




"""simple docstring"""




import math

import tensorflow as tf
from packaging import version







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  List[str]								=       tf.convert_to_tensor(__UpperCamelCase	)
			__lowercase			:  int								=       0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0	)	,				x.dtype	)	))

			return x * cdf







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Optional[int]								=       tf.convert_to_tensor(__UpperCamelCase	)
			__lowercase			:  str								=       tf.cast(math.pi	,				x.dtype	)
			__lowercase			:  Dict								=       tf.cast(0.044_715	,				x.dtype	)
			__lowercase			:  Any								=       0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi	) * (x + coeff * tf.pow(__UpperCamelCase	,				3	))	))

			return x * cdf







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Tuple								=       tf.convert_to_tensor(__UpperCamelCase	)

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







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  str								=       tf.convert_to_tensor(__UpperCamelCase	)
			__lowercase			:  int								=       tf.cast(0.044_715	,				x.dtype	)
			__lowercase			:  Union[str, Any]								=       tf.cast(0.7_978_845_608	,				x.dtype	)

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







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Optional[Any]								=       tf.convert_to_tensor(__UpperCamelCase	)
			__lowercase			:  Dict								=       tf.cast(1.702	,				x.dtype	)
			return x * tf.math.sigmoid(coeff * x	)







def      __UpperCAmelCase     ( __UpperCamelCase	):
			return tf.clip_by_value(_gelu(__UpperCamelCase	)	,				-10	,				10	)







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase=-1	):
			__lowercase			,__lowercase			:  str								=       tf.split(__UpperCamelCase	,				2	,				axis=__UpperCamelCase	)
			return a * tf.math.sigmoid(__UpperCamelCase	)


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







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








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 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Dict=7			,						__lowerCAmelCase					:					Tuple=3			,						__lowerCAmelCase					:					int=30			,						__lowerCAmelCase					:					Dict=4_00			,						__lowerCAmelCase					:					Optional[Any]=True			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Dict=True			,						__lowerCAmelCase					:					Optional[Any]=[0.5, 0.5, 0.5]			,						__lowerCAmelCase					:					Dict=[0.5, 0.5, 0.5]			,						__lowerCAmelCase					:					List[str]=True			,						__lowerCAmelCase					:					List[str]=1 / 2_55			,						__lowerCAmelCase					:					int=True			,						)   ->	List[str]:
    # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
    _A      =       size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
    _A      =       parent
    _A      =       batch_size
    _A      =       num_channels
    _A      =       min_resolution
    _A      =       max_resolution
    _A      =       do_resize
    _A      =       size
    _A      =       do_normalize
    _A      =       image_mean
    _A      =       image_std
    _A      =       do_rescale
    _A      =       rescale_factor
    _A      =       do_pad

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

    else:
       _A      =       []
       for image in image_inputs:
          _A  ,    _A      =       self.get_expected_values([image]      )
          expected_values.append((expected_height, expected_width)      )
       _A      =       max(__lowerCAmelCase			,						key=lambda __lowerCAmelCase      : item[0]      )[0]
       _A      =       max(__lowerCAmelCase			,						key=lambda __lowerCAmelCase      : item[1]      )[1]

    return expected_height, expected_width


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



 """simple docstring"""

 a__					: Any =  DeformableDetrImageProcessor if is_vision_available() else None

 def     snake_case_      (    self					:					Optional[int]      )   ->	Any:
    _A      =       DeformableDetrImageProcessingTester(self      )

 @property
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict:
    return self.image_processor_tester.prepare_image_processor_dict()

 def     snake_case_      (    self					:					Optional[int]      )   ->	List[str]:
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''image_mean'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''image_std'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_normalize'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_resize'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_rescale'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_pad'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''size'''      )      )

 def     snake_case_      (    self					:					List[str]      )   ->	int:
    _A      =       self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size			,						{'''shortest_edge''': 18, '''longest_edge''': 13_33}      )
    self.assertEqual(image_processor.do_pad			,						__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					Any      )   ->	Union[str, Any]:
    pass

 def     snake_case_      (    self					:					List[str]      )   ->	Optional[int]:
    # 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

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase      )

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

    # Test batched
    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase			,						batched=__lowerCAmelCase      )

    _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,
            expected_height,
            expected_width,
        )			,						)

 def     snake_case_      (    self					:					Tuple      )   ->	int:
    # 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

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase      )

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

    # Test batched
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase			,						batched=__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					Optional[Any]      )   ->	int:
    # 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

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase      )

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

    # Test batched
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase			,						batched=__lowerCAmelCase      )

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

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

    _A      =       {'''image_id''': 3_97_69, '''annotations''': target}

    # encode them
    _A      =       DeformableDetrImageProcessor()
    _A      =       image_processing(images=__lowerCAmelCase			,						annotations=__lowerCAmelCase			,						return_tensors='''pt'''      )

    # verify pixel values
    _A      =       torch.Size([1, 3, 8_00, 10_66]      )
    self.assertEqual(encoding['''pixel_values'''].shape			,						__lowerCAmelCase      )

    _A      =       torch.tensor([0.2796, 0.3138, 0.3481]      )
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,						__lowerCAmelCase			,						atol=1E-4      )      )

    # verify area
    _A      =       torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,						__lowerCAmelCase      )      )
    # verify boxes
    _A      =       torch.Size([6, 4]      )
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,						__lowerCAmelCase      )
    _A      =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,						__lowerCAmelCase			,						atol=1E-3      )      )
    # verify image_id
    _A      =       torch.tensor([3_97_69]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,						__lowerCAmelCase      )      )
    # verify is_crowd
    _A      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,						__lowerCAmelCase      )      )
    # verify class_labels
    _A      =       torch.tensor([75, 75, 63, 65, 17, 17]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,						__lowerCAmelCase      )      )
    # verify orig_size
    _A      =       torch.tensor([4_80, 6_40]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,						__lowerCAmelCase      )      )
    # verify size
    _A      =       torch.tensor([8_00, 10_66]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,						__lowerCAmelCase      )      )

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

    _A      =       {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

    _A      =       pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

    # encode them
    _A      =       DeformableDetrImageProcessor(format='''coco_panoptic'''      )
    _A      =       image_processing(images=__lowerCAmelCase			,						annotations=__lowerCAmelCase			,						masks_path=__lowerCAmelCase			,						return_tensors='''pt'''      )

    # verify pixel values
    _A      =       torch.Size([1, 3, 8_00, 10_66]      )
    self.assertEqual(encoding['''pixel_values'''].shape			,						__lowerCAmelCase      )

    _A      =       torch.tensor([0.2796, 0.3138, 0.3481]      )
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,						__lowerCAmelCase			,						atol=1E-4      )      )

    # verify area
    _A      =       torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,						__lowerCAmelCase      )      )
    # verify boxes
    _A      =       torch.Size([6, 4]      )
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,						__lowerCAmelCase      )
    _A      =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,						__lowerCAmelCase			,						atol=1E-3      )      )
    # verify image_id
    _A      =       torch.tensor([3_97_69]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,						__lowerCAmelCase      )      )
    # verify is_crowd
    _A      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,						__lowerCAmelCase      )      )
    # verify class_labels
    _A      =       torch.tensor([17, 17, 63, 75, 75, 93]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,						__lowerCAmelCase      )      )
    # verify masks
    _A      =       82_28_73
    self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()			,						__lowerCAmelCase      )
    # verify orig_size
    _A      =       torch.tensor([4_80, 6_40]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,						__lowerCAmelCase      )      )
    # verify size
    _A      =       torch.tensor([8_00, 10_66]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,						__lowerCAmelCase      )      )



