
'''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 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      str:
		_UpperCamelCase       : Optional[Any]    =      copy.deepcopy(UpperCamelCase	)
		_UpperCamelCase       : List[str]    =      sorted(UpperCamelCase	)
		_UpperCamelCase       : List[str]    =      [bisect.bisect_right(UpperCamelCase			,UpperCamelCase	) for y in x]
		return quantized







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase=0	) ->      Optional[Any]:
		_UpperCamelCase       : Optional[Any]    =      np.arange(start=3			,stop=UpperCamelCase			,step=4	).tolist() if k > 0 else [10]
		_UpperCamelCase       : str    =      _quantize(UpperCamelCase			,UpperCamelCase	)
		# count number of elements per group
		_UpperCamelCase       : str    =      np.unique(UpperCamelCase			,return_counts=UpperCamelCase	)[1]
		_UpperCamelCase       : Dict    =      [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				(					a_ ):





		"""simple docstring"""





		def __init__(    self  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Union[str, Any]:
				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     )     )
				_UpperCamelCase       : int    =      sampler
				_UpperCamelCase       : Dict    =      group_ids
				_UpperCamelCase       : Optional[int]    =      batch_size
		def __iter__(    self     )				->    Optional[Any]:
				_UpperCamelCase       : Optional[int]    =      defaultdict(_snake_case     )
				_UpperCamelCase       : Optional[Any]    =      defaultdict(_snake_case     )

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






'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


_UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)

class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		def __init__(    self  ,					*_snake_case  ,					**_snake_case     )				->    None:
				warnings.warn(
				    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileNetV2ImageProcessor instead.'''  ,					_snake_case  ,					)
				super().__init__(*_snake_case  ,					**_snake_case     )





