


'''simple docstring'''



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[Any]				=						logging.get_logger(__name__)

A__	:      Dict				=						[
    (
        """protein1""",
        """MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA""",
    ),
    ("""protein2""", """MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"""),
    ("""protein3""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"""),
    ("""protein4""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"""),
]

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

A__	:      Any				=						list("""ARNDCQEGHILKMFPSTWYV""")

A__	:      Union[str, Any]				=						restypes + ["""X"""]
A__	:      Optional[int]				=						restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]



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



def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       List[Any]							)  ->	int:
     __lowerCamelCase     :			int              =	our_module.load_state_dict(original_module.state_dict()							)
     if status.missing_keys:
          raise ValueError(F'Missing keys: {status.missing_keys}'							)
     if status.unexpected_keys:
          raise ValueError(F'Unexpected keys: {status.unexpected_keys}'							)



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str							)  ->	Any:
     if model.startswith('esmfold'							):
          __lowerCamelCase     :			Union[str, Any]              =	MODEL_MAPPING[model]()
     else:
          __lowerCamelCase       , __lowerCamelCase     :			str              =	MODEL_MAPPING[model]()
     esm.eval()  # disable dropout

     if model.startswith('esmfold'							):
          __lowerCamelCase     :			List[Any]              =	esm.esm.embed_dim
          __lowerCamelCase     :			List[str]              =	esm.esm.num_layers
          __lowerCamelCase     :			Optional[Any]              =	esm.esm.attention_heads
          __lowerCamelCase     :			Any              =	4 * embed_dim
          __lowerCamelCase     :			Tuple              =	esm.esm.token_dropout
          __lowerCamelCase     :			Optional[Any]              =	False  # This code path does not exist in ESM-2
          __lowerCamelCase     :			Tuple              =	'rotary'
          __lowerCamelCase     :			List[str]              =	True
          __lowerCamelCase     :			str              =	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
          __lowerCamelCase     :			str              =	esm.args.embed_dim
          __lowerCamelCase     :			Dict              =	esm.args.layers
          __lowerCamelCase     :			str              =	esm.args.attention_heads
          __lowerCamelCase     :			Optional[int]              =	esm.args.ffn_embed_dim
          __lowerCamelCase     :			str              =	esm.args.token_dropout
          __lowerCamelCase     :			List[Any]              =	True if esm.emb_layer_norm_before else False
          __lowerCamelCase     :			Tuple              =	'absolute'
          __lowerCamelCase     :			Optional[Any]              =	False
          __lowerCamelCase     :			Union[str, Any]              =	None
     else:
          # Indicates an ESM-2 model
          __lowerCamelCase     :			int              =	esm.embed_dim
          __lowerCamelCase     :			str              =	esm.num_layers
          __lowerCamelCase     :			Optional[int]              =	esm.attention_heads
          __lowerCamelCase     :			Optional[Any]              =	4 * embed_dim  # This is hardcoded in ESM-2
          __lowerCamelCase     :			List[Any]              =	esm.token_dropout
          __lowerCamelCase     :			str              =	False  # This code path does not exist in ESM-2
          __lowerCamelCase     :			int              =	'rotary'
          __lowerCamelCase     :			str              =	False
          __lowerCamelCase     :			int              =	None

     if is_folding_model:
          __lowerCamelCase     :			Optional[Any]              =	esm.esm.alphabet
     __lowerCamelCase     :			Dict              =	tuple(alphabet.all_toks							)
     __lowerCamelCase     :			Optional[int]              =	alphabet.mask_idx
     __lowerCamelCase     :			int              =	alphabet.padding_idx

     if is_folding_model:
          __lowerCamelCase     :			Optional[int]              =	esm.esm
     else:
          __lowerCamelCase     :			int              =	esm

     __lowerCamelCase     :			Dict              =	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=10_26   ,					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:
          __lowerCamelCase     :			Any              =	esm.classification_heads['mnli'].out_proj.weight.shape[0]
     print('Our ESM config:'   ,					UpperCAmelCase_							)

     if model.startswith('esmfold'							):
          __lowerCamelCase     :			List[Any]              =	EsmForProteinFolding
     elif classification_head:
          __lowerCamelCase     :			List[Any]              =	EsmForSequenceClassification
     else:
          __lowerCamelCase     :			Dict              =	EsmForMaskedLM
     __lowerCamelCase     :			Union[str, Any]              =	model_class(UpperCAmelCase_							)
     model.eval()

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

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

     __lowerCamelCase     :			int              =	original_esm_model.emb_layer_norm_after.weight
     __lowerCamelCase     :			Dict              =	original_esm_model.emb_layer_norm_after.bias

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

          # self attention
          __lowerCamelCase     :			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)							)
          )

          __lowerCamelCase     :			Optional[int]              =	esm_layer.self_attn.q_proj.weight
          __lowerCamelCase     :			Optional[Any]              =	esm_layer.self_attn.q_proj.bias
          __lowerCamelCase     :			str              =	esm_layer.self_attn.k_proj.weight
          __lowerCamelCase     :			int              =	esm_layer.self_attn.k_proj.bias
          __lowerCamelCase     :			Dict              =	esm_layer.self_attn.v_proj.weight
          __lowerCamelCase     :			Union[str, Any]              =	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.
               __lowerCamelCase     :			Union[str, Any]              =	esm_layer.self_attn.rot_emb.inv_freq

          # LayerNorm changes for pre-activation
          __lowerCamelCase     :			Dict              =	esm_layer.self_attn_layer_norm.weight
          __lowerCamelCase     :			Tuple              =	esm_layer.self_attn_layer_norm.bias
          __lowerCamelCase     :			int              =	esm_layer.final_layer_norm.weight
          __lowerCamelCase     :			Optional[int]              =	esm_layer.final_layer_norm.bias

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

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

          # output
          __lowerCamelCase     :			EsmOutput              =	layer.output
          assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
          __lowerCamelCase     :			Union[str, Any]              =	esm_layer.fca.weight
          __lowerCamelCase     :			Tuple              =	esm_layer.fca.bias
          # end of layer

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

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

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

          if is_folding_model:
               __lowerCamelCase     :			List[str]              =	torch.max(torch.abs(our_output['positions'] - their_output['positions']							)							).item()
               __lowerCamelCase     :			Tuple              =	torch.allclose(our_output['positions']   ,					their_output['positions']   ,					atol=1e-5							)
          else:
               __lowerCamelCase     :			Any              =	torch.max(torch.abs(our_output - their_output							)							).item()
               __lowerCamelCase     :			List[Any]              =	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
               __lowerCamelCase     :			List[str]              =	model.predict_contacts(hf_tokens['input_ids']   ,					hf_tokens['attention_mask']							)
               __lowerCamelCase     :			Tuple              =	esm.predict_contacts(hf_tokens['input_ids']							)
               __lowerCamelCase     :			Dict              =	torch.max(torch.abs(our_output - their_output							)							).item()
               __lowerCamelCase     :			Tuple              =	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__":
      A__	:      Tuple				=						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__	:      Optional[int]				=						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
      )










'''simple docstring'''



import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
      raise Exception("""requires fairseq >= 0.9.0""")


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

A__	:      Union[str, Any]				=						"""Hello world! cécé herlolip"""



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool							)  ->	Optional[Any]:
     __lowerCamelCase     :			Any              =	FairseqRobertaModel.from_pretrained(UpperCAmelCase_							)
     roberta.eval()  # disable dropout
     __lowerCamelCase     :			Optional[int]              =	roberta.model.encoder.sentence_encoder
     __lowerCamelCase     :			Optional[Any]              =	RobertaConfig(
         vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,					hidden_size=roberta.args.encoder_embed_dim   ,					num_hidden_layers=roberta.args.encoder_layers   ,					num_attention_heads=roberta.args.encoder_attention_heads   ,					intermediate_size=roberta.args.encoder_ffn_embed_dim   ,					max_position_embeddings=5_14   ,					type_vocab_size=1   ,					layer_norm_eps=1e-5   ,					)
     if classification_head:
          __lowerCamelCase     :			Optional[Any]              =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
     print('Our BERT config:'   ,					UpperCAmelCase_							)

     __lowerCamelCase     :			Tuple              =	RobertaForSequenceClassification(UpperCAmelCase_							) if classification_head else RobertaForMaskedLM(UpperCAmelCase_							)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     __lowerCamelCase     :			Optional[Any]              =	roberta_sent_encoder.embed_tokens.weight
     __lowerCamelCase     :			List[str]              =	roberta_sent_encoder.embed_positions.weight
     __lowerCamelCase     :			Tuple              =	torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
     __lowerCamelCase     :			Any              =	roberta_sent_encoder.emb_layer_norm.weight
     __lowerCamelCase     :			Tuple              =	roberta_sent_encoder.emb_layer_norm.bias

     for i in range(config.num_hidden_layers							):
          # Encoder: start of layer
          __lowerCamelCase     :			BertLayer              =	model.roberta.encoder.layer[i]
          __lowerCamelCase     :			TransformerSentenceEncoderLayer              =	roberta_sent_encoder.layers[i]

          # self attention
          __lowerCamelCase     :			BertSelfAttention              =	layer.attention.self
          assert (
              roberta_layer.self_attn.k_proj.weight.data.shape
              == roberta_layer.self_attn.q_proj.weight.data.shape
              == roberta_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)							)
          )

          __lowerCamelCase     :			Optional[int]              =	roberta_layer.self_attn.q_proj.weight
          __lowerCamelCase     :			Any              =	roberta_layer.self_attn.q_proj.bias
          __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn.k_proj.weight
          __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.k_proj.bias
          __lowerCamelCase     :			Any              =	roberta_layer.self_attn.v_proj.weight
          __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.v_proj.bias

          # self-attention output
          __lowerCamelCase     :			BertSelfOutput              =	layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.out_proj.weight
          __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.out_proj.bias
          __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn_layer_norm.weight
          __lowerCamelCase     :			List[str]              =	roberta_layer.self_attn_layer_norm.bias

          # intermediate
          __lowerCamelCase     :			BertIntermediate              =	layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          __lowerCamelCase     :			Union[str, Any]              =	roberta_layer.fca.weight
          __lowerCamelCase     :			Optional[Any]              =	roberta_layer.fca.bias

          # output
          __lowerCamelCase     :			BertOutput              =	layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          __lowerCamelCase     :			str              =	roberta_layer.fca.weight
          __lowerCamelCase     :			Tuple              =	roberta_layer.fca.bias
          __lowerCamelCase     :			Optional[int]              =	roberta_layer.final_layer_norm.weight
          __lowerCamelCase     :			Optional[Any]              =	roberta_layer.final_layer_norm.bias
          # end of layer

     if classification_head:
          __lowerCamelCase     :			Dict              =	roberta.model.classification_heads['mnli'].dense.weight
          __lowerCamelCase     :			Union[str, Any]              =	roberta.model.classification_heads['mnli'].dense.bias
          __lowerCamelCase     :			Optional[int]              =	roberta.model.classification_heads['mnli'].out_proj.weight
          __lowerCamelCase     :			Any              =	roberta.model.classification_heads['mnli'].out_proj.bias
     else:
          # LM Head
          __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.dense.weight
          __lowerCamelCase     :			List[Any]              =	roberta.model.encoder.lm_head.dense.bias
          __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.layer_norm.weight
          __lowerCamelCase     :			int              =	roberta.model.encoder.lm_head.layer_norm.bias
          __lowerCamelCase     :			Optional[Any]              =	roberta.model.encoder.lm_head.weight
          __lowerCamelCase     :			Tuple              =	roberta.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     __lowerCamelCase     :			torch.Tensor              =	roberta.encode(UpperCAmelCase_							).unsqueeze(0							)  # batch of size 1

     __lowerCamelCase     :			int              =	model(UpperCAmelCase_							)[0]
     if classification_head:
          __lowerCamelCase     :			List[str]              =	roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase_							)							)
     else:
          __lowerCamelCase     :			Dict              =	roberta.model(UpperCAmelCase_							)[0]
     print(our_output.shape   ,					their_output.shape							)
     __lowerCamelCase     :			Union[str, Any]              =	torch.max(torch.abs(our_output - their_output							)							).item()
     print(F'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
     __lowerCamelCase     :			List[str]              =	torch.allclose(UpperCAmelCase_   ,					UpperCAmelCase_   ,					atol=1e-3							)
     print('Do both models output the same tensors?'   ,					'🔥' if success else '💩'							)
     if not success:
          raise Exception('Something went wRoNg'							)

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


if __name__ == "__main__":
      A__	:      Optional[Any]				=						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, 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."""
      )
      A__	:      Optional[Any]				=						parser.parse_args()
      convert_roberta_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )






