


"""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 __lowercase  (   lowerCamelCase_	:   str    ,       lowerCamelCase_	:   int = 512	):
				SCREAMING_SNAKE_CASE__  				=					x.shape[1] if len(x.shape	) > 1 else 1
				SCREAMING_SNAKE_CASE__  				=					1 + np.arange(0    ,       feature_num * offset    ,       lowerCamelCase_    ,       dtype=np.intaa	)
				SCREAMING_SNAKE_CASE__  				=					x + feature_offset
				return x


def __lowercase  (   lowerCamelCase_	:   Tuple    ,       lowerCamelCase_	:   int=True	):
				requires_backends(lowerCamelCase_    ,       ["cython"]	)

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

				SCREAMING_SNAKE_CASE__  				=					np.asarray(item["edge_index"]    ,       dtype=np.intaa	)

				SCREAMING_SNAKE_CASE__  				=					convert_to_single_emb(lowerCamelCase_	) + 1
				SCREAMING_SNAKE_CASE__  				=					item["num_nodes"]

				if len(edge_attr.shape	) == 1:
								SCREAMING_SNAKE_CASE__  				=					edge_attr[:, None]
				SCREAMING_SNAKE_CASE__  				=					np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]    ,       dtype=np.intaa	)
				SCREAMING_SNAKE_CASE__  				=					convert_to_single_emb(lowerCamelCase_	) + 1

				# node adj matrix [num_nodes, num_nodes] bool
				SCREAMING_SNAKE_CASE__  				=					np.zeros([num_nodes, num_nodes]    ,       dtype=lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					True

				SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					algos_graphormer.floyd_warshall(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					np.amax(lowerCamelCase_	)

				SCREAMING_SNAKE_CASE__  				=					algos_graphormer.gen_edge_input(lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					np.zeros([num_nodes + 1, num_nodes + 1]    ,       dtype=np.single	)  # with graph token

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

				return item


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__=20							,		UpperCAmelCase__=False  ):
								if not is_cython_available():
												raise ImportError("Graphormer preprocessing needs Cython (pyximport)"  )

								SCREAMING_SNAKE_CASE__  				=					spatial_pos_max
								SCREAMING_SNAKE_CASE__  				=					on_the_fly_processing






				def __call__(    self							,		UpperCAmelCase__  ):
								if self.on_the_fly_processing:
												SCREAMING_SNAKE_CASE__  				=					[preprocess_item(UpperCAmelCase__  ) for i in features]

								if not isinstance(features[0]							,		UpperCAmelCase__  ):
												SCREAMING_SNAKE_CASE__  				=					[vars(UpperCAmelCase__  ) for f in features]
								SCREAMING_SNAKE_CASE__  				=					{}

								SCREAMING_SNAKE_CASE__  				=					max(len(i["input_nodes"]  ) for i in features  )
								SCREAMING_SNAKE_CASE__  				=					len(features[0]["input_nodes"][0]  )
								SCREAMING_SNAKE_CASE__  				=					len(features[0]["attn_edge_type"][0][0]  )
								SCREAMING_SNAKE_CASE__  				=					max(len(i["input_edges"][0][0]  ) for i in features  )
								SCREAMING_SNAKE_CASE__  				=					len(features[0]["input_edges"][0][0][0]  )
								SCREAMING_SNAKE_CASE__  				=					len(UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					torch.zeros(UpperCAmelCase__							,		max_node_num + 1							,		max_node_num + 1							,		dtype=torch.float  )
								SCREAMING_SNAKE_CASE__  				=					torch.zeros(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		dtype=torch.long  )
								SCREAMING_SNAKE_CASE__  				=					torch.zeros(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		dtype=torch.long  )
								SCREAMING_SNAKE_CASE__  				=					torch.zeros(UpperCAmelCase__							,		UpperCAmelCase__							,		dtype=torch.long  )
								SCREAMING_SNAKE_CASE__  				=					torch.zeros(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		dtype=torch.long  )
								SCREAMING_SNAKE_CASE__  				=					torch.zeros(
								    UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		dtype=torch.long  )

								for ix, f in enumerate(UpperCAmelCase__  ):
												for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																SCREAMING_SNAKE_CASE__  				=					torch.tensor(f[k]  )

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

												SCREAMING_SNAKE_CASE__  				=					f["attn_bias"]
												SCREAMING_SNAKE_CASE__  				=					f[
												    "attn_edge_type"
												]
												SCREAMING_SNAKE_CASE__  				=					f["spatial_pos"]
												SCREAMING_SNAKE_CASE__  				=					f["in_degree"]
												SCREAMING_SNAKE_CASE__  				=					f["input_nodes"]
												SCREAMING_SNAKE_CASE__  				=					f["input_edges"]

								SCREAMING_SNAKE_CASE__  				=					batch["in_degree"]

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

								return batch




"""simple docstring"""






from __future__ import annotations

import sys


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					letter
								SCREAMING_SNAKE_CASE__  				=					freq
								SCREAMING_SNAKE_CASE__  				=					{}






				def __repr__(    self  ):
								return f'''{self.letter}:{self.freq}'''


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					freq
								SCREAMING_SNAKE_CASE__  				=					left
								SCREAMING_SNAKE_CASE__  				=					right


def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					{}
				with open(lowerCamelCase_	) as f:
								while True:
												SCREAMING_SNAKE_CASE__  				=					f.read(1	)
												if not c:
																break
												SCREAMING_SNAKE_CASE__  				=					chars[c] + 1 if c in chars else 1
				return sorted((Letter(lowerCamelCase_    ,       lowerCamelCase_	) for c, f in chars.items())    ,       key=lambda lowerCamelCase_	: x.freq	)


def __lowercase  (   lowerCamelCase_	:   list[Letter]	):
				SCREAMING_SNAKE_CASE__  				=					letters  # type: ignore
				while len(lowerCamelCase_	) > 1:
								SCREAMING_SNAKE_CASE__  				=					response.pop(0	)
								SCREAMING_SNAKE_CASE__  				=					response.pop(0	)
								SCREAMING_SNAKE_CASE__  				=					left.freq + right.freq
								SCREAMING_SNAKE_CASE__  				=					TreeNode(lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_	)
								response.append(lowerCamelCase_	)
								response.sort(key=lambda lowerCamelCase_	: x.freq	)
				return response[0]


def __lowercase  (   lowerCamelCase_	:   Letter | TreeNode    ,       lowerCamelCase_	:   str	):
				if isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								SCREAMING_SNAKE_CASE__  				=					bitstring
								return [root]
				SCREAMING_SNAKE_CASE__  				=					root  # type: ignore
				SCREAMING_SNAKE_CASE__  				=					[]
				letters += traverse_tree(treenode.left    ,       bitstring + "0"	)
				letters += traverse_tree(treenode.right    ,       bitstring + "1"	)
				return letters


def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					parse_file(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					build_tree(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					{
				    k: v for letter in traverse_tree(lowerCamelCase_    ,       ""	) for k, v in letter.bitstring.items()
				}
				print(F'''Huffman Coding  of {file_path}: '''	)
				with open(lowerCamelCase_	) as f:
								while True:
												SCREAMING_SNAKE_CASE__  				=					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])
