






"""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     lowerCamelCase_					(	__A	:		Optional[int]      ,						__A	:		Dict      ):
					lowerCamelCase__					=copy.deepcopy(__A      )
					lowerCamelCase__					=sorted(__A      )
					lowerCamelCase__					=[bisect.bisect_right(__A      ,						__A      ) for y in x]
					return quantized




def     lowerCamelCase_					(	__A	:		List[Any]      ,						__A	:		Union[str, Any]=0      ):
					lowerCamelCase__					=np.arange(start=3      ,						stop=__A      ,						step=4      ).tolist() if k > 0 else [10]
					lowerCamelCase__					=_quantize(__A      ,						__A      )
					# count number of elements per group
					lowerCamelCase__					=np.unique(__A      ,						return_counts=__A      )[1]
					lowerCamelCase__					=[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 							__lowercase(							_UpperCamelCase	):






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	Any:




											"""simple docstring"""





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






						def __iter__(       self)      ->	Tuple:




											"""simple docstring"""





											lowerCamelCase__					=defaultdict(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=defaultdict(_SCREAMING_SNAKE_CASE)

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






						def __len__(       self)      ->	Dict:




											"""simple docstring"""





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







"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


_snake_case							:   Tuple	  =       logging.get_logger(__name__)




class 							__lowercase(							_UpperCamelCase	):






						def __init__(       self     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	None:




											"""simple docstring"""





											warnings.warn(
											    'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
											    ' use ViTImageProcessor instead.'     ,    _SCREAMING_SNAKE_CASE     ,    )
											super().__init__(*_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)