'''simple docstring'''




from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__snake_case   :      List[Any]						    =  datasets.utils.logging.get_logger(__name__)




class 	__UpperCAmelCase				(				folder_based_builder.FolderBasedBuilderConfig     ):





       '''simple docstring'''



       __lowercase					:					bool    =				None
       __lowercase					:					bool    =				None




class 	__UpperCAmelCase				(				folder_based_builder.FolderBasedBuilder     ):





       '''simple docstring'''



       __lowercase					:					str    =				datasets.Image()
       __lowercase					:					List[Any]    =				'image'
       __lowercase					:					List[str]    =				ImageFolderConfig
       __lowercase					:					List[str]  # definition at the bottom of the script
       __lowercase					:					Union[str, Any]    =				ImageClassification(image_column='image'						,      label_column='label'     )
__snake_case   :      Optional[int]						    =  [
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
__snake_case   :      int						    =  IMAGE_EXTENSIONS







'''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      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[Any],   _UpperCamelCase    :	int   )	->      Union[str, Any]:
    A_     =   copy.deepcopy(_UpperCamelCase   )
    A_     =   sorted(_UpperCamelCase   )
    A_     =   [bisect.bisect_right(_UpperCamelCase,   _UpperCamelCase   ) for y in x]
    return quantized





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





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           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 ) )
           A_     =   sampler
           A_     =   group_ids
           A_     =   batch_size

       def __iter__(      self )		->     Tuple:
           A_     =   defaultdict(_SCREAMING_SNAKE_CASE )
           A_     =   defaultdict(_SCREAMING_SNAKE_CASE )

           A_     =   0
           for idx in self.sampler:
               A_     =   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
           A_     =   len(self )
           A_     =   expected_num_batches - num_batches
           if num_remaining > 0:
               # for the remaining batches, group the batches by similar lengths
               A_     =   []
               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]
                       A_     =   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 )		->     Optional[int]:
           return (len(self.sampler ) + self.batch_size - 1) // self.batch_size






