
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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


UpperCAmelCase     				=			logging.get_logger(__name__)







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Union[str, Any]										=	["""pixel_values"""]





	def __init__(	self		,   snake_case = True		,   snake_case = None		,   snake_case = True		,   snake_case = None		,   snake_case = PILImageResampling.BICUBIC		,   snake_case = True		,   snake_case = 1 / 255		,   snake_case = True		,   snake_case = None		,   snake_case = None		,   **snake_case		,   ):
		super().__init__(**snake_case	)
		lowercase					  =   crop_size if crop_size is not None else {'height': 256, 'width': 256}
		lowercase					  =   get_size_dict(snake_case		,   param_name='crop_size'	)
		lowercase					  =   size if size is not None else {'height': 224, 'width': 224}
		lowercase					  =   get_size_dict(snake_case	)

		lowercase					  =   do_center_crop
		lowercase					  =   crop_size
		lowercase					  =   do_resize
		lowercase					  =   size
		lowercase					  =   resample
		lowercase					  =   do_rescale
		lowercase					  =   rescale_factor
		lowercase					  =   do_normalize
		lowercase					  =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
		lowercase					  =   image_std if image_std is not None else IMAGENET_DEFAULT_STD





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case = None		,   snake_case = None		,   **snake_case		,   ):
		lowercase					  =   self.size if size is None else size
		lowercase					  =   get_size_dict(snake_case	)
		lowercase					  =   get_size_dict(snake_case		,   param_name='crop_size'	)

		lowercase  , lowercase					  =   get_image_size(snake_case	)
		lowercase					  =   min(snake_case		,   snake_case	)
		lowercase					  =   (size['height'] / crop_size['height']) * min_dim
		lowercase					  =   (size['width'] / crop_size['width']) * min_dim
		return center_crop(snake_case		,   size=(cropped_height, cropped_width)		,   data_format=snake_case		,   **snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case = PIL.Image.BICUBIC		,   snake_case = None		,   **snake_case		,   ):
		lowercase					  =   get_size_dict(snake_case	)
		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(
		    snake_case		,   size=(size['height'], size['width'])		,   resample=snake_case		,   data_format=snake_case		,   **snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case = None		,   **snake_case		,   ):
		return rescale(snake_case		,   scale=snake_case		,   data_format=snake_case		,   **snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case = None		,   **snake_case		,   ):
		return normalize(snake_case		,   mean=snake_case		,   std=snake_case		,   data_format=snake_case		,   **snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = ChannelDimension.FIRST		,   **snake_case		,   ):
		lowercase					  =   do_center_crop if do_center_crop is not None else self.do_center_crop
		lowercase					  =   crop_size if crop_size is not None else self.crop_size
		lowercase					  =   get_size_dict(snake_case		,   param_name='crop_size'	)
		lowercase					  =   do_resize if do_resize is not None else self.do_resize
		lowercase					  =   size if size is not None else self.size
		lowercase					  =   get_size_dict(snake_case	)
		lowercase					  =   resample if resample is not None else self.resample
		lowercase					  =   do_rescale if do_rescale is not None else self.do_rescale
		lowercase					  =   rescale_factor if rescale_factor is not None else self.rescale_factor
		lowercase					  =   do_normalize if do_normalize is not None else self.do_normalize
		lowercase					  =   image_mean if image_mean is not None else self.image_mean
		lowercase					  =   image_std if image_std is not None else self.image_std

		lowercase					  =   make_list_of_images(snake_case	)

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

		if do_center_crop and crop_size is None:
			raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'	)

		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.'	)

		if do_normalize and (image_mean is None or image_std is None):
			raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'	)

		# All transformations expect numpy arrays.
		lowercase					  =   [to_numpy_array(snake_case	) for image in images]

		if do_center_crop:
			lowercase					  =   [self.center_crop(snake_case		,   snake_case		,   size=snake_case	) for image in images]

		if do_resize:
			lowercase					  =   [self.resize(image=snake_case		,   size=snake_case		,   resample=snake_case	) for image in images]

		if do_rescale:
			lowercase					  =   [self.rescale(image=snake_case		,   scale=snake_case	) for image in images]

		if do_normalize:
			lowercase					  =   [self.normalize(image=snake_case		,   mean=snake_case		,   std=snake_case	) for image in images]

		lowercase					  =   [to_channel_dimension_format(snake_case		,   snake_case	) for image in images]

		lowercase					  =   {'pixel_values': images}
		return BatchFeature(data=snake_case		,   tensor_type=snake_case	)



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_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   copy.deepcopy(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   sorted(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   [bisect.bisect_right(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) for y in x]
	return quantized






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=0							):
	lowercase					  =   np.arange(start=3			,							stop=__SCREAMING_SNAKE_CASE			,							step=4							).tolist() if k > 0 else [10]
	lowercase					  =   _quantize(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)
	# count number of elements per group
	lowercase					  =   np.unique(__SCREAMING_SNAKE_CASE			,							return_counts=__SCREAMING_SNAKE_CASE							)[1]
	lowercase					  =   [0] + bins + [np.inf]
	logger.info('Using {} as bins for aspect lengths quantization'.format(__SCREAMING_SNAKE_CASE							)							)
	logger.info('Count of instances per bin: {}'.format(__SCREAMING_SNAKE_CASE							)							)
	return groups







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case		,   snake_case	):
		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	)	)
		lowercase					  =   sampler
		lowercase					  =   group_ids
		lowercase					  =   batch_size





	def __iter__(	self	):
		lowercase					  =   defaultdict(snake_case	)
		lowercase					  =   defaultdict(snake_case	)

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

