


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[int]    ,							UpperCamelCase__					:  Union[str, Any]						):
       _A				:	Optional[Any]       =			copy.deepcopy(UpperCamelCase__						)
       _A				:	Any       =			sorted(UpperCamelCase__						)
       _A				:	Union[str, Any]       =			[bisect.bisect_right(UpperCamelCase__    ,							UpperCamelCase__						) for y in x]
       return quantized
def 							_UpperCAmelCase    (UpperCamelCase__					:  Dict    ,							UpperCamelCase__					:  Dict=0						):
       _A				:	Tuple       =			np.arange(start=3    ,							stop=UpperCamelCase__    ,							step=4						).tolist() if k > 0 else [10]
       _A				:	List[Any]       =			_quantize(UpperCamelCase__    ,							UpperCamelCase__						)
       # count number of elements per group
       _A				:	Any       =			np.unique(UpperCamelCase__    ,							return_counts=UpperCamelCase__						)[1]
       _A				:	Optional[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):




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Union[str, Any]:
            if not isinstance(__lowerCamelCase			,						__lowerCamelCase):
                   raise ValueError(
                       "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(__lowerCamelCase))
            _A				:	Union[str, Any]       =			sampler
            _A				:	Any       =			group_ids
            _A				:	Optional[int]       =			batch_size


     def __iter__(						self)      ->    Optional[int]:
            _A				:	List[str]       =			defaultdict(__lowerCamelCase)
            _A				:	str       =			defaultdict(__lowerCamelCase)

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




from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowerCAmelCase__						=			datasets.utils.logging.get_logger(__name__)







class 					lowerCAmelCase__	(    folder_based_builder.FolderBasedBuilderConfig):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			None
     __SCREAMING_SNAKE_CASE        =			None







class 					lowerCAmelCase__	(    folder_based_builder.FolderBasedBuilder):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			datasets.Image()
     __SCREAMING_SNAKE_CASE        =			"image"
     __SCREAMING_SNAKE_CASE        =			ImageFolderConfig
     __SCREAMING_SNAKE_CASE        =			42  # definition at the bottom of the script
     __SCREAMING_SNAKE_CASE        =			ImageClassification(image_column="image"					,   label_column="label")




lowerCAmelCase__						=			[
    '.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',
]
lowerCAmelCase__						=			IMAGE_EXTENSIONS
