





from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Dict:
					return getitem, k







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Union[str, Any]:
					return setitem, k, v







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Optional[int]:
					return delitem, k







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						*a  )      ->      str:
					try:
										return fun(a					,						*a  ), None
					except Exception as e:
										return None, e


UpperCAmelCase						:   str			  =     (
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
)

UpperCAmelCase						:   Any			  =     [
    _set('''key_a''', '''val_a'''),
    _set('''key_a''', '''val_b'''),
]

UpperCAmelCase						:   Union[str, Any]			  =     [
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
    _del('''key_a'''),
    _del('''key_b'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
]

UpperCAmelCase						:   int			  =     [
    _get('''key_a'''),
    _del('''key_a'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
    _del('''key_a'''),
    _get('''key_a'''),
]

UpperCAmelCase						:   Optional[int]			  =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

UpperCAmelCase						:   List[Any]			  =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('''key_a''', '''val_b'''),
]







@pytest.mark.parametrize(
    'operations'					,						(
        pytest.param(_add_items					,						id='add items'  ),
        pytest.param(_overwrite_items					,						id='overwrite items'  ),
        pytest.param(_delete_items					,						id='delete items'  ),
        pytest.param(_access_absent_items					,						id='access absent items'  ),
        pytest.param(_add_with_resize_up					,						id='add with resize up'  ),
        pytest.param(_add_with_resize_down					,						id='add with resize down'  ),
    )					,						)
def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Dict:
					__A	:						List[Any]       =				HashMap(initial_block_size=4  )
					__A	:						Dict       =				{}
					for _, (fun, *args) in enumerate(a  ):
										__A    ,				__A	:						Optional[int]       =				_run_operation(a					,						a					,						*a  )
										__A    ,				__A	:						Union[str, Any]       =				_run_operation(a					,						a					,						*a  )
										assert my_res == py_res
										assert str(a  ) == str(a  )
										assert set(a  ) == set(a  )
										assert len(a  ) == len(a  )
										assert set(my.items()  ) == set(py.items()  )







def  _SCREAMING_SNAKE_CASE				(   )      ->      str:
					def is_public(a  ) -> bool:
										return not name.startswith('_'  )

					__A	:						Optional[int]       =				{name for name in dir({}  ) if is_public(a  )}
					__A	:						List[Any]       =				{name for name in dir(HashMap()  ) if is_public(a  )}

					assert dict_public_names > hash_public_names











import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 YolosImageProcessor





class 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=7     ,					_A=3     ,					_A=30     ,					_A=400     ,					_A=True     ,					_A=None     ,					_A=True     ,					_A=[0.5, 0.5, 0.5]     ,					_A=[0.5, 0.5, 0.5]     ,					_A=True     ,					_A=1 / 255     ,					_A=True     ,					):
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									__A	:						List[Any]       =				size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
									__A	:						Union[str, Any]       =				parent
									__A	:						Optional[int]       =				batch_size
									__A	:						int       =				num_channels
									__A	:						int       =				min_resolution
									__A	:						Any       =				max_resolution
									__A	:						List[Any]       =				do_resize
									__A	:						List[Any]       =				size
									__A	:						Union[str, Any]       =				do_normalize
									__A	:						Optional[int]       =				image_mean
									__A	:						Optional[int]       =				image_std
									__A	:						int       =				do_rescale
									__A	:						str       =				rescale_factor
									__A	:						Tuple       =				do_pad



				def 					UpperCAmelCase_		(			self     ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A=False     ):
									if not batched:
														__A	:						List[str]       =				image_inputs[0]
														if isinstance(_A     ,					Image.Image     ):
																			__A    ,				__A	:						int       =				image.size
														else:
																			__A    ,				__A	:						Any       =				image.shape[1], image.shape[2]
														if w < h:
																			__A	:						List[Any]       =				int(self.size['shortest_edge'] * h / w     )
																			__A	:						List[Any]       =				self.size['shortest_edge']
														elif w > h:
																			__A	:						Union[str, Any]       =				self.size['shortest_edge']
																			__A	:						str       =				int(self.size['shortest_edge'] * w / h     )
														else:
																			__A	:						Dict       =				self.size['shortest_edge']
																			__A	:						str       =				self.size['shortest_edge']

									else:
														__A	:						int       =				[]
														for image in image_inputs:
																			__A    ,				__A	:						Optional[Any]       =				self.get_expected_values([image]     )
																			expected_values.append((expected_height, expected_width)     )
														__A	:						List[str]       =				max(_A     ,					key=lambda _A     : item[0]     )[0]
														__A	:						str       =				max(_A     ,					key=lambda _A     : item[1]     )[1]

									return expected_height, expected_width





@require_torch
@require_vision
class 						_A(					snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  List[str]					    =	YolosImageProcessor if is_vision_available() else None



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				YolosImageProcessingTester(self     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									return self.image_processor_tester.prepare_image_processor_dict()



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				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 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				self.image_processing_class.from_dict(self.image_processor_dict     )
									self.assertEqual(image_processor.size     ,					{'shortest_edge': 18, 'longest_edge': 1333}     )
									self.assertEqual(image_processor.do_pad     ,					_A     )

									__A	:						Dict       =				self.image_processing_class.from_dict(
									    self.image_processor_dict     ,					size=42     ,					max_size=84     ,					pad_and_return_pixel_mask=_A     )
									self.assertEqual(image_processor.size     ,					{'shortest_edge': 42, 'longest_edge': 84}     )
									self.assertEqual(image_processor.do_pad     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									pass



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						List[str]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PIL images
									__A	:						List[Any]       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					Image.Image     )

									# Test not batched input
									__A	:						Any       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						Optional[int]       =				self.image_processor_tester.get_expected_values(_A     )

									self.assertEqual(
									    encoded_images.shape     ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,					)

									# Test batched
									__A    ,				__A	:						Optional[Any]       =				self.image_processor_tester.get_expected_values(_A     ,					batched=_A     )

									__A	:						str       =				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,
									        expected_height,
									        expected_width,
									    )     ,					)



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						Optional[Any]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random numpy tensors
									__A	:						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
									__A	:						str       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						List[Any]       =				self.image_processor_tester.get_expected_values(_A     )

									self.assertEqual(
									    encoded_images.shape     ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,					)

									# Test batched
									__A	:						Tuple       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						Optional[int]       =				self.image_processor_tester.get_expected_values(_A     ,					batched=_A     )

									self.assertEqual(
									    encoded_images.shape     ,					(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )     ,					)



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						Union[str, Any]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									__A	:						Dict       =				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
									__A	:						Union[str, Any]       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						Union[str, Any]       =				self.image_processor_tester.get_expected_values(_A     )

									self.assertEqual(
									    encoded_images.shape     ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,					)

									# Test batched
									__A	:						Optional[int]       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						Optional[int]       =				self.image_processor_tester.get_expected_values(_A     ,					batched=_A     )

									self.assertEqual(
									    encoded_images.shape     ,					(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )     ,					)



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processings
									__A	:						Tuple       =				self.image_processing_class(**self.image_processor_dict     )
									__A	:						Any       =				self.image_processing_class(do_resize=_A     ,					do_normalize=_A     ,					do_rescale=_A     )
									# create random PyTorch tensors
									__A	:						Union[str, Any]       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     ,					torchify=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					torch.Tensor     )

									# Test whether the method "pad" and calling the image processor return the same tensors
									__A	:						Optional[int]       =				image_processing_a.pad(_A     ,					return_tensors='pt'     )
									__A	:						Optional[int]       =				image_processing_a(_A     ,					return_tensors='pt'     )

									self.assertTrue(
									    torch.allclose(encoded_images_with_method['pixel_values']     ,					encoded_images['pixel_values']     ,					atol=1e-4     )     )



				@slow
				def 					UpperCAmelCase_		(			self     ):
									# prepare image and target
									__A	:						List[Any]       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
									with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,					'r'     ) as f:
														__A	:						Optional[Any]       =				json.loads(f.read()     )

									__A	:						Optional[Any]       =				{'image_id': 39769, 'annotations': target}

									# encode them
									__A	:						str       =				YolosImageProcessor.from_pretrained('hustvl/yolos-small'     )
									__A	:						List[Any]       =				image_processing(images=_A     ,					annotations=_A     ,					return_tensors='pt'     )

									# verify pixel values
									__A	:						List[Any]       =				torch.Size([1, 3, 800, 1066]     )
									self.assertEqual(encoding['pixel_values'].shape     ,					_A     )

									__A	:						Union[str, Any]       =				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,					_A     ,					atol=1e-4     )     )

									# verify area
									__A	:						List[Any]       =				torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,					_A     )     )
									# verify boxes
									__A	:						Any       =				torch.Size([6, 4]     )
									self.assertEqual(encoding['labels'][0]['boxes'].shape     ,					_A     )
									__A	:						Optional[Any]       =				torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,					_A     ,					atol=1e-3     )     )
									# verify image_id
									__A	:						Optional[int]       =				torch.tensor([39769]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,					_A     )     )
									# verify is_crowd
									__A	:						str       =				torch.tensor([0, 0, 0, 0, 0, 0]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,					_A     )     )
									# verify class_labels
									__A	:						Any       =				torch.tensor([75, 75, 63, 65, 17, 17]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,					_A     )     )
									# verify orig_size
									__A	:						int       =				torch.tensor([480, 640]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,					_A     )     )
									# verify size
									__A	:						str       =				torch.tensor([800, 1066]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,					_A     )     )





				@slow
				def 					UpperCAmelCase_		(			self     ):
									# prepare image, target and masks_path
									__A	:						Dict       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
									with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'     ,					'r'     ) as f:
														__A	:						Tuple       =				json.loads(f.read()     )

									__A	:						Any       =				{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

									__A	:						List[Any]       =				pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

									# encode them
									__A	:						Any       =				YolosImageProcessor(format='coco_panoptic'     )
									__A	:						List[Any]       =				image_processing(images=_A     ,					annotations=_A     ,					masks_path=_A     ,					return_tensors='pt'     )

									# verify pixel values
									__A	:						Any       =				torch.Size([1, 3, 800, 1066]     )
									self.assertEqual(encoding['pixel_values'].shape     ,					_A     )

									__A	:						Union[str, Any]       =				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,					_A     ,					atol=1e-4     )     )

									# verify area
									__A	:						int       =				torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,					_A     )     )
									# verify boxes
									__A	:						Optional[int]       =				torch.Size([6, 4]     )
									self.assertEqual(encoding['labels'][0]['boxes'].shape     ,					_A     )
									__A	:						Optional[Any]       =				torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,					_A     ,					atol=1e-3     )     )
									# verify image_id
									__A	:						Union[str, Any]       =				torch.tensor([39769]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,					_A     )     )
									# verify is_crowd
									__A	:						Tuple       =				torch.tensor([0, 0, 0, 0, 0, 0]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,					_A     )     )
									# verify class_labels
									__A	:						List[str]       =				torch.tensor([17, 17, 63, 75, 75, 93]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,					_A     )     )
									# verify masks
									__A	:						Tuple       =				822873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,					_A     )
									# verify orig_size
									__A	:						str       =				torch.tensor([480, 640]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,					_A     )     )
									# verify size
									__A	:						int       =				torch.tensor([800, 1066]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,					_A     )     )




