



"""simple docstring"""







import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: int     =   copy.deepcopy(__UpperCAmelCase      )
		_lowercase				: List[Any]     =   sorted(__UpperCAmelCase      )
		_lowercase				: str     =   [bisect.bisect_right(__UpperCAmelCase   ,				__UpperCAmelCase      ) for y in x]
		return quantized


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase=0      ):
		_lowercase				: List[Any]     =   np.arange(start=3   ,				stop=__UpperCAmelCase   ,				step=4      ).tolist() if k > 0 else [10]
		_lowercase				: Optional[Any]     =   _quantize(__UpperCAmelCase   ,				__UpperCAmelCase      )
		# count number of elements per group
		_lowercase				: Optional[Any]     =   np.unique(__UpperCAmelCase   ,				return_counts=__UpperCAmelCase      )[1]
		_lowercase				: Optional[Any]     =   [0] + bins + [np.inf]
		logger.info("""Using {} as bins for aspect lengths quantization""".format(__UpperCAmelCase      )      )
		logger.info("""Count of instances per bin: {}""".format(__UpperCAmelCase      )      )
		return groups





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			if not isinstance(UpperCAmelCase_				,UpperCAmelCase_   ):
					raise ValueError(
					    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(UpperCAmelCase_   )   )
			_lowercase				: List[Any]     =   sampler
			_lowercase				: str     =   group_ids
			_lowercase				: Optional[Any]     =   batch_size






	def __iter__(     self   ):
			_lowercase				: Dict     =   defaultdict(UpperCAmelCase_   )
			_lowercase				: int     =   defaultdict(UpperCAmelCase_   )

			_lowercase				: int     =   0
			for idx in self.sampler:
					_lowercase				: List[str]     =   self.group_ids[idx]
					buffer_per_group[group_id].append(UpperCAmelCase_   )
					samples_per_group[group_id].append(UpperCAmelCase_   )
					if len(buffer_per_group[group_id]   ) == self.batch_size:
							yield buffer_per_group[group_id]  # TODO
							num_batches += 1
							del buffer_per_group[group_id]
					assert len(buffer_per_group[group_id]   ) < self.batch_size

			# now we have run out of elements that satisfy
			# the group criteria, let's return the remaining
			# elements so that the size of the sampler is
			# deterministic
			_lowercase				: Optional[int]     =   len(self   )
			_lowercase				: Any     =   expected_num_batches - num_batches
			if num_remaining > 0:
					# for the remaining batches, group the batches by similar lengths
					_lowercase				: List[Any]     =   []
					for group_id, idxs in sorted(buffer_per_group.items()				,key=lambda UpperCAmelCase_   : x[0]   ):
							batch_idx.extend(UpperCAmelCase_   )
							if len(UpperCAmelCase_   ) >= self.batch_size:
									yield batch_idx[: self.batch_size]
									_lowercase				: str     =   batch_idx[self.batch_size :]
									num_remaining -= 1
					if len(UpperCAmelCase_   ) > 0:
							yield batch_idx
							num_remaining -= 1
			assert num_remaining == 0






	def __len__(     self   ):
			return (len(self.sampler   ) + self.batch_size - 1) // self.batch_size









'''simple docstring'''







A					       =[[0, 1, 1], [0, 0, 1], [1, 0, 0]]


class      _a    :
       def __init__(      self		:      Optional[int]  ,							lowercase		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						name
           UpperCAmelCase     			=						[]
           UpperCAmelCase     			=						[]
       def        A (      self		:      Union[str, Any]  ,							lowercase		:      int							):



           '''simple docstring'''

           self.inbound.append(lowercase							)
       def        A (      self		:      List[str]  ,							lowercase		:      Optional[Any]							):



           '''simple docstring'''

           self.outbound.append(lowercase							)







       def __repr__(      self		:      Any							):



           '''simple docstring'''

           return f"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"






def 		snake_case_				(_a      :				str       ,  _a      :				str=3       ,  _a      :				Any=0.85	):
    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						1

    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						len(node.outbound	)

    for i in range(_a	):
        print(F"======= Iteration {i + 1} ======="	)
        for _, node in enumerate(_a	):
            UpperCAmelCase     			=						(1 - d) + d * sum(
                ranks[ib] / outbounds[ib] for ib in node.inbound	)
        print(_a	)





def 		snake_case_				():
    UpperCAmelCase     			=						list(input('''Enter Names of the Nodes: '''	).split()	)

    UpperCAmelCase     			=						[Node(_a	) for name in names]

    for ri, row in enumerate(_a	):
        for ci, col in enumerate(_a	):
            if col == 1:
                nodes[ci].add_inbound(names[ri]	)
                nodes[ri].add_outbound(names[ci]	)

    print('''======= Nodes ======='''	)
    for node in nodes:
        print(_a	)

    page_rank(_a	)


if __name__ == "__main__":
   main()




