




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 DeiTImageProcessor




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				def __init__( self							:	Tuple   , __lowerCamelCase							:	Any   , __lowerCamelCase							:	Dict=7   , __lowerCamelCase							:	Any=3   , __lowerCamelCase							:	Any=18   , __lowerCamelCase							:	List[Any]=30   , __lowerCamelCase							:	List[Any]=4_00   , __lowerCamelCase							:	List[Any]=True   , __lowerCamelCase							:	Any=None   , __lowerCamelCase							:	Any=True   , __lowerCamelCase							:	Optional[Any]=None   , __lowerCamelCase							:	Optional[Any]=True   , __lowerCamelCase							:	int=[0.5, 0.5, 0.5]   , __lowerCamelCase							:	Optional[Any]=[0.5, 0.5, 0.5]   , )   ->	Any:
							a       =     size if size is not None else {'height': 20, 'width': 20}
							a       =     crop_size if crop_size is not None else {'height': 18, 'width': 18}

							a       =     parent
							a       =     batch_size
							a       =     num_channels
							a       =     image_size
							a       =     min_resolution
							a       =     max_resolution
							a       =     do_resize
							a       =     size
							a       =     do_center_crop
							a       =     crop_size
							a       =     do_normalize
							a       =     image_mean
							a       =     image_std





				def 				__UpperCAmelCase				( self							:	int				)   ->	str:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}




@require_torch
@require_vision
class        snake_case__				(UpperCAmelCase__       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Tuple									=   DeiTImageProcessor if is_vision_available() else None
				SCREAMING_SNAKE_CASE_			:				Tuple									=   True





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Optional[int]:
							a       =     DeiTImageProcessingTester(self				)





				@property
				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	List[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Optional[Any]:
							a       =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_resize"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "size"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_center_crop"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "center_crop"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_normalize"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "image_mean"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "image_std"				)				)





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Tuple:
							a       =     self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size   , {"height": 20, "width": 20}				)
							self.assertEqual(image_processor.crop_size   , {"height": 18, "width": 18}				)

							a       =     self.image_processing_class.from_dict(self.image_processor_dict   , size=42   , crop_size=84				)
							self.assertEqual(image_processor.size   , {"height": 42, "width": 42}				)
							self.assertEqual(image_processor.crop_size   , {"height": 84, "width": 84}				)





				def 				__UpperCAmelCase				( self							:	str				)   ->	List[str]:
							pass





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	List[str]:
							# Initialize image_processing
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							a       =     prepare_image_inputs(self.image_processor_tester   , equal_resolution=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , Image.Image				)

							# Test not batched input
							a       =     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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )   , )

							# Test batched
							a       =     image_processing(__lowerCamelCase   , 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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )   , )





				def 				__UpperCAmelCase				( self							:	int				)   ->	str:
							# Initialize image_processing
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							a       =     prepare_image_inputs(self.image_processor_tester   , equal_resolution=__lowerCamelCase   , numpify=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , np.ndarray				)

							# Test not batched input
							a       =     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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )   , )

							# Test batched
							a       =     image_processing(__lowerCamelCase   , 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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )   , )





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Tuple:
							# Initialize image_processing
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							a       =     prepare_image_inputs(self.image_processor_tester   , equal_resolution=__lowerCamelCase   , torchify=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , torch.Tensor				)

							# Test not batched input
							a       =     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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )   , )

							# Test batched
							a       =     image_processing(__lowerCamelCase   , 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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )   , )








import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:int 		=    copy.deepcopy(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:List[Any] 		=    sorted(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    [bisect.bisect_right(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							) for y in x]
			return quantized




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=0							):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    np.arange(start=3     ,						stop=SCREAMING_SNAKE_CASE     ,						step=4							).tolist() if k > 0 else [10]
			UpperCAmelCase__		:List[Any] 		=    _quantize(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							)
			# count number of elements per group
			UpperCAmelCase__		:Dict 		=    np.unique(SCREAMING_SNAKE_CASE     ,						return_counts=SCREAMING_SNAKE_CASE							)[1]
			UpperCAmelCase__		:Any 		=    [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 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



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


						def __iter__(  self	)  ->Dict:
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)

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


						def __len__(  self	)  ->Union[str, Any]:
									return (len(self.sampler	) + self.batch_size - 1) // self.batch_size





