



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    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  int = 512  ):



 """simple docstring"""





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






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  int=True  ):



 """simple docstring"""





 requires_backends(__lowerCamelCase		,						["cython"]  )

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

 __SCREAMING_SNAKE_CASE							:     Any							=						np.asarray(item["edge_index"]		,						dtype=np.intaa  )

 __SCREAMING_SNAKE_CASE							:     Any							=						convert_to_single_emb(__lowerCamelCase  ) + 1
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						item["num_nodes"]

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

 # node adj matrix [num_nodes, num_nodes] bool
 __SCREAMING_SNAKE_CASE							:     int							=						np.zeros([num_nodes, num_nodes]		,						dtype=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						True

 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     str							=						algos_graphormer.floyd_warshall(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Tuple							=						np.amax(__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     str							=						algos_graphormer.gen_edge_input(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						np.zeros([num_nodes + 1, num_nodes + 1]		,						dtype=np.single  )  # with graph token

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

 return item






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				str  ,	UpperCamelCase			:				List[str]=2_0  ,	UpperCamelCase			:				Dict=False					)->Any:
      if not is_cython_available():
       raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

      __SCREAMING_SNAKE_CASE							:     List[str]							=						spatial_pos_max
      __SCREAMING_SNAKE_CASE							:     Dict							=						on_the_fly_processing






     def __call__(							self			:				Optional[int]  ,	UpperCamelCase			:				List[dict]					)->Dict[str, Any]:
      if self.on_the_fly_processing:
       __SCREAMING_SNAKE_CASE							:     Tuple							=						[preprocess_item(UpperCamelCase					) for i in features]

      if not isinstance(features[0]  ,	UpperCamelCase					):
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						[vars(UpperCamelCase					) for f in features]
      __SCREAMING_SNAKE_CASE							:     str							=						{}

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

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.zeros(UpperCamelCase  ,	max_node_num + 1  ,	max_node_num + 1  ,	dtype=torch.float					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     Any							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     int							=						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							:     List[str]							=						torch.tensor(f[k]					)

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

       __SCREAMING_SNAKE_CASE							:     str							=						f["attn_bias"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						f[
           "attn_edge_type"
       ]
       __SCREAMING_SNAKE_CASE							:     Dict							=						f["spatial_pos"]
       __SCREAMING_SNAKE_CASE							:     int							=						f["in_degree"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						f["input_nodes"]
       __SCREAMING_SNAKE_CASE							:     List[str]							=						f["input_edges"]

      __SCREAMING_SNAKE_CASE							:     Any							=						batch["in_degree"]

      __SCREAMING_SNAKE_CASE							:     int							=						features[0]["labels"]
      if len(UpperCamelCase					) == 1:  # one task
       if isinstance(sample[0]  ,	UpperCamelCase					):  # regression
        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
       else:  # binary classification
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						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							:     int							=						torch.from_numpy(np.stack([i["labels"] for i in features]  ,	axis=0					)					)

      return batch











"""simple docstring"""







import math
from collections.abc import Generator


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: Generator     =   (i for i in range(1   ,				(max_n + 1)      ))
		for i in (n for n in numbers if n > 1):
				for j in range(2   ,				__UpperCAmelCase      ):
						if (i % j) == 0:
								break
				else:
						yield i


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: Generator     =   (i for i in range(1   ,				(max_n + 1)      ))
		for i in (n for n in numbers if n > 1):
				# only need to check for factors up to sqrt(i)
				_lowercase				: Union[str, Any]     =   int(math.sqrt(__UpperCAmelCase      )      ) + 1
				for j in range(2   ,				__UpperCAmelCase      ):
						if (i % j) == 0:
								break
				else:
						yield i


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: Generator     =   (i for i in range(1   ,				(max_n + 1)   ,				2      ))
		# It's useless to test even numbers as they will not be prime
		if max_n > 2:
				yield 2  # Because 2 will not be tested, it's necessary to yield it now
		for i in (n for n in numbers if n > 1):
				_lowercase				: str     =   int(math.sqrt(__UpperCAmelCase      )      ) + 1
				for j in range(3   ,				__UpperCAmelCase   ,				2      ):
						# As we removed the even numbers, we don't need them now
						if (i % j) == 0:
								break
				else:
						yield i


def  __SCREAMING_SNAKE_CASE (  ):
		from timeit import timeit

		_lowercase				: Tuple     =   """from __main__ import slow_primes, primes, fast_primes"""
		print(timeit("""slow_primes(1_000_000_000_000)"""   ,				setup=__UpperCAmelCase   ,				number=1000000      )      )
		print(timeit("""primes(1_000_000_000_000)"""   ,				setup=__UpperCAmelCase   ,				number=1000000      )      )
		print(timeit("""fast_primes(1_000_000_000_000)"""   ,				setup=__UpperCAmelCase   ,				number=1000000      )      )


if __name__ == "__main__":
		UpperCAmelCase:  int			     =				int(input("""Calculate primes up to:\n>> """).strip())
		for ret in primes(number):
				print(ret)
		benchmark()



