

"""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 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	lowerCAmelCase__			:		int						       =			copy.deepcopy(A_				)
	lowerCAmelCase__			:		int						       =			sorted(A_				)
	lowerCAmelCase__			:		Tuple						       =			[bisect.bisect_right(A_		,  A_				) for y in x]
	return quantized





def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_=0				):
	lowerCAmelCase__			:		Optional[int]						       =			np.arange(start=3		,  stop=A_		,  step=4				).tolist() if k > 0 else [10]
	lowerCAmelCase__			:		Optional[Any]						       =			_quantize(A_		,  A_				)
	# count number of elements per group
	lowerCAmelCase__			:		str						       =			np.unique(A_		,  return_counts=A_				)[1]
	lowerCAmelCase__			:		Optional[Any]						       =			[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        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					def __init__( self    :       Dict     ,lowercase_    :       Optional[int]     ,lowercase_    :       List[str]     ,lowercase_    :       int	):
						if not isinstance(lowercase_     ,lowercase_	):
							raise ValueError(
							    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(lowercase_	)	)
						lowerCAmelCase__			:		Dict						       =			sampler
						lowerCAmelCase__			:		Optional[Any]						       =			group_ids
						lowerCAmelCase__			:		Dict						       =			batch_size







					def __iter__( self    :       List[str]	):
						lowerCAmelCase__			:		Dict						       =			defaultdict(lowercase_	)
						lowerCAmelCase__			:		Union[str, Any]						       =			defaultdict(lowercase_	)

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







					def __len__( self    :       List[Any]	):
						return (len(self.sampler	) + self.batch_size - 1) // self.batch_size







"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase						:   List[Any]							   =	logging.get_logger(__name__)







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					lowercase__       				= ["pixel_values"]







					def __init__( self    :       Tuple     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Dict[str, int]] = None     ,lowercase_    :       PILImageResampling = PILImageResampling.BILINEAR     ,lowercase_    :       bool = True     ,lowercase_    :       Union[int, float] = 1 / 2_5_5     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,**lowercase_    :       Tuple     ,):
						super().__init__(**lowercase_	)
						lowerCAmelCase__			:		Any						       =			size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__			:		Tuple						       =			get_size_dict(lowercase_	)
						lowerCAmelCase__			:		Tuple						       =			do_resize
						lowerCAmelCase__			:		Tuple						       =			do_rescale
						lowerCAmelCase__			:		Union[str, Any]						       =			do_normalize
						lowerCAmelCase__			:		Optional[int]						       =			size
						lowerCAmelCase__			:		List[Any]						       =			resample
						lowerCAmelCase__			:		int						       =			rescale_factor
						lowerCAmelCase__			:		int						       =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase__			:		Any						       =			image_std if image_std is not None else IMAGENET_STANDARD_STD







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       np.ndarray     ,lowercase_    :       Dict[str, int]     ,lowercase_    :       PILImageResampling = PILImageResampling.BILINEAR     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       str     ,):
						lowerCAmelCase__			:		Union[str, Any]						       =			get_size_dict(lowercase_	)
						if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'	)
						return resize(
						    lowercase_     ,size=(size['''height'''], size['''width'''])     ,resample=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       np.ndarray     ,lowercase_    :       float     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       Tuple	):
						return rescale(lowercase_     ,scale=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       np.ndarray     ,lowercase_    :       Union[float, List[float]]     ,lowercase_    :       Union[float, List[float]]     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       Union[str, Any]     ,):
						return normalize(lowercase_     ,mean=lowercase_     ,std=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       ImageInput     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Dict[str, int] = None     ,lowercase_    :       PILImageResampling = None     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Optional[float] = None     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[str, TensorType]] = None     ,lowercase_    :       Union[str, ChannelDimension] = ChannelDimension.FIRST     ,**lowercase_    :       List[Any]     ,):
						lowerCAmelCase__			:		Optional[int]						       =			do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__			:		Any						       =			do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__			:		Dict						       =			do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__			:		Dict						       =			resample if resample is not None else self.resample
						lowerCAmelCase__			:		Dict						       =			rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__			:		Optional[Any]						       =			image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__			:		str						       =			image_std if image_std is not None else self.image_std

						lowerCAmelCase__			:		Any						       =			size if size is not None else self.size
						lowerCAmelCase__			:		Dict						       =			get_size_dict(lowercase_	)

						lowerCAmelCase__			:		Union[str, Any]						       =			make_list_of_images(lowercase_	)

						if not valid_images(lowercase_	):
							raise ValueError(
							    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
							    '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

						if do_resize and size is None:
							raise ValueError('''Size must be specified if do_resize is True.'''	)

						if do_rescale and rescale_factor is None:
							raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

						# All transformations expect numpy arrays.
						lowerCAmelCase__			:		Dict						       =			[to_numpy_array(lowercase_	) for image in images]

						if do_resize:
							lowerCAmelCase__			:		Optional[Any]						       =			[self.resize(image=lowercase_     ,size=lowercase_     ,resample=lowercase_	) for image in images]

						if do_rescale:
							lowerCAmelCase__			:		List[Any]						       =			[self.rescale(image=lowercase_     ,scale=lowercase_	) for image in images]

						if do_normalize:
							lowerCAmelCase__			:		Dict						       =			[self.normalize(image=lowercase_     ,mean=lowercase_     ,std=lowercase_	) for image in images]

						lowerCAmelCase__			:		Tuple						       =			[to_channel_dimension_format(lowercase_     ,lowercase_	) for image in images]

						lowerCAmelCase__			:		int						       =			{'''pixel_values''': images}
						return BatchFeature(data=lowercase_     ,tensor_type=lowercase_	)




