





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
     )



import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
	from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
	raise ImportError('You need to install the version of fairseq from the Mega repo!')





class 		__lowerCamelCase     (   nn.Module		):



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								super().__init__()
								UpperCAmelCase:					int 						=     mega_args
								UpperCAmelCase:					Union[str, Any] 						=     nn.Embedding(__snake_case	,				self.mega_args.encoder_embed_dim							)
								UpperCAmelCase:					List[str] 						=     nn.ModuleList([MegaEncoderLayer(self.mega_args							) for _ in range(__snake_case							)]							)
								UpperCAmelCase:					List[str] 						=     depth




							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case=True	,				__snake_case=0							)						->     List[str]:






								"""simple docstring"""



								if batch_first:
									UpperCAmelCase:					int 						=     input_ids.T

								# to make things more confusing, Mega expects the attention mask to
								# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
								# which is the opposite of what HF returns
								if ignore_mask_value == 0:
									UpperCAmelCase:					Union[str, Any] 						=     1 - attention_mask

								# get token embeddings from IDs
								UpperCAmelCase:					Dict 						=     self.embedding_layer(__snake_case							)

								# pass through the Mega layers
								# input is (time, batch, encoder dim) and output is the same
								for encoder in self.encoders:
									UpperCAmelCase:					Any 						=     encoder(__snake_case	,				__snake_case							)

								# return according to the shape specified
								if batch_first:
									# (T, B, H) --> (B, T, H)
									return torch.transpose(__snake_case	,				0	,				1							)
								else:
									return embeds





class 		__lowerCamelCase     (   nn.Module		):



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								super().__init__()
								UpperCAmelCase:					int 						=     MegaLM(__snake_case	,				__snake_case	,				__snake_case							)
								UpperCAmelCase:					Tuple 						=     nn.Linear(mega_args.encoder_embed_dim	,				__snake_case							)
								UpperCAmelCase:					List[str] 						=     nn.Dropout(p=0.1							)




							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case=True	,				__snake_case=0							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     self.mega(__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)
								return self.mlm_head(self.dropout(__snake_case							)							)



def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				,					snake_case_							:		Dict				,					snake_case_							:		Tuple				):



	'''simple docstring'''



	with open(os.path.join(snake_case_				,					"model_args.pkl"				)				,					"rb"				) as f:
		UpperCAmelCase:					Union[str, Any] 						=     pkl.load(snake_case_				)

	# load the original encoder
	UpperCAmelCase:					Tuple 						=     OriginalMegaForMaskedLM(**snake_case_				).eval()

	# load its weights
	print(
	    "Original Mega encoder:"				,					original_mlm.mega.load_state_dict(
	        torch.load(os.path.join(snake_case_				,					"encoder_weights.pt"				)				,					map_location="cpu"				)				)				,					)
	print(
	    "Original Mega MLM layer:"				,					original_mlm.mlm_head.load_state_dict(
	        torch.load(os.path.join(snake_case_				,					"mlm_head_weights.pt"				)				,					map_location="cpu"				)				)				,					)

	# create a new config from the old one
	UpperCAmelCase:					int 						=     MegaConfig(
	    num_hidden_layers=mega_original_args["depth"]				,					vocab_size=mega_original_args["vocab_size"]				,					hidden_size=mega_original_args["mega_args"].encoder_embed_dim				,					shared_representation_size=mega_original_args["mega_args"].encoder_z_dim				,					intermediate_size=mega_original_args["mega_args"].encoder_hidden_dim				,					ema_projection_size=mega_original_args["mega_args"].encoder_n_dim				,					dropout_prob=mega_original_args["mega_args"].dropout				,					attention_probs_dropout_prob=mega_original_args["mega_args"].attention_dropout				,					hidden_dropout_prob=mega_original_args["mega_args"].hidden_dropout				,					activation=mega_original_args["mega_args"].activation_fn				,					attention_activation=mega_original_args["mega_args"].attention_activation_fn				,					bidirectional=mega_original_args["mega_args"].bidirectional				,					use_chunking=mega_original_args["mega_args"].encoder_chunk_size > 0				,					chunk_size=mega_original_args["mega_args"].encoder_chunk_size				,					truncation=mega_original_args["mega_args"].truncation_length				,					normalization_type=mega_original_args["mega_args"].normalization_type				,					normalize_before_mega=snake_case_				,					norm_affine=snake_case_				,					use_feature_dropout=mega_original_args["mega_args"].feature_dropout				,					relative_positional_bias=mega_original_args["mega_args"].rel_pos_bias				,					max_positions=mega_original_args["mega_args"].max_source_positions				,					nffn_hidden_size=mega_original_args["mega_args"].encoder_ffn_embed_dim				,					normalize_before_ffn=mega_original_args["mega_args"].normalize_before				,					nffn_activation_dropout_prob=0.0				,					add_token_type_embeddings=snake_case_				,					add_lm_hidden_dense_layer=snake_case_				,					)

	UpperCAmelCase:					Union[str, Any] 						=     MegaForMaskedLM(snake_case_				).eval()

	# the originl checkpoint just uses nn.Embedding for the word embeddings
	# we use a wrapper module for embeddings to add support for positional embeddings
	UpperCAmelCase:					str 						=     original_mlm.mega.embedding_layer.weight

	# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
	# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
	# also renaming previously confusing parameter names
	UpperCAmelCase:					List[str] 						=     original_mlm.mega.encoders.state_dict()
	UpperCAmelCase:					Optional[Any] 						=     {}
	for module_name in original_state_dict.keys():
		UpperCAmelCase:					Optional[Any] 						=     None
		# have to handle gamma, beta, and alpha differently due to their use
		# in multiple modules within the original repository;
		# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
		# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
		if "beta" in module_name:
			# EMA sub-layers were always called "move" in the original repo
			if "move.beta" in module_name:
				UpperCAmelCase:					Any 						=     module_name.replace("move.beta"				,					"ema_gate.ema_expansion_matrix"				)
			elif "mega_layer.beta" in module_name:
				UpperCAmelCase:					Optional[int] 						=     module_name.replace("beta"				,					"qk_bias"				)
			else:
				UpperCAmelCase:					List[str] 						=     module_name.replace("beta"				,					"b_param"				)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
		elif "gamma" in module_name:
			if "move.gamma" in module_name:
				UpperCAmelCase:					Union[str, Any] 						=     module_name.replace("move.gamma"				,					"ema_gate.kernel_projection_matrix"				)
			elif "mega_layer.gamma" in module_name:
				UpperCAmelCase:					Any 						=     module_name.replace("gamma"				,					"qk_weight"				)
			else:
				UpperCAmelCase:					List[Any] 						=     module_name.replace("gamma"				,					"g_param"				)
        # alpha is used in EMA and positional bias; renaming to improve readability
		elif "move.alpha" in module_name:
			UpperCAmelCase:					Tuple 						=     module_name.replace("move.alpha"				,					"ema_gate.decay_factor"				)
		# delta is only used in EMA; renaming to improve readability
		elif "move.delta" in module_name:
			UpperCAmelCase:					Any 						=     module_name.replace("move.delta"				,					"ema_gate.damping_factor"				)
		# omega is only used in EMA; renaming to improve readability
		elif "omega" in module_name:
			UpperCAmelCase:					Optional[int] 						=     module_name.replace("move.omega"				,					"ema_gate.residual_weight"				)

		if new_module_name:
			UpperCAmelCase:					List[Any] 						=     new_module_name

	if len(snake_case_				) != 0:
		print(F'Renaming these keys: {updated_keys.keys()}'				)
	else:
		print("No need to rename state dict entries"				)
	for old, new in updated_keys.items():
		UpperCAmelCase:					str 						=     original_state_dict.pop(snake_case_				)

	# now attempt to load the state dictionary with updated names
	# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
	print("HF Mega encoder:"				,					hf_mlm.mega.layers.load_state_dict(snake_case_				)				)

	# load the MLM head weights directly
	print(
	    "HF Mega MLM layer:"				,					hf_mlm.mlm_head.load_state_dict(
	        torch.load(os.path.join(snake_case_				,					"mlm_head_weights.pt"				)				,					map_location="cpu"				)				)				,					)

	# test on a randomly generated input sequence
	UpperCAmelCase:					Optional[int] 						=     torch.randint(0				,					hf_config.vocab_size				,					size=(4, 2_5_6)				)
	UpperCAmelCase:					int 						=     torch.ones_like(snake_case_				)
	# mask a few tokens to make sure masking is applied appropriately :)
	UpperCAmelCase:					int 						=     0

	# run forward passes
	UpperCAmelCase:					Optional[int] 						=     original_mlm(snake_case_				,					snake_case_				,					batch_first=snake_case_				,					ignore_mask_value=0				)
	UpperCAmelCase:					Optional[int] 						=     hf_mlm(snake_case_				,					snake_case_				)[0]

	# print shapes and diff
	print(F'original output {original_output.shape}'				)
	print(F'hf output {hf_output.shape}'				)
	print(F'max diff: {(original_output - hf_output).max()}'				)  # 0.0
	UpperCAmelCase:					Tuple 						=     torch.allclose(snake_case_				,					snake_case_				,					atol=1e-3				)

	if success:
		print("Yay!"				)
		hf_mlm.save_pretrained(snake_case_				)
	else:
		raise RuntimeError(F'Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'				)

	if includes_tokenizer:
		print("Transferring tokenizer"				)
		UpperCAmelCase:					List[str] 						=     AutoTokenizer.from_pretrained(snake_case_				)
		tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
	snake_case_      :					str				      =     argparse.ArgumentParser()

	parser.add_argument(
	    '--pretrained_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Point to the directory containing your model weights using the official Mega repo',
	)

	parser.add_argument(
	    '--output_path', default=None, type=str, required=True, help='Location to save the Hugging Face version'
	)

	parser.add_argument(
	    '--includes_tokenizer',
	    action='store_true',
	    help='Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo',
	)

	snake_case_      :					str				      =     parser.parse_args()

	convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)
