




'''simple docstring'''







import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
						from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
						_lowerCamelCase				:		str				      =    None

if is_ray_available():
						import ray  # noqa: E402 # isort:skip
						from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
						_lowerCamelCase				:		List[Any]				      =    None
						_lowerCamelCase				:		Tuple				      =    None
						_lowerCamelCase				:		List[str]				      =    None






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
				_snake_case      			=			unittest.skip("test requires Datasets, Faiss, psutil"    )(UpperCamelCase_    )
		return test_case





@require_distributed_retrieval
class 	lowerCamelCase_				(			lowerCAmelCase_				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			tempfile.mkdtemp()
							_snake_case      			=			8

							# DPR tok
							_snake_case      			=			[
							    "[UNK]",
							    "[CLS]",
							    "[SEP]",
							    "[PAD]",
							    "[MASK]",
							    "want",
							    "##want",
							    "##ed",
							    "wa",
							    "un",
							    "runn",
							    "##ing",
							    ",",
							    "low",
							    "lowest",
							]
							_snake_case      			=			os.path.join(self.tmpdirname,    "dpr_tokenizer"					)
							os.makedirs(_SCREAMING_SNAKE_CASE,    exist_ok=_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			os.path.join(_SCREAMING_SNAKE_CASE,    DPR_VOCAB_FILES_NAMES["vocab_file"]					)
							with open(self.vocab_file,    "w",    encoding="utf-8"					) as vocab_writer:
									vocab_writer.write("".join([x + "\n" for x in vocab_tokens]					)					)

							# BART tok
							_snake_case      			=			[
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							_snake_case      			=			dict(zip(_SCREAMING_SNAKE_CASE,    range(len(_SCREAMING_SNAKE_CASE					)					)					)					)
							_snake_case      			=			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							_snake_case      			=			{"unk_token": "<unk>"}

							_snake_case      			=			os.path.join(self.tmpdirname,    "bart_tokenizer"					)
							os.makedirs(_SCREAMING_SNAKE_CASE,    exist_ok=_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			os.path.join(_SCREAMING_SNAKE_CASE,    BART_VOCAB_FILES_NAMES["vocab_file"]					)
							_snake_case      			=			os.path.join(_SCREAMING_SNAKE_CASE,    BART_VOCAB_FILES_NAMES["merges_file"]					)
							with open(self.vocab_file,    "w",    encoding="utf-8"					) as fp:
									fp.write(json.dumps(_SCREAMING_SNAKE_CASE					) + "\n"					)
							with open(self.merges_file,    "w",    encoding="utf-8"					) as fp:
									fp.write("\n".join(_SCREAMING_SNAKE_CASE					)					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,    "dpr_tokenizer"					)					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname,    "bart_tokenizer"					)					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							shutil.rmtree(self.tmpdirname					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			Dataset.from_dict(
							    {
							        "id": ["0", "1"],
							        "text": ["foo", "bar"],
							        "title": ["Foo", "Bar"],
							        "embeddings": [np.ones(self.retrieval_vector_size					), 2 * np.ones(self.retrieval_vector_size					)],
							    }					)
							dataset.add_faiss_index("embeddings",    string_factory="Flat",    metric_type=faiss.METRIC_INNER_PRODUCT					)
							return dataset






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase=12_345					):
							_snake_case      			=			self.get_dummy_dataset()
							_snake_case      			=			RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size,    question_encoder=DPRConfig().to_dict(),    generator=BartConfig().to_dict(),    )
							with patch("transformers.models.rag.retrieval_rag.load_dataset"					) as mock_load_dataset:
									_snake_case      			=			dataset
									_snake_case      			=			RagPyTorchDistributedRetriever(
									    _SCREAMING_SNAKE_CASE,    question_encoder_tokenizer=self.get_dpr_tokenizer(),    generator_tokenizer=self.get_bart_tokenizer(),    )
									if init_retrieval:
											retriever.init_retrieval(_SCREAMING_SNAKE_CASE					)
							return retriever






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							ray.init(local_mode=_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size,    question_encoder=DPRConfig().to_dict(),    generator=BartConfig().to_dict(),    )
							_snake_case      			=			ray.remote(_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			[remote_cls.remote() for _ in range(1					)]
							with patch("transformers.models.rag.retrieval_rag.load_dataset"					) as mock_load_dataset:
									_snake_case      			=			self.get_dummy_dataset()
									_snake_case      			=			RagRayDistributedRetriever(
									    _SCREAMING_SNAKE_CASE,    question_encoder_tokenizer=self.get_dpr_tokenizer(),    generator_tokenizer=self.get_bart_tokenizer(),    retrieval_workers=_SCREAMING_SNAKE_CASE,    )
									if init_retrieval:
											retriever.init_retrieval()
							return retriever






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase=12_345					):
							_snake_case      			=			self.get_dummy_dataset()
							_snake_case      			=			RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size,    question_encoder=DPRConfig().to_dict(),    generator=BartConfig().to_dict(),    index_name="custom",    )
							if from_disk:
									_snake_case      			=			os.path.join(self.tmpdirname,    "dataset"					)
									_snake_case      			=			os.path.join(self.tmpdirname,    "index.faiss"					)
									dataset.get_index("embeddings"					).save(os.path.join(self.tmpdirname,    "index.faiss"					)					)
									dataset.drop_index("embeddings"					)
									dataset.save_to_disk(os.path.join(self.tmpdirname,    "dataset"					)					)
									del dataset
									_snake_case      			=			RagPyTorchDistributedRetriever(
									    _SCREAMING_SNAKE_CASE,    question_encoder_tokenizer=self.get_dpr_tokenizer(),    generator_tokenizer=self.get_bart_tokenizer(),    )
							else:
									_snake_case      			=			RagPyTorchDistributedRetriever(
									    _SCREAMING_SNAKE_CASE,    question_encoder_tokenizer=self.get_dpr_tokenizer(),    generator_tokenizer=self.get_bart_tokenizer(),    index=CustomHFIndex(config.retrieval_vector_size,    _SCREAMING_SNAKE_CASE					),    )
							if init_retrieval:
									retriever.init_retrieval(_SCREAMING_SNAKE_CASE					)
							return retriever






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase					):
							ray.init(local_mode=_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			self.get_dummy_dataset()
							_snake_case      			=			RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size,    question_encoder=DPRConfig().to_dict(),    generator=BartConfig().to_dict(),    index_name="custom",    )
							_snake_case      			=			ray.remote(_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			[remote_cls.remote() for _ in range(1					)]
							if from_disk:
									_snake_case      			=			os.path.join(self.tmpdirname,    "dataset"					)
									_snake_case      			=			os.path.join(self.tmpdirname,    "index.faiss"					)
									dataset.get_index("embeddings"					).save(os.path.join(self.tmpdirname,    "index.faiss"					)					)
									dataset.drop_index("embeddings"					)
									dataset.save_to_disk(os.path.join(self.tmpdirname,    "dataset"					)					)
									del dataset
									_snake_case      			=			RagRayDistributedRetriever(
									    _SCREAMING_SNAKE_CASE,    question_encoder_tokenizer=self.get_dpr_tokenizer(),    generator_tokenizer=self.get_bart_tokenizer(),    retrieval_workers=_SCREAMING_SNAKE_CASE,    index=CustomHFIndex.load_from_disk(
									        vector_size=config.retrieval_vector_size,    dataset_path=config.passages_path,    index_path=config.index_path,    ),    )
							else:
									_snake_case      			=			RagRayDistributedRetriever(
									    _SCREAMING_SNAKE_CASE,    question_encoder_tokenizer=self.get_dpr_tokenizer(),    generator_tokenizer=self.get_bart_tokenizer(),    retrieval_workers=_SCREAMING_SNAKE_CASE,    index=CustomHFIndex(config.retrieval_vector_size,    _SCREAMING_SNAKE_CASE					),    )
							if init_retrieval:
									retriever.init_retrieval()
							return retriever






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					):
							_snake_case			,       _snake_case			,       _snake_case      			=			retriever.retrieve(_SCREAMING_SNAKE_CASE,    n_docs=_SCREAMING_SNAKE_CASE					)
							self.assertEqual(retrieved_doc_embeds.shape,    (2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(_SCREAMING_SNAKE_CASE					),    2					)
							self.assertEqual(sorted(doc_dicts[0]					),    ["embeddings", "id", "text", "title"]					)
							self.assertEqual(len(doc_dicts[0]["id"]					),    _SCREAMING_SNAKE_CASE					)
							self.assertEqual(doc_dicts[0]["id"][0],    "1"					)  # max inner product is reached with second doc
							self.assertEqual(doc_dicts[1]["id"][0],    "0"					)  # max inner product is reached with first doc
							self.assertListEqual(doc_ids.tolist(),    [[1], [0]]					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			1
							_snake_case      			=			np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)],    dtype=np.floataa					)

							self.distributed_retriever_check(
							    self.get_dummy_pytorch_distributed_retriever(init_retrieval=_SCREAMING_SNAKE_CASE					),    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			1
							_snake_case      			=			np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)],    dtype=np.floataa					)

							self.distributed_retriever_check(
							    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_SCREAMING_SNAKE_CASE,    from_disk=_SCREAMING_SNAKE_CASE					),    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    )






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			1
							_snake_case      			=			np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)],    dtype=np.floataa					)

							self.distributed_retriever_check(
							    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_SCREAMING_SNAKE_CASE,    from_disk=_SCREAMING_SNAKE_CASE					),    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    )






					@require_ray
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			1
							_snake_case      			=			np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)],    dtype=np.floataa					)

							self.distributed_retriever_check(
							    self.get_dummy_ray_distributed_retriever(init_retrieval=_SCREAMING_SNAKE_CASE					),    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE					)
							ray.shutdown()






					@require_ray
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			1
							_snake_case      			=			np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)],    dtype=np.floataa					)
							with self.assertRaises(_SCREAMING_SNAKE_CASE					):
									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_SCREAMING_SNAKE_CASE,    from_disk=_SCREAMING_SNAKE_CASE					),    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    )
							ray.shutdown()






					@require_ray
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			1
							_snake_case      			=			np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)],    dtype=np.floataa					)

							self.distributed_retriever_check(
							    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_SCREAMING_SNAKE_CASE,    from_disk=_SCREAMING_SNAKE_CASE					),    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE					)
							ray.shutdown()












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
     )

