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       :			Optional[Any]					,	__UpperCamelCase       :			Tuple      )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= copy.deepcopy(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__     				= sorted(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__     				= [bisect.bisect_right(__UpperCamelCase					,	__UpperCamelCase      ) for y in x]
  return quantized



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[Any]					,	__UpperCamelCase       :			str=0      )				->   Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= np.arange(start=3					,	stop=__UpperCamelCase					,	step=4      ).tolist() if k > 0 else [10]
  SCREAMING_SNAKE_CASE__     				= _quantize(__UpperCamelCase					,	__UpperCamelCase      )
  # count number of elements per group
  SCREAMING_SNAKE_CASE__     				= np.unique(__UpperCamelCase					,	return_counts=__UpperCamelCase      )[1]
  SCREAMING_SNAKE_CASE__     				= [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 __snake_case							(		lowerCamelCase_						):




   def __init__(       self     :							List[str]			,      _lowercase     :							List[Any]			,      _lowercase     :							List[str]			,      _lowercase     :							List[Any] ):



     """simple docstring"""






     if not isinstance(_lowercase			,      _lowercase ):
       raise ValueError(
           """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(_lowercase ) )
     SCREAMING_SNAKE_CASE__     				= sampler
     SCREAMING_SNAKE_CASE__     				= group_ids
     SCREAMING_SNAKE_CASE__     				= batch_size




   def __iter__(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= defaultdict(_lowercase )
     SCREAMING_SNAKE_CASE__     				= defaultdict(_lowercase )

     SCREAMING_SNAKE_CASE__     				= 0
     for idx in self.sampler:
       SCREAMING_SNAKE_CASE__     				= self.group_ids[idx]
       buffer_per_group[group_id].append(_lowercase )
       samples_per_group[group_id].append(_lowercase )
       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
     SCREAMING_SNAKE_CASE__     				= len(self )
     SCREAMING_SNAKE_CASE__     				= expected_num_batches - num_batches
     if num_remaining > 0:
       # for the remaining batches, group the batches by similar lengths
       SCREAMING_SNAKE_CASE__     				= []
       for group_id, idxs in sorted(buffer_per_group.items()			,      key=lambda _lowercase : x[0] ):
         batch_idx.extend(_lowercase )
         if len(_lowercase ) >= self.batch_size:
           yield batch_idx[: self.batch_size]
           SCREAMING_SNAKE_CASE__     				= batch_idx[self.batch_size :]
           num_remaining -= 1
       if len(_lowercase ) > 0:
         yield batch_idx
         num_remaining -= 1
     assert num_remaining == 0




   def __len__(       self     :							str ):



     """simple docstring"""






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

from hashlib import mda, shaaaa

__lowerCamelCase     :					Dict				  =						(shaaaa, mda)







class __snake_case							:




   def __init__(       self     :							str			,      _lowercase     :							int = 8 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 0B0
     SCREAMING_SNAKE_CASE__     				= size




   def       __a		(       self     :							List[str]			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.hash_(_lowercase )
     self.bitarray |= h




   def       __a		(       self     :							int			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.hash_(_lowercase )
     return (h & self.bitarray) == h




   def __contains__(       self     :							Union[str, Any]			,      _lowercase     :							str ):



     """simple docstring"""






     return self.exists(_lowercase )




   def       __a		(       self     :							Dict			,      _lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= bin(_lowercase )[2:]
     return res.zfill(self.size )




   @property
   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     return self.format_bin(self.bitarray )




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 0B0
     for func in HASH_FUNCTIONS:
       SCREAMING_SNAKE_CASE__     				= (
           int.from_bytes(func(value.encode() ).digest()			,      """little""" ) % self.size
       )
       res |= 2**position
     return res




   def       __a		(       self     :							str			,      _lowercase     :							str ):



     """simple docstring"""






     return self.format_bin(self.hash_(_lowercase ) )




   @property
   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= bin(self.bitarray ).count("""1""" )
     return (n_ones / self.size) ** len(_lowercase )
