

"""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						(   A_		,  A_				):
	lowerCAmelCase__			:		Optional[Any]						       =			copy.deepcopy(A_				)
	lowerCAmelCase__			:		List[str]						       =			sorted(A_				)
	lowerCAmelCase__			:		List[str]						       =			[bisect.bisect_right(A_		,  A_				) for y in x]
	return quantized

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







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					def __init__( self    :       str     ,lowercase_    :       Union[str, Any]     ,lowercase_    :       Tuple     ,lowercase_    :       Tuple	):
						if not isinstance(_snake_case     ,_snake_case	):
							raise ValueError(
							    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(_snake_case	)	)
						lowerCAmelCase__			:		int						       =			sampler
						lowerCAmelCase__			:		Dict						       =			group_ids
						lowerCAmelCase__			:		Optional[int]						       =			batch_size







					def __iter__( self    :       int	):
						lowerCAmelCase__			:		Optional[int]						       =			defaultdict(_snake_case	)
						lowerCAmelCase__			:		Optional[Any]						       =			defaultdict(_snake_case	)

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







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







"""simple docstring"""





def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		List[Any]						       =			53_81
	for x in s:
		lowerCAmelCase__			:		Tuple						       =			((hash_value << 5) + hash_value) + ord(A_				)
	return hash_value & 0Xf_f_f_f_f_f_f_f




