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()
  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 	_UpperCAmelCase				(  )    ->						Dict:
    with TemporaryDirectory() as tempdir:
      _lowerCAmelCase       :  Union[str, Any]   				=					"""\n""".join(_lowerCamelCase		)
      _lowerCAmelCase       :  str   				=					Path(_lowerCamelCase		) / """vocab.txt"""
      vocab_file.write_text(_lowerCamelCase		)
      _lowerCAmelCase       :  List[str]   				=					EsmTokenizer(vocab_file=str(_lowerCamelCase		)		)
    _lowerCAmelCase       :  Tuple   				=					0  # Overlaps with 'A' but that seems to be what they want
    return hf_tokenizer
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       List[Any]  ,      _lowerCamelCase						:       Any		)    ->						Any:
    _lowerCAmelCase       :  Dict   				=					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				(  _lowerCamelCase						:       str  ,      _lowerCamelCase						:       str  ,      _lowerCamelCase						:       bool  ,      _lowerCamelCase						:       str  ,      _lowerCamelCase						:       str		)    ->						Optional[Any]:
    if model.startswith("""esmfold"""		):
      _lowerCAmelCase       :  List[str]   				=					MODEL_MAPPING[model]()
    else:
      _lowerCAmelCase				,					_lowerCAmelCase       :  List[Any]   				=					MODEL_MAPPING[model]()
    esm.eval()  # disable dropout
  
    if model.startswith("""esmfold"""		):
      _lowerCAmelCase       :  Optional[int]   				=					esm.esm.embed_dim
      _lowerCAmelCase       :  Any   				=					esm.esm.num_layers
      _lowerCAmelCase       :  Tuple   				=					esm.esm.attention_heads
      _lowerCAmelCase       :  Tuple   				=					4 * embed_dim
      _lowerCAmelCase       :  Optional[Any]   				=					esm.esm.token_dropout
      _lowerCAmelCase       :  Tuple   				=					False  # This code path does not exist in ESM-2
      _lowerCAmelCase       :  List[Any]   				=					"""rotary"""
      _lowerCAmelCase       :  List[str]   				=					True
      _lowerCAmelCase       :  Tuple   				=					EsmFoldConfig()
      for key, val in esm.cfg.items():
        if hasattr(_lowerCamelCase  ,      _lowerCamelCase		) and key != "trunk":
          setattr(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase		)
      for key, val in esm.cfg.trunk.items():
        if hasattr(esmfold_config.trunk  ,      _lowerCamelCase		) and key != "structure_module":
          setattr(esmfold_config.trunk  ,      _lowerCamelCase  ,      _lowerCamelCase		)
      for key, val in esm.cfg.trunk.structure_module.items():
        if hasattr(esmfold_config.trunk.structure_module  ,      _lowerCamelCase		):
          setattr(esmfold_config.trunk.structure_module  ,      _lowerCamelCase  ,      _lowerCamelCase		)
    elif hasattr(_lowerCamelCase  ,      """args"""		):
      # Indicates an ESM-1b or ESM-1v model
      _lowerCAmelCase       :  Optional[Any]   				=					esm.args.embed_dim
      _lowerCAmelCase       :  Optional[int]   				=					esm.args.layers
      _lowerCAmelCase       :  Optional[Any]   				=					esm.args.attention_heads
      _lowerCAmelCase       :  Dict   				=					esm.args.ffn_embed_dim
      _lowerCAmelCase       :  List[Any]   				=					esm.args.token_dropout
      _lowerCAmelCase       :  int   				=					True if esm.emb_layer_norm_before else False
      _lowerCAmelCase       :  Dict   				=					"""absolute"""
      _lowerCAmelCase       :  Optional[Any]   				=					False
      _lowerCAmelCase       :  Optional[int]   				=					None
    else:
      # Indicates an ESM-2 model
      _lowerCAmelCase       :  int   				=					esm.embed_dim
      _lowerCAmelCase       :  str   				=					esm.num_layers
      _lowerCAmelCase       :  Dict   				=					esm.attention_heads
      _lowerCAmelCase       :  Union[str, Any]   				=					4 * embed_dim  # This is hardcoded in ESM-2
      _lowerCAmelCase       :  int   				=					esm.token_dropout
      _lowerCAmelCase       :  List[Any]   				=					False  # This code path does not exist in ESM-2
      _lowerCAmelCase       :  Any   				=					"""rotary"""
      _lowerCAmelCase       :  Any   				=					False
      _lowerCAmelCase       :  Optional[Any]   				=					None
  
    if is_folding_model:
      _lowerCAmelCase       :  str   				=					esm.esm.alphabet
    _lowerCAmelCase       :  int   				=					tuple(alphabet.all_toks		)
    _lowerCAmelCase       :  Union[str, Any]   				=					alphabet.mask_idx
    _lowerCAmelCase       :  Optional[Any]   				=					alphabet.padding_idx
  
    if is_folding_model:
      _lowerCAmelCase       :  int   				=					esm.esm
    else:
      _lowerCAmelCase       :  Tuple   				=					esm
  
    _lowerCAmelCase       :  Optional[Any]   				=					EsmConfig(
        vocab_size=original_esm_model.embed_tokens.num_embeddings  ,      mask_token_id=_lowerCamelCase  ,      hidden_size=_lowerCamelCase  ,      num_hidden_layers=_lowerCamelCase  ,      num_attention_heads=_lowerCamelCase  ,      intermediate_size=_lowerCamelCase  ,      max_position_embeddings=10_26  ,      layer_norm_eps=1e-5  ,      attention_probs_dropout_prob=0.0  ,      hidden_dropout_prob=0.0  ,      pad_token_id=_lowerCamelCase  ,      emb_layer_norm_before=_lowerCamelCase  ,      token_dropout=_lowerCamelCase  ,      position_embedding_type=_lowerCamelCase  ,      is_folding_model=_lowerCamelCase  ,      esmfold_config=_lowerCamelCase  ,      vocab_list=_lowerCamelCase  ,      )
    if classification_head:
      _lowerCAmelCase       :  Union[str, Any]   				=					esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
    print("""Our ESM config:"""  ,      _lowerCamelCase		)
  
    if model.startswith("""esmfold"""		):
      _lowerCAmelCase       :  Optional[int]   				=					EsmForProteinFolding
    elif classification_head:
      _lowerCAmelCase       :  List[Any]   				=					EsmForSequenceClassification
    else:
      _lowerCAmelCase       :  int   				=					EsmForMaskedLM
    _lowerCAmelCase       :  Union[str, Any]   				=					model_class(_lowerCamelCase		)
    model.eval()
  
    # Now let's copy all the weights.
    # Embeddings
    _lowerCAmelCase       :  int   				=					original_esm_model.embed_tokens.weight
    if position_embedding_type == "absolute":
      _lowerCAmelCase       :  Any   				=					original_esm_model.embed_positions.weight
  
    if config.emb_layer_norm_before:
      _lowerCAmelCase       :  List[Any]   				=					original_esm_model.emb_layer_norm_before.weight
      _lowerCAmelCase       :  str   				=					original_esm_model.emb_layer_norm_before.bias
  
    _lowerCAmelCase       :  str   				=					original_esm_model.emb_layer_norm_after.weight
    _lowerCAmelCase       :  Optional[int]   				=					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       :  Tuple   				=					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       :  List[str]   				=					esm_layer.self_attn.q_proj.weight
      _lowerCAmelCase       :  int   				=					esm_layer.self_attn.q_proj.bias
      _lowerCAmelCase       :  int   				=					esm_layer.self_attn.k_proj.weight
      _lowerCAmelCase       :  List[str]   				=					esm_layer.self_attn.k_proj.bias
      _lowerCAmelCase       :  Tuple   				=					esm_layer.self_attn.v_proj.weight
      _lowerCAmelCase       :  Optional[int]   				=					esm_layer.self_attn.v_proj.bias
  
      if getattr(esm_layer.self_attn  ,      """rot_emb"""  ,      _lowerCamelCase		) 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       :  List[Any]   				=					esm_layer.self_attn.rot_emb.inv_freq
  
      # LayerNorm changes for pre-activation
      _lowerCAmelCase       :  Dict   				=					esm_layer.self_attn_layer_norm.weight
      _lowerCAmelCase       :  str   				=					esm_layer.self_attn_layer_norm.bias
      _lowerCAmelCase       :  List[Any]   				=					esm_layer.final_layer_norm.weight
      _lowerCAmelCase       :  List[str]   				=					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       :  Optional[Any]   				=					esm_layer.self_attn.out_proj.weight
      _lowerCAmelCase       :  str   				=					esm_layer.self_attn.out_proj.bias
  
      # intermediate
      _lowerCAmelCase       :  EsmIntermediate   				=					layer.intermediate
      assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
      _lowerCAmelCase       :  str   				=					esm_layer.fca.weight
      _lowerCAmelCase       :  Dict   				=					esm_layer.fca.bias
  
      # output
      _lowerCAmelCase       :  EsmOutput   				=					layer.output
      assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
      _lowerCAmelCase       :  Optional[int]   				=					esm_layer.fca.weight
      _lowerCAmelCase       :  List[Any]   				=					esm_layer.fca.bias
      # end of layer
  
    if is_folding_model:
      _lowerCAmelCase       :  Optional[Any]   				=					esm.esm_s_combine.data
      _lowerCAmelCase       :  int   				=					esm.afa_to_esm.data
      transfer_and_check_weights(esm.embedding  ,      model.embedding		)
      transfer_and_check_weights(esm.esm_s_mlp  ,      model.esm_s_mlp		)
      transfer_and_check_weights(esm.trunk  ,      model.trunk		)
      transfer_and_check_weights(esm.distogram_head  ,      model.distogram_head		)
      transfer_and_check_weights(esm.ptm_head  ,      model.ptm_head		)
      transfer_and_check_weights(esm.lm_head  ,      model.lm_head		)
      transfer_and_check_weights(esm.lddt_head  ,      model.lddt_head		)
  
    elif classification_head:
      _lowerCAmelCase       :  List[str]   				=					esm.esm.classification_heads["""mnli"""].dense.weight
      _lowerCAmelCase       :  int   				=					esm.classification_heads["""mnli"""].dense.bias
      _lowerCAmelCase       :  List[str]   				=					esm.classification_heads["""mnli"""].out_proj.weight
      _lowerCAmelCase       :  Tuple   				=					esm.classification_heads["""mnli"""].out_proj.bias
    else:
      # LM Head
      _lowerCAmelCase       :  int   				=					esm.lm_head.dense.weight
      _lowerCAmelCase       :  List[Any]   				=					esm.lm_head.dense.bias
      _lowerCAmelCase       :  Tuple   				=					esm.lm_head.layer_norm.weight
      _lowerCAmelCase       :  Union[str, Any]   				=					esm.lm_head.layer_norm.bias
      _lowerCAmelCase       :  Dict   				=					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       :  Any   				=					SAMPLE_DATA[:2]
    else:
      _lowerCAmelCase       :  Tuple   				=					SAMPLE_DATA
  
    if is_folding_model:
      _lowerCAmelCase       :  str   				=					get_esmfold_tokenizer()
      _lowerCAmelCase       :  Dict   				=					hf_tokenizer(
          [row[1] for row in sample_data]  ,      return_tensors="""pt"""  ,      padding=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase		)
      _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase       :  Optional[int]   				=					esmfold_encode_sequences([row[1] for row in sample_data]		)
      _lowerCAmelCase       :  int   				=					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       :  List[Any]   				=					alphabet.get_batch_converter()
      _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase       :  List[Any]   				=					batch_converter(_lowerCamelCase		)
      # Prepare tokenizer and make sure it matches
      with TemporaryDirectory() as tempdir:
        _lowerCAmelCase       :  List[str]   				=					"""\n""".join(alphabet.all_toks		)
        _lowerCAmelCase       :  Any   				=					Path(_lowerCamelCase		) / """vocab.txt"""
        vocab_file.write_text(_lowerCamelCase		)
        _lowerCAmelCase       :  Dict   				=					EsmTokenizer(vocab_file=str(_lowerCamelCase		)		)
  
      _lowerCAmelCase       :  str   				=					hf_tokenizer([row[1] for row in sample_data]  ,      return_tensors="""pt"""  ,      padding=_lowerCamelCase		)
      _lowerCAmelCase       :  Optional[Any]   				=					torch.all(hf_tokens["""input_ids"""] == batch_tokens		)
  
    print("""Do both models tokenizers output the same tokens?"""  ,      """🔥""" if success else """💩"""		)
    if not success:
      raise Exception("""Tokenization does not match!"""		)
  
    with torch.no_grad():
      if is_folding_model:
        # Let's test the model in parts
        # ESMFold always converts the ESM stem to float16, which requires float16 ops
        # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
        # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
        # original and the converted model on the GPU at the same time.
        _lowerCAmelCase       :  Tuple   				=					esm.cuda().infer([row[1] for row in sample_data]		)
        _lowerCAmelCase       :  List[Any]   				=					model.cuda()(
            input_ids=hf_tokens["""input_ids"""].cuda()  ,      attention_mask=hf_tokens["""attention_mask"""].cuda()		)
      else:
        _lowerCAmelCase       :  Tuple   				=					model(**_lowerCamelCase  ,      output_hidden_states=_lowerCamelCase		)
        _lowerCAmelCase       :  List[str]   				=					our_output["""logits"""]
        if classification_head:
          _lowerCAmelCase       :  int   				=					esm.model.classification_heads["""mnli"""](esm.extract_features(_lowerCamelCase		)		)
        else:
          _lowerCAmelCase       :  Optional[int]   				=					esm(hf_tokens["""input_ids"""]  ,      repr_layers=list(range(9_99		)		)		)
          _lowerCAmelCase       :  int   				=					their_output["""logits"""]
  
      if is_folding_model:
        _lowerCAmelCase       :  Optional[Any]   				=					torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]		)		).item()
        _lowerCAmelCase       :  Dict   				=					torch.allclose(our_output["""positions"""]  ,      their_output["""positions"""]  ,      atol=1e-5		)
      else:
        _lowerCAmelCase       :  str   				=					torch.max(torch.abs(our_output - their_output		)		).item()
        _lowerCAmelCase       :  str   				=					torch.allclose(_lowerCamelCase  ,      _lowerCamelCase  ,      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       :  str   				=					model.predict_contacts(hf_tokens["""input_ids"""]  ,      hf_tokens["""attention_mask"""]		)
        _lowerCAmelCase       :  Tuple   				=					esm.predict_contacts(hf_tokens["""input_ids"""]		)
        _lowerCAmelCase       :  int   				=					torch.max(torch.abs(our_output - their_output		)		).item()
        _lowerCAmelCase       :  Union[str, Any]   				=					torch.allclose(_lowerCamelCase  ,      _lowerCamelCase  ,      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(_lowerCamelCase		).mkdir(parents=_lowerCamelCase  ,      exist_ok=_lowerCamelCase		)
      print(f'Saving model to {pytorch_dump_folder_path}'		)
      model.save_pretrained(_lowerCamelCase		)
  
      del esm  # Free up some memory before continuing
  
    print(f'Saving tokenizer to {pytorch_dump_folder_path}'		)
    hf_tokenizer.save_pretrained(_lowerCamelCase		)
  
    if push_to_repo:
      model.push_to_hub(repo_id=_lowerCamelCase  ,      use_auth_token=_lowerCamelCase		)
      hf_tokenizer.push_to_hub(repo_id=_lowerCamelCase  ,      use_auth_token=_lowerCamelCase		)
  
  
  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
       )
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  UpperCamelCase_     			=       {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTSanJapaneseForConditionalGeneration""",
           """GPTSanJapaneseModel""",
           """GPTSanJapanesePreTrainedModel""",
       ]
       UpperCamelCase_     			=       [
           """GPTSanJapaneseTokenizer""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  