

"""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					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int )				->			str:
	_UpperCAmelCase    :							Optional[Any]									=					copy.deepcopy(_lowerCAmelCase )
	_UpperCAmelCase    :							List[str]									=					sorted(_lowerCAmelCase )
	_UpperCAmelCase    :							List[str]									=					[bisect.bisect_right(_lowerCAmelCase,  _lowerCAmelCase ) for y in x]
	return quantized
def       UpperCamelCase					(			_lowerCAmelCase     :						Any,  _lowerCAmelCase     :						Union[str, Any]=0 )				->			Optional[Any]:
	_UpperCAmelCase    :							Optional[Any]									=					np.arange(start=3,  stop=_lowerCAmelCase,  step=4 ).tolist() if k > 0 else [10]
	_UpperCAmelCase    :							str									=					_quantize(_lowerCAmelCase,  _lowerCAmelCase )
	# count number of elements per group
	_UpperCAmelCase    :							str									=					np.unique(_lowerCAmelCase,  return_counts=_lowerCAmelCase )[1]
	_UpperCAmelCase    :							Dict									=					[0] + bins + [np.inf]
	logger.info("""Using {} as bins for aspect lengths quantization""".format(_lowerCAmelCase ) )
	logger.info("""Count of instances per bin: {}""".format(_lowerCAmelCase ) )
	return groups






class     _UpperCAmelCase			(		a_):
			def __init__(		self      ,		_A      ,		_A      ,		_A     )    ->    Union[str, Any]:






				'''simple docstring'''







				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     )     )
				_UpperCAmelCase    :							int									=					sampler
				_UpperCAmelCase    :							Dict									=					group_ids
				_UpperCAmelCase    :							Optional[int]									=					batch_size
			def __iter__(		self     )    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[int]									=					defaultdict(_snake_case     )
				_UpperCAmelCase    :							Optional[Any]									=					defaultdict(_snake_case     )

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






				'''simple docstring'''







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








"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor






class     _UpperCAmelCase			(		unittest.TestCase):
			def __init__(		self      ,		_A      ,		_A=7      ,		_A=3      ,		_A=18      ,		_A=30      ,		_A=4_00      ,		_A=True      ,		_A=None      ,		_A=True      ,		_A=[0.5, 0.5, 0.5]      ,		_A=[0.5, 0.5, 0.5]      ,		)    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[Any]									=					size if size is not None else {"""height""": 18, """width""": 18}
				_UpperCAmelCase    :							Tuple									=					parent
				_UpperCAmelCase    :							Tuple									=					batch_size
				_UpperCAmelCase    :							Optional[int]									=					num_channels
				_UpperCAmelCase    :							Any									=					image_size
				_UpperCAmelCase    :							str									=					min_resolution
				_UpperCAmelCase    :							Dict									=					max_resolution
				_UpperCAmelCase    :							Optional[Any]									=					do_resize
				_UpperCAmelCase    :							str									=					size
				_UpperCAmelCase    :							Optional[int]									=					do_normalize
				_UpperCAmelCase    :							Optional[Any]									=					image_mean
				_UpperCAmelCase    :							Any									=					image_std
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				}






@require_torch
@require_vision
class     _UpperCAmelCase			(		__a    ,     unittest.TestCase):
			__a :       List[str]									=							ViTImageProcessor if is_vision_available() else None
			def 							__snake_case (		self     )    ->    Any:






				'''simple docstring'''







				_UpperCAmelCase    :							Tuple									=					ViTImageProcessingTester(self     )
			@property
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				_UpperCAmelCase    :							int									=					self.image_processing_class(**self.image_processor_dict     )
				self.assertTrue(hasattr(_A      ,		"""image_mean"""     )     )
				self.assertTrue(hasattr(_A      ,		"""image_std"""     )     )
				self.assertTrue(hasattr(_A      ,		"""do_normalize"""     )     )
				self.assertTrue(hasattr(_A      ,		"""do_resize"""     )     )
				self.assertTrue(hasattr(_A      ,		"""size"""     )     )
			def 							__snake_case (		self     )    ->    Dict:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[int]									=					self.image_processing_class.from_dict(self.image_processor_dict     )
				self.assertEqual(image_processor.size      ,		{"""height""": 18, """width""": 18}     )

				_UpperCAmelCase    :							Any									=					self.image_processing_class.from_dict(self.image_processor_dict      ,		size=42     )
				self.assertEqual(image_processor.size      ,		{"""height""": 42, """width""": 42}     )
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				pass
			def 							__snake_case (		self     )    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					self.image_processing_class(**self.image_processor_dict     )
				# create random PIL images
				_UpperCAmelCase    :							Dict									=					prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A     )
				for image in image_inputs:
					self.assertIsInstance(_A      ,		Image.Image     )

				# Test not batched input
				_UpperCAmelCase    :							Optional[Any]									=					image_processing(image_inputs[0]      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							Union[str, Any]									=					image_processing(_A      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				_UpperCAmelCase    :							Union[str, Any]									=					self.image_processing_class(**self.image_processor_dict     )
				# create random numpy tensors
				_UpperCAmelCase    :							List[Any]									=					prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		numpify=_A     )
				for image in image_inputs:
					self.assertIsInstance(_A      ,		np.ndarray     )

				# Test not batched input
				_UpperCAmelCase    :							Any									=					image_processing(image_inputs[0]      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							int									=					image_processing(_A      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				_UpperCAmelCase    :							Union[str, Any]									=					self.image_processing_class(**self.image_processor_dict     )
				# create random PyTorch tensors
				_UpperCAmelCase    :							Tuple									=					prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A     )
				for image in image_inputs:
					self.assertIsInstance(_A      ,		torch.Tensor     )

				# Test not batched input
				_UpperCAmelCase    :							Optional[int]									=					image_processing(image_inputs[0]      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							Tuple									=					image_processing(_A      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)





