




'''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()
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 				SCREAMING_SNAKE_CASE(      ) ->   str:
     with TemporaryDirectory() as tempdir:
          A:		List[str]        =		'''\n'''.join(__lowercase  )
          A:		List[str]        =		Path(__lowercase  ) / '''vocab.txt'''
          vocab_file.write_text(__lowercase  )
          A:		List[Any]        =		EsmTokenizer(vocab_file=str(__lowercase  )  )
     A:		str        =		0  # Overlaps with 'A' but that seems to be what they want
     return hf_tokenizer
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   Optional[int]:
     A:		Any        =		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 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase		,							__lowercase		,							__lowercase  ) ->   Tuple:
     if model.startswith('''esmfold'''  ):
          A:		Any        =		MODEL_MAPPING[model]()
     else:
          A:		List[Any]        =		MODEL_MAPPING[model]()
     esm.eval()  # disable dropout

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

     if is_folding_model:
          A:		Any        =		esm.esm.alphabet
     A:		str        =		tuple(alphabet.all_toks  )
     A:		Union[str, Any]        =		alphabet.mask_idx
     A:		List[Any]        =		alphabet.padding_idx

     if is_folding_model:
          A:		Dict        =		esm.esm
     else:
          A:		Union[str, Any]        =		esm

     A:		List[str]        =		EsmConfig(
         vocab_size=original_esm_model.embed_tokens.num_embeddings		,							mask_token_id=__lowercase		,							hidden_size=__lowercase		,							num_hidden_layers=__lowercase		,							num_attention_heads=__lowercase		,							intermediate_size=__lowercase		,							max_position_embeddings=1_0_2_6		,							layer_norm_eps=1E-5		,							attention_probs_dropout_prob=0.0		,							hidden_dropout_prob=0.0		,							pad_token_id=__lowercase		,							emb_layer_norm_before=__lowercase		,							token_dropout=__lowercase		,							position_embedding_type=__lowercase		,							is_folding_model=__lowercase		,							esmfold_config=__lowercase		,							vocab_list=__lowercase		,							)
     if classification_head:
          A:		List[Any]        =		esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our ESM config:'''		,							__lowercase  )

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

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

     if config.emb_layer_norm_before:
          A:		Optional[int]        =		original_esm_model.emb_layer_norm_before.weight
          A:		List[str]        =		original_esm_model.emb_layer_norm_before.bias

     A:		Tuple        =		original_esm_model.emb_layer_norm_after.weight
     A:		Dict        =		original_esm_model.emb_layer_norm_after.bias

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

          # self attention
          A:		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)  )
          )

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

          if getattr(esm_layer.self_attn		,							'''rot_emb'''		,							__lowercase  ) 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.
               A:		Optional[int]        =		esm_layer.self_attn.rot_emb.inv_freq

          # LayerNorm changes for pre-activation
          A:		Any        =		esm_layer.self_attn_layer_norm.weight
          A:		int        =		esm_layer.self_attn_layer_norm.bias
          A:		int        =		esm_layer.final_layer_norm.weight
          A:		Dict        =		esm_layer.final_layer_norm.bias

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

          # intermediate
          A:		EsmIntermediate        =		layer.intermediate
          assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
          A:		int        =		esm_layer.fca.weight
          A:		Dict        =		esm_layer.fca.bias

          # output
          A:		EsmOutput        =		layer.output
          assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
          A:		str        =		esm_layer.fca.weight
          A:		str        =		esm_layer.fca.bias
          # end of layer

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

     if is_folding_model:
          A:		Union[str, Any]        =		get_esmfold_tokenizer()
          A:		List[str]        =		hf_tokenizer(
              [row[1] for row in sample_data]		,							return_tensors='''pt'''		,							padding=__lowercase		,							add_special_tokens=__lowercase  )
          A:		int        =		esmfold_encode_sequences([row[1] for row in sample_data]  )
          A:		Optional[Any]        =		torch.all(hf_tokens['''input_ids'''] == esmfold_aas  ) and torch.all(
              hf_tokens['''attention_mask'''] == esmfold_mask  )
     else:
          # Let's check that we get the same results.
          A:		Union[str, Any]        =		alphabet.get_batch_converter()
          A:		str        =		batch_converter(__lowercase  )
          # Prepare tokenizer and make sure it matches
          with TemporaryDirectory() as tempdir:
               A:		List[Any]        =		'''\n'''.join(alphabet.all_toks  )
               A:		Optional[int]        =		Path(__lowercase  ) / '''vocab.txt'''
               vocab_file.write_text(__lowercase  )
               A:		Any        =		EsmTokenizer(vocab_file=str(__lowercase  )  )

          A:		List[Any]        =		hf_tokenizer([row[1] for row in sample_data]		,							return_tensors='''pt'''		,							padding=__lowercase  )
          A:		str        =		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.
               A:		Union[str, Any]        =		esm.cuda().infer([row[1] for row in sample_data]  )
               A:		Tuple        =		model.cuda()(
                   input_ids=hf_tokens['''input_ids'''].cuda()		,							attention_mask=hf_tokens['''attention_mask'''].cuda()  )
          else:
               A:		Any        =		model(**__lowercase		,							output_hidden_states=__lowercase  )
               A:		List[Any]        =		our_output['''logits''']
               if classification_head:
                    A:		Dict        =		esm.model.classification_heads['''mnli'''](esm.extract_features(__lowercase  )  )
               else:
                    A:		Optional[Any]        =		esm(hf_tokens['''input_ids''']		,							repr_layers=list(range(9_9_9  )  )  )
                    A:		Union[str, Any]        =		their_output['''logits''']

          if is_folding_model:
               A:		List[str]        =		torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']  )  ).item()
               A:		Optional[Any]        =		torch.allclose(our_output['''positions''']		,							their_output['''positions''']		,							atol=1E-5  )
          else:
               A:		Dict        =		torch.max(torch.abs(our_output - their_output  )  ).item()
               A:		List[str]        =		torch.allclose(__lowercase		,							__lowercase		,							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
               A:		List[str]        =		model.predict_contacts(hf_tokens['''input_ids''']		,							hf_tokens['''attention_mask''']  )
               A:		List[str]        =		esm.predict_contacts(hf_tokens['''input_ids''']  )
               A:		Optional[Any]        =		torch.max(torch.abs(our_output - their_output  )  ).item()
               A:		Tuple        =		torch.allclose(__lowercase		,							__lowercase		,							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(__lowercase  ).mkdir(parents=__lowercase		,							exist_ok=__lowercase  )
          print(F"""Saving model to {pytorch_dump_folder_path}"""  )
          model.save_pretrained(__lowercase  )

          del esm  # Free up some memory before continuing

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

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


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
     )












'''simple docstring'''


from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  def 				_snake_case    (   self							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		int:







       '''simple docstring'''



       return True




  def 				_snake_case    (   self							:       Optional[int]  ) ->		List[str]:







       '''simple docstring'''



       A:		Optional[Any]        =		CpmTokenizer.from_pretrained('''TsinghuaAI/CPM-Generate'''  )
       A:		Optional[int]        =		'''Hugging Face大法好，谁用谁知道。'''
       A:		str        =		'''Hugging Face大法好,谁用谁知道。<unk>'''
       A:		Dict        =		'''▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'''.split()

       A:		int        =		tokenizer.tokenize(SCREAMING_SNAKE_CASE_  )
       self.assertListEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )

       A:		Optional[int]        =		tokens + [tokenizer.unk_token]

       A:		str        =		[1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_  )				,     SCREAMING_SNAKE_CASE_  )

       A:		Any        =		tokenizer.decode(SCREAMING_SNAKE_CASE_  )
       self.assertEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )






