



'''simple docstring'''

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 DeformableDetrImageProcessor






class 						a_		(     unittest.TestCase    ):


							def __init__(				self   :						int      ,						a_   :						Optional[int]      ,						a_   :						List[str]=7      ,						a_   :						Tuple=3      ,						a_   :						str=3_0      ,						a_   :						str=4_0_0      ,						a_   :						str=True      ,						a_   :						int=None      ,						a_   :						Optional[Any]=True      ,						a_   :						Dict=[0.5, 0.5, 0.5]      ,						a_   :						Any=[0.5, 0.5, 0.5]      ,						a_   :						Tuple=True      ,						a_   :						Dict=1 / 2_5_5      ,						a_   :						Optional[int]=True      ,						)			->					List[Any]:
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										snake_case:    Dict      							=size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
										snake_case:    int      							=parent
										snake_case:    Any      							=batch_size
										snake_case:    List[Any]      							=num_channels
										snake_case:    Dict      							=min_resolution
										snake_case:    str      							=max_resolution
										snake_case:    List[str]      							=do_resize
										snake_case:    int      							=size
										snake_case:    Tuple      							=do_normalize
										snake_case:    List[Any]      							=image_mean
										snake_case:    List[Any]      							=image_std
										snake_case:    str      							=do_rescale
										snake_case:    int      							=rescale_factor
										snake_case:    Union[str, Any]      							=do_pad


							def    UpperCamelCase     (				self   :						str			)			->					List[str]:
										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   :						Optional[int]      ,						a_   :						int      ,						a_   :						List[Any]=False			)			->					Dict:
										if not batched:
													snake_case:    int      							=image_inputs[0]
													if isinstance(a_      ,						Image.Image			):
																snake_case							,  snake_case:    Tuple      							=image.size
													else:
																snake_case							,  snake_case:    Any      							=image.shape[1], image.shape[2]
													if w < h:
																snake_case:    Tuple      							=int(self.size['shortest_edge'] * h / w			)
																snake_case:    List[Any]      							=self.size['shortest_edge']
													elif w > h:
																snake_case:    str      							=self.size['shortest_edge']
																snake_case:    Optional[int]      							=int(self.size['shortest_edge'] * w / h			)
													else:
																snake_case:    int      							=self.size['shortest_edge']
																snake_case:    Optional[Any]      							=self.size['shortest_edge']

										else:
													snake_case:    Dict      							=[]
													for image in image_inputs:
																snake_case							,  snake_case:    str      							=self.get_expected_values([image]			)
																expected_values.append((expected_height, expected_width)			)
													snake_case:    int      							=max(a_      ,						key=lambda a_			: item[0]			)[0]
													snake_case:    Dict      							=max(a_      ,						key=lambda a_			: item[1]			)[1]

										return expected_height, expected_width






@require_torch
@require_vision
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		List[str]        =				DeformableDetrImageProcessor if is_vision_available() else None


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[Any]:
										snake_case:    Dict      							=DeformableDetrImageProcessingTester(self			)


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


							def    UpperCamelCase     (				self   :						List[Any]			)			->					Tuple:
										snake_case:    List[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_      ,						'do_rescale'			)			)
										self.assertTrue(hasattr(a_      ,						'do_pad'			)			)
										self.assertTrue(hasattr(a_      ,						'size'			)			)


							def    UpperCamelCase     (				self   :						str			)			->					List[Any]:
										snake_case:    str      							=self.image_processing_class.from_dict(self.image_processor_dict			)
										self.assertEqual(image_processor.size      ,						{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}			)
										self.assertEqual(image_processor.do_pad      ,						a_			)

										snake_case:    Union[str, Any]      							=self.image_processing_class.from_dict(
										    self.image_processor_dict      ,						size=4_2      ,						max_size=8_4      ,						pad_and_return_pixel_mask=a_			)
										self.assertEqual(image_processor.size      ,						{'shortest_edge': 4_2, 'longest_edge': 8_4}			)
										self.assertEqual(image_processor.do_pad      ,						a_			)


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


							def    UpperCamelCase     (				self   :						int			)			->					Union[str, Any]:
										# Initialize image_processing
										snake_case:    List[Any]      							=self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										snake_case:    Optional[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
										snake_case:    List[str]      							=image_processing(image_inputs[0]      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    Dict      							=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
										snake_case							,  snake_case:    Dict      							=self.image_processor_tester.get_expected_values(a_      ,						batched=a_			)

										snake_case:    List[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   :						Tuple			)			->					Union[str, Any]:
										# Initialize image_processing
										snake_case:    int      							=self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										snake_case:    List[str]      							=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
										snake_case:    Union[str, Any]      							=image_processing(image_inputs[0]      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    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
										snake_case:    Optional[int]      							=image_processing(a_      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    Tuple      							=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   :						Dict			)			->					Any:
										# Initialize image_processing
										snake_case:    Tuple      							=self.image_processing_class(**self.image_processor_dict			)
										# create random PyTorch tensors
										snake_case:    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 not batched input
										snake_case:    str      							=image_processing(image_inputs[0]      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    List[str]      							=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
										snake_case:    int      							=image_processing(a_      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    List[str]      							=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,
										    )      ,						)


							@slow
							def    UpperCamelCase     (				self   :						str			)			->					Dict:
										# prepare image and target
										snake_case:    Dict      							=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
										with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,						'r'			) as f:
													snake_case:    Dict      							=json.loads(f.read()			)

										snake_case:    Dict      							={'image_id': 3_9_7_6_9, 'annotations': target}

										# encode them
										snake_case:    Dict      							=DeformableDetrImageProcessor()
										snake_case:    Dict      							=image_processing(images=a_      ,						annotations=a_      ,						return_tensors='pt'			)

										# verify pixel values
										snake_case:    Any      							=torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
										self.assertEqual(encoding['pixel_values'].shape      ,						a_			)

										snake_case:    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
										snake_case:    List[str]      							=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
										snake_case:    List[Any]      							=torch.Size([6, 4]			)
										self.assertEqual(encoding['labels'][0]['boxes'].shape      ,						a_			)
										snake_case:    Optional[int]      							=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
										snake_case:    str      							=torch.tensor([3_9_7_6_9]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,						a_			)			)
										# verify is_crowd
										snake_case:    int      							=torch.tensor([0, 0, 0, 0, 0, 0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,						a_			)			)
										# verify class_labels
										snake_case:    List[Any]      							=torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,						a_			)			)
										# verify orig_size
										snake_case:    Dict      							=torch.tensor([4_8_0, 6_4_0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,						a_			)			)
										# verify size
										snake_case:    Any      							=torch.tensor([8_0_0, 1_0_6_6]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,						a_			)			)




							@slow
							def    UpperCamelCase     (				self   :						str			)			->					Union[str, Any]:
										# prepare image, target and masks_path
										snake_case:    Union[str, Any]      							=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
										with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,						'r'			) as f:
													snake_case:    Any      							=json.loads(f.read()			)

										snake_case:    Union[str, Any]      							={'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

										snake_case:    Optional[int]      							=pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

										# encode them
										snake_case:    Dict      							=DeformableDetrImageProcessor(format='coco_panoptic'			)
										snake_case:    Tuple      							=image_processing(images=a_      ,						annotations=a_      ,						masks_path=a_      ,						return_tensors='pt'			)

										# verify pixel values
										snake_case:    Any      							=torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
										self.assertEqual(encoding['pixel_values'].shape      ,						a_			)

										snake_case:    Dict      							=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
										snake_case:    Optional[Any]      							=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
										snake_case:    Tuple      							=torch.Size([6, 4]			)
										self.assertEqual(encoding['labels'][0]['boxes'].shape      ,						a_			)
										snake_case:    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
										snake_case:    List[str]      							=torch.tensor([3_9_7_6_9]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,						a_			)			)
										# verify is_crowd
										snake_case:    Optional[Any]      							=torch.tensor([0, 0, 0, 0, 0, 0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,						a_			)			)
										# verify class_labels
										snake_case:    Optional[int]      							=torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,						a_			)			)
										# verify masks
										snake_case:    Optional[Any]      							=8_2_2_8_7_3
										self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,						a_			)
										# verify orig_size
										snake_case:    Dict      							=torch.tensor([4_8_0, 6_4_0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,						a_			)			)
										# verify size
										snake_case:    List[Any]      							=torch.tensor([8_0_0, 1_0_6_6]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,						a_			)			)









'''simple docstring'''

import math

import tensorflow as tf
from packaging import version







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    str      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    int      							=0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0					)				,							x.dtype					)					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(math.pi				,							x.dtype					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi					) * (x + coeff * tf.pow(__UpperCAmelCase				,							3					))					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)

			return x * tf.tanh(tf.math.softplus(__UpperCAmelCase					)					)







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    List[str]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=tf.cast(0.79_7884_5608				,							x.dtype					)

			return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)					))







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(1.702				,							x.dtype					)
			return x * tf.math.sigmoid(coeff * x					)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			return tf.clip_by_value(_gelu(__UpperCAmelCase					)				,							-10				,							10					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=-1					)				->					List[str]:





			"""simple docstring"""







			snake_case							,  snake_case:    int      							=tf.split(__UpperCAmelCase				,							2				,							axis=__UpperCAmelCase					)
			return a * tf.math.sigmoid(__UpperCAmelCase					)


if version.parse(tf.version.VERSION) >= version.parse('2.4'):







	def a_     (    __UpperCAmelCase					)				->					int:





				"""simple docstring"""







				return tf.keras.activations.gelu(__UpperCAmelCase				,							approximate=__UpperCAmelCase					)

	a							   =				tf.keras.activations.gelu







	a							   =				approximate_gelu_wrap
else:
	a							   =				_gelu
	a							   =				_gelu_new


a							   =				{
    'gelu': gelu,
    'gelu_10': gelu_aa,
    'gelu_fast': gelu_fast,
    'gelu_new': gelu_new,
    'glu': glu,
    'mish': mish,
    'quick_gelu': quick_gelu,
    'relu': tf.keras.activations.relu,
    'sigmoid': tf.keras.activations.sigmoid,
    'silu': tf.keras.activations.swish,
    'swish': tf.keras.activations.swish,
    'tanh': tf.keras.activations.tanh,
}







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()					)}'''					)




