import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
__a  :		List[str]		     =   logging.get_logger(__name__)

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

__a  :		List[str]		     =   {
    '''esm1b_t33_650M_UR50S''': esm_module.pretrained.esm1b_t33_650M_UR50S,
    '''esm1v_t33_650M_UR90S_1''': esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    '''esm1v_t33_650M_UR90S_2''': esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    '''esm1v_t33_650M_UR90S_3''': esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    '''esm1v_t33_650M_UR90S_4''': esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    '''esm1v_t33_650M_UR90S_5''': esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    '''esm2_t48_15B_UR50D''': esm_module.pretrained.esm2_t48_15B_UR50D,
    '''esm2_t36_3B_UR50D''': esm_module.pretrained.esm2_t36_3B_UR50D,
    '''esm2_t33_650M_UR50D''': esm_module.pretrained.esm2_t33_650M_UR50D,
    '''esm2_t30_150M_UR50D''': esm_module.pretrained.esm2_t30_150M_UR50D,
    '''esm2_t12_35M_UR50D''': esm_module.pretrained.esm2_t12_35M_UR50D,
    '''esm2_t6_8M_UR50D''': esm_module.pretrained.esm2_t6_8M_UR50D,
    '''esmfold_v1''': esmfold_va,
}

__a  :		Dict		     =   list('''ARNDCQEGHILKMFPSTWYV''')

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

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

							if is_folding_model:
														lowercase__  :	List[str]        =							esm.esm.alphabet
							lowercase__  :	Dict        =							tuple(alphabet.all_toks    )
							lowercase__  :	str        =							alphabet.mask_idx
							lowercase__  :	Tuple        =							alphabet.padding_idx

							if is_folding_model:
														lowercase__  :	List[str]        =							esm.esm
							else:
														lowercase__  :	Dict        =							esm

							lowercase__  :	Optional[int]        =							EsmConfig(
							    vocab_size=original_esm_model.embed_tokens.num_embeddings    ,mask_token_id=SCREAMING_SNAKE_CASE_    ,hidden_size=SCREAMING_SNAKE_CASE_    ,num_hidden_layers=SCREAMING_SNAKE_CASE_    ,num_attention_heads=SCREAMING_SNAKE_CASE_    ,intermediate_size=SCREAMING_SNAKE_CASE_    ,max_position_embeddings=10_26    ,layer_norm_eps=1E-5    ,attention_probs_dropout_prob=0.0    ,hidden_dropout_prob=0.0    ,pad_token_id=SCREAMING_SNAKE_CASE_    ,emb_layer_norm_before=SCREAMING_SNAKE_CASE_    ,token_dropout=SCREAMING_SNAKE_CASE_    ,position_embedding_type=SCREAMING_SNAKE_CASE_    ,is_folding_model=SCREAMING_SNAKE_CASE_    ,esmfold_config=SCREAMING_SNAKE_CASE_    ,vocab_list=SCREAMING_SNAKE_CASE_    ,)
							if classification_head:
														lowercase__  :	Union[str, Any]        =							esm.classification_heads["mnli"].out_proj.weight.shape[0]
							print("Our ESM config:"    ,SCREAMING_SNAKE_CASE_    )

							if model.startswith("esmfold"    ):
														lowercase__  :	Optional[int]        =							EsmForProteinFolding
							elif classification_head:
														lowercase__  :	Optional[Any]        =							EsmForSequenceClassification
							else:
														lowercase__  :	List[str]        =							EsmForMaskedLM
							lowercase__  :	List[str]        =							model_class(SCREAMING_SNAKE_CASE_    )
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							lowercase__  :	Optional[int]        =							original_esm_model.embed_tokens.weight
							if position_embedding_type == "absolute":
														lowercase__  :	str        =							original_esm_model.embed_positions.weight

							if config.emb_layer_norm_before:
														lowercase__  :	Dict        =							original_esm_model.emb_layer_norm_before.weight
														lowercase__  :	List[str]        =							original_esm_model.emb_layer_norm_before.bias

							lowercase__  :	Union[str, Any]        =							original_esm_model.emb_layer_norm_after.weight
							lowercase__  :	List[str]        =							original_esm_model.emb_layer_norm_after.bias

							for i in range(config.num_hidden_layers    ):
														# Encoder: start of layer
														lowercase__  :	EsmLayer        =							model.esm.encoder.layer[i]
														# esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
														lowercase__  :	Union[str, Any]        =							original_esm_model.layers[i]

														# self attention
														lowercase__  :	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)    )
														)

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

														if getattr(esm_layer.self_attn    ,"rot_emb"    ,SCREAMING_SNAKE_CASE_    ) is not None:
																					# Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
																					# During the training of ESM-2 the model was converted to float16 precision, which also converts
																					# the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
																					# If we recompute inv_freq without this loss of precision then we will get subtly different rotary
																					# embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
																					# we make sure the new model copies the data from the old inv_freq.
																					lowercase__  :	List[Any]        =							esm_layer.self_attn.rot_emb.inv_freq

														# LayerNorm changes for pre-activation
														lowercase__  :	Optional[Any]        =							esm_layer.self_attn_layer_norm.weight
														lowercase__  :	int        =							esm_layer.self_attn_layer_norm.bias
														lowercase__  :	str        =							esm_layer.final_layer_norm.weight
														lowercase__  :	List[Any]        =							esm_layer.final_layer_norm.bias

														# self-attention output
														lowercase__  :	EsmSelfOutput        =							layer.attention.output
														assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
														lowercase__  :	List[str]        =							esm_layer.self_attn.out_proj.weight
														lowercase__  :	Union[str, Any]        =							esm_layer.self_attn.out_proj.bias

														# intermediate
														lowercase__  :	EsmIntermediate        =							layer.intermediate
														assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
														lowercase__  :	Union[str, Any]        =							esm_layer.fca.weight
														lowercase__  :	Any        =							esm_layer.fca.bias

														# output
														lowercase__  :	EsmOutput        =							layer.output
														assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
														lowercase__  :	Tuple        =							esm_layer.fca.weight
														lowercase__  :	Optional[int]        =							esm_layer.fca.bias
														# end of layer

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

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

														lowercase__  :	Any        =							hf_tokenizer([row[1] for row in sample_data]    ,return_tensors="pt"    ,padding=SCREAMING_SNAKE_CASE_    )
														lowercase__  :	List[Any]        =							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.
																					lowercase__  :	Dict        =							esm.cuda().infer([row[1] for row in sample_data]    )
																					lowercase__  :	str        =							model.cuda()(
																					    input_ids=hf_tokens["input_ids"].cuda()    ,attention_mask=hf_tokens["attention_mask"].cuda()    )
														else:
																					lowercase__  :	str        =							model(**SCREAMING_SNAKE_CASE_    ,output_hidden_states=SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Optional[Any]        =							our_output["logits"]
																					if classification_head:
																												lowercase__  :	Optional[Any]        =							esm.model.classification_heads["mnli"](esm.extract_features(SCREAMING_SNAKE_CASE_    )    )
																					else:
																												lowercase__  :	int        =							esm(hf_tokens["input_ids"]    ,repr_layers=list(range(9_99    )    )    )
																												lowercase__  :	Tuple        =							their_output["logits"]

														if is_folding_model:
																					lowercase__  :	Any        =							torch.max(torch.abs(our_output["positions"] - their_output["positions"]    )    ).item()
																					lowercase__  :	Any        =							torch.allclose(our_output["positions"]    ,their_output["positions"]    ,atol=1E-5    )
														else:
																					lowercase__  :	str        =							torch.max(torch.abs(our_output - their_output    )    ).item()
																					lowercase__  :	Optional[int]        =							torch.allclose(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,atol=1E-5    )

														print(F"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-5
														print("Do both models output the same tensors?"    ,"🔥" if success else "💩"    )

														if not success:
																					raise Exception("Something went wRoNg"    )

														if not is_folding_model:
																					# Let's check contact prediction too
																					lowercase__  :	Union[str, Any]        =							model.predict_contacts(hf_tokens["input_ids"]    ,hf_tokens["attention_mask"]    )
																					lowercase__  :	Tuple        =							esm.predict_contacts(hf_tokens["input_ids"]    )
																					lowercase__  :	Dict        =							torch.max(torch.abs(our_output - their_output    )    ).item()
																					lowercase__  :	List[str]        =							torch.allclose(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,atol=1E-5    )

																					print("Contact prediction testing:"    )
																					print(F"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-5
																					print("Do both models output the same tensors?"    ,"🔥" if success else "💩"    )

																					if not success:
																												raise Exception("Something went wRoNg"    )

														pathlib.Path(SCREAMING_SNAKE_CASE_    ).mkdir(parents=SCREAMING_SNAKE_CASE_    ,exist_ok=SCREAMING_SNAKE_CASE_    )
														print(F"""Saving model to {pytorch_dump_folder_path}"""    )
														model.save_pretrained(SCREAMING_SNAKE_CASE_    )

														del esm  # Free up some memory before continuing

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

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


if __name__ == "__main__":
						__a  :		Union[str, 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.''')
						__a  :		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
						)





# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=0    )      ->			Any:
							# Format the message.
							if name is None:
														lowercase__  :	Any        =							None
							else:
														lowercase__  :	int        =							"." * max(0    ,spaces - 2    ) + "# {:" + str(50 - spaces    ) + "s}"
														lowercase__  :	str        =							fmt.format(SCREAMING_SNAKE_CASE_    )

							# Print and recurse (if needed).
							if isinstance(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ):
														if msg is not None:
																					print(SCREAMING_SNAKE_CASE_    )
														for k in val.keys():
																					recursive_print(SCREAMING_SNAKE_CASE_    ,val[k]    ,spaces + 2    )
							elif isinstance(SCREAMING_SNAKE_CASE_    ,torch.Tensor    ):
														print(SCREAMING_SNAKE_CASE_    ,":"    ,val.size()    )
							else:
														print(SCREAMING_SNAKE_CASE_    ,":"    ,SCREAMING_SNAKE_CASE_    )
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Dict:
							# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
							# for compatibility with later versions of NVIDIA Megatron-LM.
							# The inverse operation is performed inside Megatron-LM to read checkpoints:
							# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
							# If param is the weight tensor of the self-attention block, the returned tensor
							# will have to be transposed one more time to be read by HuggingFace BERT.
							lowercase__  :	Optional[Any]        =							param.size()
							if checkpoint_version == 1.0:
														# version 1.0 stores [num_heads * hidden_size * num_splits, :]
														lowercase__  :	List[str]        =							(num_heads, hidden_size, num_splits) + input_shape[1:]
														lowercase__  :	List[Any]        =							param.view(*SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Optional[Any]        =							param.transpose(0    ,2    )
														lowercase__  :	List[str]        =							param.transpose(1    ,2    ).contiguous()
							elif checkpoint_version >= 2.0:
														# other versions store [num_heads * num_splits * hidden_size, :]
														lowercase__  :	Any        =							(num_heads, num_splits, hidden_size) + input_shape[1:]
														lowercase__  :	str        =							param.view(*SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Any        =							param.transpose(0    ,1    ).contiguous()
							lowercase__  :	List[Any]        =							param.view(*SCREAMING_SNAKE_CASE_    )
							return param
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Optional[int]:
							# The converted output model.
							lowercase__  :	List[str]        =							{}

							# old versions did not store training args
							lowercase__  :	List[str]        =							input_state_dict.get("args"    ,SCREAMING_SNAKE_CASE_    )
							if ds_args is not None:
														# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
														# from pprint import pprint
														# pprint(vars(ds_args))

														lowercase__  :	List[Any]        =							ds_args.tokenizer_type
														lowercase__  :	Tuple        =							ds_args.padded_vocab_size
														lowercase__  :	Optional[int]        =							ds_args.max_position_embeddings
														lowercase__  :	Any        =							ds_args.hidden_size
														lowercase__  :	List[str]        =							ds_args.num_layers
														lowercase__  :	List[str]        =							ds_args.num_attention_heads
														lowercase__  :	List[Any]        =							ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
														# pprint(config)

							# The number of heads.
							lowercase__  :	Optional[int]        =							config.num_attention_heads
							# The hidden_size per head.
							lowercase__  :	int        =							config.hidden_size // heads
							# Megatron-LM checkpoint version
							if "checkpoint_version" in input_state_dict.keys():
														lowercase__  :	List[Any]        =							input_state_dict["checkpoint_version"]
							else:
														lowercase__  :	Optional[int]        =							0.0

							# The model.
							lowercase__  :	Any        =							input_state_dict["model"]
							# The language model.
							lowercase__  :	int        =							model["language_model"]
							# The embeddings.
							lowercase__  :	str        =							lm["embedding"]

							# The word embeddings.
							lowercase__  :	Any        =							embeddings["word_embeddings"]["weight"]
							# Truncate the embedding table to vocab_size rows.
							lowercase__  :	str        =							word_embeddings[: config.vocab_size, :]
							# Store the word embeddings.
							lowercase__  :	Any        =							word_embeddings

							# The position embeddings.
							lowercase__  :	Optional[Any]        =							embeddings["position_embeddings"]["weight"]
							assert pos_embeddings.size(0    ) == config.max_position_embeddings and pos_embeddings.size(1    ) == config.hidden_size
							# Store the position embeddings.
							lowercase__  :	Tuple        =							pos_embeddings

							# The token-type embeddings.
							lowercase__  :	List[str]        =							embeddings["tokentype_embeddings"]["weight"]
							# Store the position embeddings.
							lowercase__  :	Tuple        =							tokentype_embeddings

							# The transformer.
							lowercase__  :	Optional[int]        =							lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

							# The regex to extract layer names.
							lowercase__  :	Dict        =							re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"    )

							# The simple map of names for "automated" rules.
							lowercase__  :	int        =							{
							    "attention.dense": ".attention.output.dense.",
							    "self_attention.dense": ".attention.output.dense.",
							    "mlp.dense_h_to_4h": ".intermediate.dense.",
							    "mlp.dense_4h_to_h": ".output.dense.",
							}

							# Keep track of the attention/query/value tensor.
							lowercase__  :	int        =							None

							# Extract the layers.
							for key, val in transformer.items():
														# Match the name.
														lowercase__  :	List[str]        =							layer_re.match(SCREAMING_SNAKE_CASE_    )

														# Stop if that's not a layer
														if m is None:
																					break

														# The index of the layer.
														lowercase__  :	Dict        =							int(m.group(1    )    )
														# The name of the operation.
														lowercase__  :	Any        =							m.group(2    )
														# Is it a weight or a bias?
														lowercase__  :	Tuple        =							m.group(3    )

														# The name of the layer.
														lowercase__  :	Tuple        =							F"""bert.encoder.layer.{layer_idx}"""

														# For layernorm(s), simply store the layer norm.
														if op_name.endswith("layernorm"    ):
																					lowercase__  :	Optional[Any]        =							"attention.ln" if op_name.startswith("input"    ) else "ln"
																					lowercase__  :	Optional[int]        =							val

														# Transpose the QKV matrix.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "weight":
																					# Make sure the QKV pointer is nil.
																					assert attention_qkv_weight is None, ""

																					lowercase__  :	List[Any]        =							fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,3    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
																					# Store the tensor as we need the bias as well to interleave QKV and biases.
																					lowercase__  :	Union[str, Any]        =							out_val

														# Transpose the bias.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "bias":
																					# Make sure we read the weight tensor.
																					assert attention_qkv_weight is not None, ""

																					# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
																					lowercase__  :	List[Any]        =							attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																					lowercase__  :	Optional[int]        =							attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																					lowercase__  :	List[Any]        =							attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

																					lowercase__  :	Any        =							fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,3    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
																					# Split the bias.
																					lowercase__  :	Dict        =							out_val[0 * config.hidden_size : 1 * config.hidden_size]
																					lowercase__  :	List[str]        =							out_val[1 * config.hidden_size : 2 * config.hidden_size]
																					lowercase__  :	str        =							out_val[2 * config.hidden_size : 3 * config.hidden_size]

																					# Store.
																					lowercase__  :	int        =							q
																					lowercase__  :	Optional[Any]        =							q_bias
																					lowercase__  :	List[str]        =							k
																					lowercase__  :	str        =							k_bias
																					lowercase__  :	List[str]        =							v
																					lowercase__  :	Optional[Any]        =							v_bias

																					# Clear the stored tensor.
																					lowercase__  :	Union[str, Any]        =							None

														# Copy weights and biases as is.
														elif weight_or_bias in ["weight", "bias"]:
																					lowercase__  :	int        =							megatron_to_transformers[op_name]
																					lowercase__  :	Optional[int]        =							val

    # The final layernorm.
							lowercase__  :	str        =							transformer["final_layernorm.weight"]
							lowercase__  :	Dict        =							transformer["final_layernorm.bias"]

							# The pooler.
							lowercase__  :	Union[str, Any]        =							lm["pooler"]

							# Store the matrix and the bias.
							lowercase__  :	Dict        =							pooler["dense.weight"]
							lowercase__  :	List[Any]        =							pooler["dense.bias"]

							# The LM head from Megatron (for RACE).
							lowercase__  :	Any        =							model["lm_head"]

							# The transform matrix.
							lowercase__  :	Optional[int]        =							lm_head["dense.weight"]
							lowercase__  :	Union[str, Any]        =							lm_head["dense.bias"]

							# The transform LN.
							lowercase__  :	int        =							lm_head["layernorm.weight"]
							lowercase__  :	Union[str, Any]        =							lm_head["layernorm.bias"]

							# For the decoder, we replicate the weights.
							lowercase__  :	str        =							word_embeddings
							lowercase__  :	str        =							lm_head["bias"]

							# The classifier from Megatron (for MLNI).
							lowercase__  :	Optional[Any]        =							model["binary_head"]

							# Store the classifier.
							lowercase__  :	Tuple        =							binary_head["weight"]
							lowercase__  :	Any        =							binary_head["bias"]

							# It should be done!
							return output_state_dict
def  snake_case_					(		)      ->			str:
							# Create the argument parser.
							lowercase__  :	Optional[Any]        =							argparse.ArgumentParser()
							parser.add_argument("--print-checkpoint-structure"    ,action="store_true"    )
							parser.add_argument("path_to_checkpoint"    ,type=SCREAMING_SNAKE_CASE_    ,help="Path to the ZIP file containing the checkpoint"    )
							parser.add_argument(
							    "--config_file"    ,default=""    ,type=SCREAMING_SNAKE_CASE_    ,help="An optional config json file describing the pre-trained model."    ,)
							lowercase__  :	List[str]        =							parser.parse_args()

							# Extract the basename.
							lowercase__  :	Optional[Any]        =							os.path.dirname(args.path_to_checkpoint    )

							# Load the model.
							# the .zip is very optional, let's keep it for backward compatibility
							print(F"""Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\""""    )
							if args.path_to_checkpoint.endswith(".zip"    ):
														with zipfile.ZipFile(args.path_to_checkpoint    ,"r"    ) as checkpoint:
																					with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"    ) as pytorch_dict:
																												lowercase__  :	Optional[Any]        =							torch.load(SCREAMING_SNAKE_CASE_    ,map_location="cpu"    )
							else:
														lowercase__  :	str        =							torch.load(args.path_to_checkpoint    ,map_location="cpu"    )

							if args.config_file == "":
														# Default config of megatron-bert 345m
														lowercase__  :	Optional[int]        =							MegatronBertConfig()

														# different megatron-bert-*-345m models have different vocab sizes, so override the default
														# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
														lowercase__  :	Union[str, Any]        =							input_state_dict["model"]["lm_head"]["bias"].numel()
							else:
														lowercase__  :	Any        =							MegatronBertConfig.from_json_file(args.config_file    )

							# Convert.
							print("Converting"    )
							lowercase__  :	Optional[int]        =							convert_megatron_checkpoint(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

							# Print the structure of converted state dict.
							if args.print_checkpoint_structure:
														recursive_print(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

							# Store the config to file.
							print("Saving config"    )
							config.save_pretrained(SCREAMING_SNAKE_CASE_    )

							# Store the state_dict to file.
							lowercase__  :	Tuple        =							os.path.join(SCREAMING_SNAKE_CASE_    ,"pytorch_model.bin"    )
							print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
							torch.save(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )


####################################################################################################

if __name__ == "__main__":
						main()

####################################################################################################