code: 
  
  """simple docstring"""
  from typing import Any, Dict, List, Mapping
  
  import numpy as np
  import torch
  
  from ...utils import is_cython_available, requires_backends
  
  
  if is_cython_available():
      import pyximport
  
      pyximport.install(setup_args={"""include_dirs""": np.get_include()})
      from . import algos_graphormer  # noqa E402
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase = 5_12						)  ->   int:
        UpperCAmelCase__ :       Any						=						x.shape[1] if len(x.shape						) > 1 else 1
        UpperCAmelCase__ :       Union[str, Any]						=						1 + np.arange(0 ,  feature_num * offset ,  lowerCAmelCase ,  dtype=np.intaa						)
        UpperCAmelCase__ :       Optional[Any]						=						x + feature_offset
        return x
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase=True						)  ->   Optional[Any]:
        requires_backends(lowerCAmelCase ,  ["""cython"""]						)
  
        if keep_features and "edge_attr" in item.keys():  # edge_attr
              UpperCAmelCase__ :       List[Any]						=						np.asarray(item["""edge_attr"""] ,  dtype=np.intaa						)
        else:
              UpperCAmelCase__ :       Union[str, Any]						=						np.ones((len(item["""edge_index"""][0]						), 1) ,  dtype=np.intaa						)  # same embedding for all
  
        if keep_features and "node_feat" in item.keys():  # input_nodes
              UpperCAmelCase__ :       Dict						=						np.asarray(item["""node_feat"""] ,  dtype=np.intaa						)
        else:
              UpperCAmelCase__ :       int						=						np.ones((item["""num_nodes"""], 1) ,  dtype=np.intaa						)  # same embedding for all
  
        UpperCAmelCase__ :       List[Any]						=						np.asarray(item["""edge_index"""] ,  dtype=np.intaa						)
  
        UpperCAmelCase__ :       Any						=						convert_to_single_emb(lowerCAmelCase						) + 1
        UpperCAmelCase__ :       Dict						=						item["""num_nodes"""]
  
        if len(edge_attr.shape						) == 1:
              UpperCAmelCase__ :       List[Any]						=						edge_attr[:, None]
        UpperCAmelCase__ :       Optional[Any]						=						np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]] ,  dtype=np.intaa						)
        UpperCAmelCase__ :       Union[str, Any]						=						convert_to_single_emb(lowerCAmelCase						) + 1
  
        # node adj matrix [num_nodes, num_nodes] bool
        UpperCAmelCase__ :       str						=						np.zeros([num_nodes, num_nodes] ,  dtype=lowerCAmelCase						)
        UpperCAmelCase__ :       List[Any]						=						True
  
        UpperCAmelCase__ :       Any						=						algos_graphormer.floyd_warshall(lowerCAmelCase						)
        UpperCAmelCase__ :       Tuple						=						np.amax(lowerCAmelCase						)
  
        UpperCAmelCase__ :       Dict						=						algos_graphormer.gen_edge_input(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
        UpperCAmelCase__ :       str						=						np.zeros([num_nodes + 1, num_nodes + 1] ,  dtype=np.single						)  # with graph token
  
        # combine
        UpperCAmelCase__ :       Optional[int]						=						input_nodes + 1  # we shift all indices by one for padding
        UpperCAmelCase__ :       Optional[Any]						=						attn_bias
        UpperCAmelCase__ :       List[Any]						=						attn_edge_type
        UpperCAmelCase__ :       Optional[Any]						=						shortest_path_result.astype(np.intaa						) + 1  # we shift all indices by one for padding
        UpperCAmelCase__ :       Dict						=						np.sum(lowerCAmelCase ,  axis=1						).reshape(-1						) + 1  # we shift all indices by one for padding
        UpperCAmelCase__ :       int						=						item["""in_degree"""]  # for undirected graph
        UpperCAmelCase__ :       Optional[Any]						=						input_edges + 1  # we shift all indices by one for padding
        if "labels" not in item:
              UpperCAmelCase__ :       Any						=						item["""y"""]
  
        return item
  
  
  
  
  
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase=20       ,					_lowerCamelCase=False  ):
  
  
          """simple docstring"""
  
          if not is_cython_available():
                raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""  )
  
          UpperCAmelCase__ :       str						=						spatial_pos_max
          UpperCAmelCase__ :       Dict						=						on_the_fly_processing
  
  
  
  
    def __call__(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          if self.on_the_fly_processing:
                UpperCAmelCase__ :       Dict						=						[preprocess_item(_lowerCamelCase  ) for i in features]
  
          if not isinstance(features[0]       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       Tuple						=						[vars(_lowerCamelCase  ) for f in features]
          UpperCAmelCase__ :       Any						=						{}
  
          UpperCAmelCase__ :       Union[str, Any]						=						max(len(i["""input_nodes"""]  ) for i in features  )
          UpperCAmelCase__ :       Union[str, Any]						=						len(features[0]["""input_nodes"""][0]  )
          UpperCAmelCase__ :       Tuple						=						len(features[0]["""attn_edge_type"""][0][0]  )
          UpperCAmelCase__ :       Dict						=						max(len(i["""input_edges"""][0][0]  ) for i in features  )
          UpperCAmelCase__ :       Optional[int]						=						len(features[0]["""input_edges"""][0][0][0]  )
          UpperCAmelCase__ :       Optional[Any]						=						len(_lowerCamelCase  )
  
          UpperCAmelCase__ :       Dict						=						torch.zeros(_lowerCamelCase       ,					max_node_num + 1       ,					max_node_num + 1       ,					dtype=torch.float  )
          UpperCAmelCase__ :       str						=						torch.zeros(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					dtype=torch.long  )
          UpperCAmelCase__ :       Optional[Any]						=						torch.zeros(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					dtype=torch.long  )
          UpperCAmelCase__ :       Optional[Any]						=						torch.zeros(_lowerCamelCase       ,					_lowerCamelCase       ,					dtype=torch.long  )
          UpperCAmelCase__ :       Union[str, Any]						=						torch.zeros(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					dtype=torch.long  )
          UpperCAmelCase__ :       Tuple						=						torch.zeros(
              _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					dtype=torch.long  )
  
          for ix, f in enumerate(_lowerCamelCase  ):
                for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                      UpperCAmelCase__ :       int						=						torch.tensor(f[k]  )
  
                if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]  ) > 0:
                      UpperCAmelCase__ :       Optional[Any]						=						float("""-inf"""  )
  
                UpperCAmelCase__ :       Tuple						=						f["""attn_bias"""]
                UpperCAmelCase__ :       Dict						=						f[
                    """attn_edge_type"""
                ]
                UpperCAmelCase__ :       Optional[int]						=						f["""spatial_pos"""]
                UpperCAmelCase__ :       List[str]						=						f["""in_degree"""]
                UpperCAmelCase__ :       Tuple						=						f["""input_nodes"""]
                UpperCAmelCase__ :       int						=						f["""input_edges"""]
  
          UpperCAmelCase__ :       Dict						=						batch["""in_degree"""]
  
          UpperCAmelCase__ :       Tuple						=						features[0]["""labels"""]
          if len(_lowerCamelCase  ) == 1:  # one task
                if isinstance(sample[0]       ,					_lowerCamelCase  ):  # regression
                      UpperCAmelCase__ :       str						=						torch.from_numpy(np.concatenate([i["""labels"""] for i in features]  )  )
                else:  # binary classification
                      UpperCAmelCase__ :       Optional[int]						=						torch.from_numpy(np.concatenate([i["""labels"""] for i in features]  )  )
          else:  # multi task classification, left to float to keep the NaNs
                UpperCAmelCase__ :       Optional[int]						=						torch.from_numpy(np.stack([i["""labels"""] for i in features]       ,					axis=0  )  )
  
          return batch
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
        # Initialise PyTorch model
        UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
        UpperCAmelCase__ :       Any						=						GPTNeoConfig(
            hidden_size=config_json["""n_embd"""] ,  num_layers=config_json["""n_layer"""] ,  num_heads=config_json["""n_head"""] ,  attention_types=config_json["""attention_types"""] ,  max_position_embeddings=config_json["""n_positions"""] ,  resid_dropout=config_json["""res_dropout"""] ,  embed_dropout=config_json["""embed_dropout"""] ,  attention_dropout=config_json["""attn_dropout"""] ,  )
        print(F"""Building PyTorch model from configuration: {config}"""						)
        UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        # Save pytorch-model
        print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
        model.save_pretrained(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      _A						=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained mesh-tf model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      _A						=     parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  