




"""simple docstring"""

import unittest

from transformers.image_processing_utils import get_size_dict

class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						{'''wrong_key''': 2_24}
					with self.assertRaises(_lowerCamelCase    ):
						get_size_dict(_lowerCamelCase    )

					A_					:			Any						=						{'''height''': 2_24}
					with self.assertRaises(_lowerCamelCase    ):
						get_size_dict(_lowerCamelCase    )

					A_					:			Tuple						=						{'''width''': 2_24, '''shortest_edge''': 2_24}
					with self.assertRaises(_lowerCamelCase    ):
						get_size_dict(_lowerCamelCase    )

					# Test a dict with the correct keys is returned as is
					A_					:			int						=						{'''height''': 2_24, '''width''': 2_24}
					A_					:			Union[str, Any]						=						get_size_dict(_lowerCamelCase    )
					self.assertEqual(_lowerCamelCase ,       _lowerCamelCase    )

					A_					:			List[Any]						=						{'''shortest_edge''': 2_24}
					A_					:			int						=						get_size_dict(_lowerCamelCase    )
					self.assertEqual(_lowerCamelCase ,       {'''shortest_edge''': 2_24}    )

					A_					:			int						=						{'''longest_edge''': 2_24, '''shortest_edge''': 2_24}
					A_					:			Optional[Any]						=						get_size_dict(_lowerCamelCase    )
					self.assertEqual(_lowerCamelCase ,       {'''longest_edge''': 2_24, '''shortest_edge''': 2_24}    )

					# Test a single int value which  represents (size, size)
					A_					:			Dict						=						get_size_dict(2_24    )
					self.assertEqual(_lowerCamelCase ,       {'''height''': 2_24, '''width''': 2_24}    )

					# Test a single int value which represents the shortest edge
					A_					:			Any						=						get_size_dict(2_24 ,       default_to_square=_lowerCamelCase    )
					self.assertEqual(_lowerCamelCase ,       {'''shortest_edge''': 2_24}    )

					# Test a tuple of ints which represents (height, width)
					A_					:			str						=						get_size_dict((1_50, 2_00)    )
					self.assertEqual(_lowerCamelCase ,       {'''height''': 1_50, '''width''': 2_00}    )

					# Test a tuple of ints which represents (width, height)
					A_					:			Optional[int]						=						get_size_dict((1_50, 2_00) ,       height_width_order=_lowerCamelCase    )
					self.assertEqual(_lowerCamelCase ,       {'''height''': 2_00, '''width''': 1_50}    )

					# Test an int representing the shortest edge and max_size which represents the longest edge
					A_					:			int						=						get_size_dict(2_24 ,       max_size=2_56 ,       default_to_square=_lowerCamelCase    )
					self.assertEqual(_lowerCamelCase ,       {'''shortest_edge''': 2_24, '''longest_edge''': 2_56}    )

					# Test int with default_to_square=True and max_size fails
					with self.assertRaises(_lowerCamelCase    ):
						get_size_dict(2_24 ,       max_size=2_56 ,       default_to_square=_lowerCamelCase    )











"""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 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






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

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase=0		):




	"""simple docstring"""






	A_					:			List[Any]						=						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_					:			Tuple						=						np.unique(_UpperCAmelCase					, return_counts=_UpperCAmelCase		)[1]
	A_					:			Optional[int]						=						[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     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   Union[str, Any] ,       _lowerCamelCase				:   List[Any] ,       _lowerCamelCase				:   Dict ,       _lowerCamelCase				:   Dict    ):



					"""simple docstring"""

					if not isinstance(_lowerCamelCase ,       _lowerCamelCase    ):
						raise ValueError(
						    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(_lowerCamelCase    )    )
					A_					:			List[str]						=						sampler
					A_					:			str						=						group_ids
					A_					:			Any						=						batch_size







				def __iter__(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			List[str]						=						defaultdict(_lowerCamelCase    )
					A_					:			Dict						=						defaultdict(_lowerCamelCase    )

					A_					:			List[str]						=						0
					for idx in self.sampler:
						A_					:			Union[str, Any]						=						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_					:			int						=						len(self    )
					A_					:			Dict						=						expected_num_batches - num_batches
					if num_remaining > 0:
						# for the remaining batches, group the batches by similar lengths
						A_					:			Optional[Any]						=						[]
						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_					:			str						=						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				:   List[Any]    ):



					"""simple docstring"""

					return (len(self.sampler    ) + self.batch_size - 1) // self.batch_size





