


'''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 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = 512     ):






       """simple docstring"""





       snake_case__					:	Optional[int]          =					x.shape[1] if len(x.shape     ) > 1 else 1
       snake_case__					:	Any          =					1 + np.arange(0    ,       feature_num * offset    ,       UpperCAmelCase_    ,       dtype=np.intaa     )
       snake_case__					:	str          =					x + feature_offset
       return x



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase=True     ):






       """simple docstring"""





       requires_backends(UpperCAmelCase_    ,       ["""cython"""]     )

       if keep_features and "edge_attr" in item.keys():  # edge_attr
              snake_case__					:	Optional[Any]          =					np.asarray(item["""edge_attr"""]    ,       dtype=np.intaa     )
       else:
              snake_case__					:	str          =					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
              snake_case__					:	str          =					np.asarray(item["""node_feat"""]    ,       dtype=np.intaa     )
       else:
              snake_case__					:	str          =					np.ones((item["""num_nodes"""], 1)    ,       dtype=np.intaa     )  # same embedding for all

       snake_case__					:	Dict          =					np.asarray(item["""edge_index"""]    ,       dtype=np.intaa     )

       snake_case__					:	Union[str, Any]          =					convert_to_single_emb(UpperCAmelCase_     ) + 1
       snake_case__					:	List[Any]          =					item["""num_nodes"""]

       if len(edge_attr.shape     ) == 1:
              snake_case__					:	Any          =					edge_attr[:, None]
       snake_case__					:	List[Any]          =					np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]    ,       dtype=np.intaa     )
       snake_case__					:	str          =					convert_to_single_emb(UpperCAmelCase_     ) + 1

       # node adj matrix [num_nodes, num_nodes] bool
       snake_case__					:	Any          =					np.zeros([num_nodes, num_nodes]    ,       dtype=UpperCAmelCase_     )
       snake_case__					:	Dict          =					True

       snake_case__					:	Dict          =					algos_graphormer.floyd_warshall(UpperCAmelCase_     )
       snake_case__					:	Tuple          =					np.amax(UpperCAmelCase_     )

       snake_case__					:	Union[str, Any]          =					algos_graphormer.gen_edge_input(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_     )
       snake_case__					:	Dict          =					np.zeros([num_nodes + 1, num_nodes + 1]    ,       dtype=np.single     )  # with graph token

       # combine
       snake_case__					:	str          =					input_nodes + 1  # we shift all indices by one for padding
       snake_case__					:	Union[str, Any]          =					attn_bias
       snake_case__					:	str          =					attn_edge_type
       snake_case__					:	Union[str, Any]          =					shortest_path_result.astype(np.intaa     ) + 1  # we shift all indices by one for padding
       snake_case__					:	List[Any]          =					np.sum(UpperCAmelCase_    ,       axis=1     ).reshape(-1     ) + 1  # we shift all indices by one for padding
       snake_case__					:	Optional[int]          =					item["""in_degree"""]  # for undirected graph
       snake_case__					:	int          =					input_edges + 1  # we shift all indices by one for padding
       if "labels" not in item:
              snake_case__					:	str          =					item["""y"""]

       return item







class 					_A						:



   '''simple docstring'''






   def __init__(	self	:					Any					, lowerCamelCase	:					Dict=20					, lowerCamelCase	:					Union[str, Any]=False						)->	Optional[Any]:


          if not is_cython_available():
                 raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""						)

          snake_case__					:	str          =					spatial_pos_max
          snake_case__					:	List[Any]          =					on_the_fly_processing




   def __call__(	self	:					int					, lowerCamelCase	:					List[str]						)->	Tuple:


          if self.on_the_fly_processing:
                 snake_case__					:	Optional[int]          =					[preprocess_item(__lowerCAmelCase						) for i in features]

          if not isinstance(features[0]					, __lowerCAmelCase						):
                 snake_case__					:	Union[str, Any]          =					[vars(__lowerCAmelCase						) for f in features]
          snake_case__					:	Optional[Any]          =					{}

          snake_case__					:	List[str]          =					max(len(i["""input_nodes"""]						) for i in features						)
          snake_case__					:	Dict          =					len(features[0]["""input_nodes"""][0]						)
          snake_case__					:	Optional[int]          =					len(features[0]["""attn_edge_type"""][0][0]						)
          snake_case__					:	List[Any]          =					max(len(i["""input_edges"""][0][0]						) for i in features						)
          snake_case__					:	Dict          =					len(features[0]["""input_edges"""][0][0][0]						)
          snake_case__					:	Union[str, Any]          =					len(__lowerCAmelCase						)

          snake_case__					:	str          =					torch.zeros(__lowerCAmelCase					, max_node_num + 1					, max_node_num + 1					, dtype=torch.float						)
          snake_case__					:	List[Any]          =					torch.zeros(__lowerCAmelCase					, __lowerCAmelCase					, __lowerCAmelCase					, __lowerCAmelCase					, dtype=torch.long						)
          snake_case__					:	Optional[Any]          =					torch.zeros(__lowerCAmelCase					, __lowerCAmelCase					, __lowerCAmelCase					, dtype=torch.long						)
          snake_case__					:	List[Any]          =					torch.zeros(__lowerCAmelCase					, __lowerCAmelCase					, dtype=torch.long						)
          snake_case__					:	Tuple          =					torch.zeros(__lowerCAmelCase					, __lowerCAmelCase					, __lowerCAmelCase					, dtype=torch.long						)
          snake_case__					:	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"]:
                        snake_case__					:	Tuple          =					torch.tensor(f[k]						)

                 if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]						) > 0:
                        snake_case__					:	List[str]          =					float("""-inf"""						)

                 snake_case__					:	List[str]          =					f["""attn_bias"""]
                 snake_case__					:	Any          =					f[
                     """attn_edge_type"""
                 ]
                 snake_case__					:	Dict          =					f["""spatial_pos"""]
                 snake_case__					:	Dict          =					f["""in_degree"""]
                 snake_case__					:	str          =					f["""input_nodes"""]
                 snake_case__					:	List[Any]          =					f["""input_edges"""]

          snake_case__					:	Union[str, Any]          =					batch["""in_degree"""]

          snake_case__					:	Optional[Any]          =					features[0]["""labels"""]
          if len(__lowerCAmelCase						) == 1:  # one task
                 if isinstance(sample[0]					, __lowerCAmelCase						):  # regression
                        snake_case__					:	List[str]          =					torch.from_numpy(np.concatenate([i["""labels"""] for i in features]						)						)
                 else:  # binary classification
                        snake_case__					:	Any          =					torch.from_numpy(np.concatenate([i["""labels"""] for i in features]						)						)
          else:  # multi task classification, left to float to keep the NaNs
                 snake_case__					:	int          =					torch.from_numpy(np.stack([i["""labels"""] for i in features]					, axis=0						)						)

          return batch










'''simple docstring'''





from __future__ import annotations

import sys




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							str							       =      letter
      _a				:							int							       =      freq
      _a				:							dict[str, str]							       =      {}



  def __repr__(			self ):







      '''simple docstring'''


      return f'''{self.letter}:{self.freq}'''






class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							int							       =      freq
      _a				:							Letter | TreeNode							       =      left
      _a				:							Letter | TreeNode							       =      right






def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							dict[str, int]							       =      {}
    with open(UpperCAmelCase_						) as f:
        while True:
            _a				:							Any							       =      f.read(1						)
            if not c:
                break
            _a				:							Optional[Any]							       =      chars[c] + 1 if c in chars else 1
    return sorted((Letter(UpperCAmelCase_   ,	UpperCAmelCase_						) for c, f in chars.items())   ,	key=lambda UpperCAmelCase_						: x.freq						)





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							list[Letter | TreeNode]							       =      letters  # type: ignore
    while len(UpperCAmelCase_						) > 1:
        _a				:							List[Any]							       =      response.pop(0						)
        _a				:							List[str]							       =      response.pop(0						)
        _a				:							Dict							       =      left.freq + right.freq
        _a				:							Any							       =      TreeNode(UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						)
        response.append(UpperCAmelCase_						)
        response.sort(key=lambda UpperCAmelCase_						: x.freq						)
    return response[0]





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    if isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
        _a				:							int							       =      bitstring
        return [root]
    _a				:							TreeNode							       =      root  # type: ignore
    _a				:							Optional[Any]							       =      []
    letters += traverse_tree(treenode.left   ,	bitstring + """0"""						)
    letters += traverse_tree(treenode.right   ,	bitstring + """1"""						)
    return letters





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Optional[Any]							       =      parse_file(UpperCAmelCase_						)
    _a				:							Tuple							       =      build_tree(UpperCAmelCase_						)
    _a				:							Union[str, Any]							       =      {
        k: v for letter in traverse_tree(UpperCAmelCase_   ,	""""""						) for k, v in letter.bitstring.items()
    }
    print(F'''Huffman Coding  of {file_path}: '''						)
    with open(UpperCAmelCase_						) as f:
        while True:
            _a				:							Any							       =      f.read(1						)
            if not c:
                break
            print(letters[c]   ,	end=""" """						)
    print()


if __name__ == "__main__":
  # pass the file path to the huffman function
  huffman(sys.argv[1])


