 							=	create_rename_keys(UpperCamelCase_       ,		hf_prefix=UpperCamelCase_       ,		has_lm_head=UpperCamelCase_	)
       UpperCamelCase_ 							=	orig_model.state_dict()
       for src, dest in rename_keys:
              UpperCamelCase_ 							=	state_dict.pop(UpperCamelCase_	)
              UpperCamelCase_ 							=	val

       read_in_q_k_v(UpperCamelCase_       ,		UpperCamelCase_       ,		hf_prefix=UpperCamelCase_       ,		has_lm_head=UpperCamelCase_	)
       UpperCamelCase_							,			UpperCamelCase_ 							=	hf_model.load_state_dict(UpperCamelCase_       ,		strict=UpperCamelCase_	)
       print("HF missing"       ,		UpperCamelCase_	)
       print("HF unexpected_keys"       ,		UpperCamelCase_	)

       # 5. Forward HF Data2VecVision model
       with torch.no_grad():
              UpperCamelCase_ 							=	hf_model(UpperCamelCase_	)

       UpperCamelCase_ 							=	hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

       # 6. Compare
       UpperCamelCase_ 							=	torch.max(torch.abs(hf_output - orig_model_output	)	).item()

       print(F'''max_absolute_diff = {max_absolute_diff}'''	)
       UpperCamelCase_ 							=	torch.allclose(UpperCamelCase_       ,		UpperCamelCase_       ,		atol=1e-3	)
       print("Do both models output the same tensors?"       ,		"🔥" if success else "💩"	)
       if not success:
              raise Exception("Something went wRoNg"	)

       # 7. Save
       print(F'''Saving to {args.hf_checkpoint_name}'''	)
       hf_model.save_pretrained(args.hf_checkpoint_name	)
       image_processor.save_pretrained(args.hf_checkpoint_name	)


if __name__ == "__main__":
     main()
     # Run the following to convert checkpoints
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./pretrained_base.pt \
     #          --hf_checkpoint_name "./data2vec-vision-base"
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./finetuned_base.pt \
     #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./pretrained_large.pt \
     #          --hf_checkpoint_name "./data2vec-vision-large"
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./finetuned_large.pt \
     #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"








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										=					logging.get_logger(__name__)

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

_UpperCAmelCase										=					{
    '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										=					list('ARNDCQEGHILKMFPSTWYV')

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




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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[str]:
       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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       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(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
              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=UpperCamelCase_       ,		hidden_size=UpperCamelCase_       ,		num_hidden_layers=UpperCamelCase_       ,		num_attention_heads=UpperCamelCase_       ,		intermediate_size=UpperCamelCase_       ,		max_position_embeddings=1026       ,		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:
              UpperCamelCase_ 							=	esm.classification_heads["mnli"].out_proj.weight.shape[0]
       print("Our ESM config:"       ,		UpperCamelCase_	)

       if model.startswith("esmfold"	):
              UpperCamelCase_ 							=	EsmForProteinFolding
       elif classification_head:
              UpperCamelCase_ 							=	EsmForSequenceClassification
       else:
              UpperCamelCase_ 							=	EsmForMaskedLM
       UpperCamelCase_ 							=	model_class(UpperCamelCase_	)
       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"       ,		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.
                     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=UpperCamelCase_       ,		add_special_tokens=UpperCamelCase_	)
              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(UpperCamelCase_	)
              # Prepare tokenizer and make sure it matches
              with TemporaryDirectory() as tempdir:
                     UpperCamelCase_ 							=	"\n".join(alphabet.all_toks	)
                     UpperCamelCase_ 							=	Path(UpperCamelCase_	) / "vocab.txt"
                     vocab_file.write_text(UpperCamelCase_	)
                     UpperCamelCase_ 							=	EsmTokenizer(vocab_file=str(UpperCamelCase_	)	)

              UpperCamelCase_ 							=	hf_tokenizer([row[1] for row in sample_data]       ,		return_tensors="pt"       ,		padding=UpperCamelCase_	)
              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(**UpperCamelCase_       ,		output_hidden_states=UpperCamelCase_	)
                     UpperCamelCase_ 							=	our_output["logits"]
                     if classification_head:
                            UpperCamelCase_ 							=	esm.model.classification_heads["mnli"](esm.extract_features(UpperCamelCase_	)	)
                     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(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
                     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(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__":
     _UpperCAmelCase										=					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										=					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
     )

