




'''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 = 512   )					->int:






       '''simple docstring'''




       lowercase	:		List[Any]    =   x.shape[1] if len(x.shape   ) > 1 else 1
       lowercase	:		int    =   1 + np.arange(0 ,					feature_num * offset ,					lowerCamelCase ,					dtype=np.intaa   )
       lowercase	:		Dict    =   x + feature_offset
       return x




def 		a_(			lowerCamelCase ,					lowerCamelCase=True   )					->str:






       '''simple docstring'''




       requires_backends(lowerCamelCase ,					['''cython''']   )

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

       lowercase	:		Dict    =   np.asarray(item['''edge_index'''] ,					dtype=np.intaa   )

       lowercase	:		Any    =   convert_to_single_emb(lowerCamelCase   ) + 1
       lowercase	:		Dict    =   item['''num_nodes''']

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

       # node adj matrix [num_nodes, num_nodes] bool
       lowercase	:		Tuple    =   np.zeros([num_nodes, num_nodes] ,					dtype=lowerCamelCase   )
       lowercase	:		List[str]    =   True

       lowercase,  lowercase	:		Union[str, Any]    =   algos_graphormer.floyd_warshall(lowerCamelCase   )
       lowercase	:		str    =   np.amax(lowerCamelCase   )

       lowercase	:		str    =   algos_graphormer.gen_edge_input(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )
       lowercase	:		List[str]    =   np.zeros([num_nodes + 1, num_nodes + 1] ,					dtype=np.single   )  # with graph token

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

       return item





class 	SCREAMING_SNAKE_CASE_		:



 def __init__(	self ,		A=20 ,		A=False			):






        '''simple docstring'''




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

        lowercase	:		Tuple    =   spatial_pos_max
        lowercase	:		Tuple    =   on_the_fly_processing





 def __call__(	self ,		A			):






        '''simple docstring'''




        if self.on_the_fly_processing:
               lowercase	:		Dict    =   [preprocess_item(A			) for i in features]

        if not isinstance(features[0] ,		A			):
               lowercase	:		Optional[int]    =   [vars(A			) for f in features]
        lowercase	:		Any    =   {}

        lowercase	:		str    =   max(len(i['''input_nodes''']			) for i in features			)
        lowercase	:		Optional[int]    =   len(features[0]['''input_nodes'''][0]			)
        lowercase	:		int    =   len(features[0]['''attn_edge_type'''][0][0]			)
        lowercase	:		int    =   max(len(i['''input_edges'''][0][0]			) for i in features			)
        lowercase	:		Optional[int]    =   len(features[0]['''input_edges'''][0][0][0]			)
        lowercase	:		Dict    =   len(A			)

        lowercase	:		Any    =   torch.zeros(A ,		max_node_num + 1 ,		max_node_num + 1 ,		dtype=torch.float			)
        lowercase	:		Any    =   torch.zeros(A ,		A ,		A ,		A ,		dtype=torch.long			)
        lowercase	:		Optional[int]    =   torch.zeros(A ,		A ,		A ,		dtype=torch.long			)
        lowercase	:		int    =   torch.zeros(A ,		A ,		dtype=torch.long			)
        lowercase	:		Optional[int]    =   torch.zeros(A ,		A ,		A ,		dtype=torch.long			)
        lowercase	:		Dict    =   torch.zeros(
            A ,		A ,		A ,		A ,		A ,		dtype=torch.long			)

        for ix, f in enumerate(A			):
               for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                      lowercase	:		List[str]    =   torch.tensor(f[k]			)

               if len(f['''attn_bias'''][1:, 1:][f['''spatial_pos'''] >= self.spatial_pos_max]			) > 0:
                      lowercase	:		Dict    =   float('''-inf'''			)

               lowercase	:		Union[str, Any]    =   f['''attn_bias''']
               lowercase	:		Dict    =   f[
                   '''attn_edge_type'''
               ]
               lowercase	:		int    =   f['''spatial_pos''']
               lowercase	:		Tuple    =   f['''in_degree''']
               lowercase	:		List[str]    =   f['''input_nodes''']
               lowercase	:		Dict    =   f['''input_edges''']

        lowercase	:		Any    =   batch['''in_degree''']

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

        return batch










'''simple docstring'''






def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       if len(lowerCamelCase   ) < len(lowerCamelCase   ):
              return levenshtein_distance(lowerCamelCase ,					lowerCamelCase   )

       if len(lowerCamelCase   ) == 0:
              return len(lowerCamelCase   )

       lowercase	:		List[Any]    =   list(range(len(lowerCamelCase   ) + 1   )   )

       for i, ca in enumerate(lowerCamelCase   ):
              lowercase	:		Any    =   [i + 1]

              for j, ca in enumerate(lowerCamelCase   ):
                     # Calculate insertions, deletions and substitutions
                     lowercase	:		List[Any]    =   previous_row[j + 1] + 1
                     lowercase	:		str    =   current_row[j] + 1
                     lowercase	:		List[Any]    =   previous_row[j] + (ca != ca)

                     # Get the minimum to append to the current row
                     current_row.append(min(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )   )

              # Store the previous row
              lowercase	:		List[str]    =   current_row

       # Returns the last element (distance)
       return previous_row[-1]


if __name__ == "__main__":
      lowercase						:   Dict      						=							input('''Enter the first word:\n''').strip()
      lowercase						:   Tuple      						=							input('''Enter the second word:\n''').strip()

      lowercase						:   Tuple      						=							levenshtein_distance(first_word, second_word)
      print(f'Levenshtein distance between {first_word} and {second_word} is {result}')




