





import bisect
import copy
from collections import defaultdict

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

from utils import logger






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Optional[int]:






				"""simple docstring"""


				snake_case_		:					Tuple			    =						copy.deepcopy(_UpperCamelCase				)
				snake_case_		:					Tuple			    =						sorted(_UpperCamelCase				)
				snake_case_		:					List[str]			    =						[bisect.bisect_right(_UpperCamelCase				,       _UpperCamelCase				) for y in x]
				return quantized






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase=0				)	->		str:






				"""simple docstring"""


				snake_case_		:					Optional[Any]			    =						np.arange(start=3				,       stop=_UpperCamelCase				,       step=4				).tolist() if k > 0 else [10]
				snake_case_		:					str			    =						_quantize(_UpperCamelCase				,       _UpperCamelCase				)
				# count number of elements per group
				snake_case_		:					Union[str, Any]			    =						np.unique(_UpperCamelCase				,       return_counts=_UpperCamelCase				)[1]
				snake_case_		:					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       __lowerCAmelCase (    _a  ):






			def __init__(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     List[str]:


							'''simple docstring'''



							if not isinstance(__magic_name__      ,							__magic_name__					):
											raise ValueError(
											    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(__magic_name__					)					)
							snake_case_		:					List[Any]			    =						sampler
							snake_case_		:					str			    =						group_ids
							snake_case_		:					int			    =						batch_size






			def __iter__(self					)  ->     str:


							'''simple docstring'''



							snake_case_		:					int			    =						defaultdict(__magic_name__					)
							snake_case_		:					str			    =						defaultdict(__magic_name__					)

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






			def __len__(self					)  ->     Tuple:


							'''simple docstring'''



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







class       __lowerCAmelCase :






			def __init__(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						[]
							snake_case_		:					Any			    =						0
							snake_case_		:					Optional[int]			    =						0






			def __str__(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					int			    =						'''<''' + str(self.entries					)[1:-1] + '''>'''
							return printed






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Dict:


							'''simple docstring'''



							self.entries.append(__magic_name__					)
							snake_case_		:					List[str]			    =						self.length + 1






			def     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.length - 1
							snake_case_		:					Dict			    =						self.entries[self.front]
							# self.front-=1
							# self.entries = self.entries[self.front:]
							snake_case_		:					List[str]			    =						self.entries[1:]
							return dequeued






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Optional[Any]:


							'''simple docstring'''



							for _ in range(__magic_name__					):
											self.put(self.get()					)






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							return self.entries[0]






			def     lowerCamelCase						(self					)  ->     Union[str, Any]:


							'''simple docstring'''



							return self.length
