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     _A   (    unittest.TestCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=7   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=18   ,				SCREAMING_SNAKE_CASE_=30   ,				SCREAMING_SNAKE_CASE_=400   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=True   ,				)		->      Dict:





       '''simple docstring'''





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






  def 		_a					(self	)		->      Tuple:





       '''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     _A   (    __a	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						=MobileViTImageProcessor if is_vision_available() else None






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      MobileViTImageProcessingTester(self	)






  @property
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       self.assertTrue(hasattr(A__   ,				'''do_resize'''	)	)
       self.assertTrue(hasattr(A__   ,				'''size'''	)	)
       self.assertTrue(hasattr(A__   ,				'''do_center_crop'''	)	)
       self.assertTrue(hasattr(A__   ,				'''center_crop'''	)	)
       self.assertTrue(hasattr(A__   ,				'''do_flip_channel_order'''	)	)






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





       '''simple docstring'''





       UpperCamelCase__										=      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}	)

       UpperCamelCase__										=      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:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





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

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






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       UpperCamelCase__										=      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
       UpperCamelCase__										=      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
       UpperCamelCase__										=      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,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )   ,				)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       UpperCamelCase__										=      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
       UpperCamelCase__										=      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
       UpperCamelCase__										=      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,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )   ,				)




import math

import tensorflow as tf
from packaging import version







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0							) ,							x.dtype							)							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(math.pi ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi							) * (x + coeff * tf.pow(A ,							3							))							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)

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







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.79_78_84_56_08 ,							x.dtype							)

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







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(1.7_02 ,							x.dtype							)
     return x * tf.math.sigmoid(coeff * x							)







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







def  __UpperCamelCase    (						A ,							A=-1							):
     UpperCamelCase__						,       UpperCamelCase__										=      tf.split(A ,							2 ,							axis=A							)
     return a * tf.math.sigmoid(A							)


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







     def  __UpperCamelCase    (						A							):
          return tf.keras.activations.gelu(A ,							approximate=A							)

     __magic_name__								=tf.keras.activations.gelu



     __magic_name__								=approximate_gelu_wrap
else:
     __magic_name__								=_gelu
     __magic_name__								=_gelu_new


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



