import math

import tensorflow as tf
from packaging import version






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			lowercase							=		tf.convert_to_tensor(UpperCAmelCase       )
			lowercase							=		0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0       ),		x.dtype       )       ))

			return x * cdf






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			lowercase							=		tf.convert_to_tensor(UpperCAmelCase       )
			lowercase							=		tf.cast(math.pi,		x.dtype       )
			lowercase							=		tf.cast(0.04_4715,		x.dtype       )
			lowercase							=		0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi       ) * (x + coeff * tf.pow(UpperCAmelCase,		3       ))       ))

			return x * cdf






def        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







			lowercase							=		tf.convert_to_tensor(UpperCAmelCase       )

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






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			lowercase							=		tf.convert_to_tensor(UpperCAmelCase       )
			lowercase							=		tf.cast(0.04_4715,		x.dtype       )
			lowercase							=		tf.cast(0.79_7884_5608,		x.dtype       )

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






def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







			lowercase							=		tf.convert_to_tensor(UpperCAmelCase       )
			lowercase							=		tf.cast(1.702,		x.dtype       )
			return x * tf.math.sigmoid(coeff * x       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[Any]:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase=-1       )->       Tuple:



			"""simple docstring"""







			lowercase		,lowercase							=		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       )->       Optional[Any]:



									"""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        __UpperCAmelCase    (       UpperCAmelCase       )->       List[Any]:



			"""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()       )}'       )






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 MobileViTImageProcessor






class      __lowercase					(    unittest.TestCase    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      int=7							,							__lowerCamelCase				:      Dict=3							,							__lowerCamelCase				:      Optional[int]=18							,							__lowerCamelCase				:      List[Any]=30							,							__lowerCamelCase				:      Union[str, Any]=4_00							,							__lowerCamelCase				:      Optional[int]=True							,							__lowerCamelCase				:      List[str]=None							,							__lowerCamelCase				:      Dict=True							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      Tuple=True							,							)							->		Tuple:


									'''simple docstring'''

									lowercase							=		size if size is not None else {'''shortest_edge''': 20}
									lowercase							=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		num_channels
									lowercase							=		image_size
									lowercase							=		min_resolution
									lowercase							=		max_resolution
									lowercase							=		do_resize
									lowercase							=		size
									lowercase							=		do_center_crop
									lowercase							=		crop_size
									lowercase							=		do_flip_channel_order



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


									'''simple docstring'''

									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_flip_channel_order": self.do_flip_channel_order,
									}






@require_torch
@require_vision
class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     MobileViTImageProcessor if is_vision_available() else None

						def        __a		(	self				:      Dict   )							->		Dict:


									'''simple docstring'''

									lowercase							=		MobileViTImageProcessingTester(self   )

						@property
						def        __a		(	self				:      Tuple   )							->		Dict:


									'''simple docstring'''

									return self.image_processor_tester.prepare_image_processor_dict()

						def        __a		(	self				:      Dict   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		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_flip_channel_order'''   )   )

						def        __a		(	self				:      List[str]   )							->		str:


									'''simple docstring'''

									lowercase							=		self.image_processing_class.from_dict(self.image_processor_dict   )
									self.assertEqual(image_processor.size							,							{'''shortest_edge''': 20}   )
									self.assertEqual(image_processor.crop_size							,							{'''height''': 18, '''width''': 18}   )

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

						def        __a		(	self				:      Any   )							->		Optional[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Any   )							->		List[str]:


									'''simple docstring'''

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

									# Test not batched input
									lowercase							=		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
									lowercase							=		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        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random numpy tensors
									lowercase							=		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
									lowercase							=		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
									lowercase							=		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        __a		(	self				:      Dict   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random PyTorch tensors
									lowercase							=		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
									lowercase							=		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
									lowercase							=		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'''],
									    )							,							)





