code: 
  
  
  '''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
        )
  
  
  
  
  
  
  
style_context: 
  
  
  '''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
        )
  
  
  
  
  
  
  