






import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
  from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor
class 							A_    (						__lowercase			):






 '''simple docstring'''




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

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.config_class(**self.inputs_dict)
     self.parent.assertTrue(hasattr(_A							,  '''hidden_sizes'''))
     self.parent.assertTrue(hasattr(_A							,  '''neck_hidden_sizes'''))
     self.parent.assertTrue(hasattr(_A							,  '''num_attention_heads'''))



class 							A_    :






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A=13							,  _A=32							,  _A=2							,  _A=3							,  _A=640							,  _A=4							,  _A="silu"							,  _A=3							,  _A=32							,  _A=0.1							,  _A=0.1							,  _A=0.1							,  _A=0.02							,  _A=True							,  _A=True							,  _A=10							,  _A=None							,  ) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				parent
     _UpperCAmelCase				:      List[Any]			  =				batch_size
     _UpperCAmelCase				:      List[Any]			  =				image_size
     _UpperCAmelCase				:      Tuple			  =				patch_size
     _UpperCAmelCase				:      List[str]			  =				num_channels
     _UpperCAmelCase				:      int			  =				last_hidden_size
     _UpperCAmelCase				:      List[Any]			  =				num_attention_heads
     _UpperCAmelCase				:      Any			  =				hidden_act
     _UpperCAmelCase				:      List[Any]			  =				conv_kernel_size
     _UpperCAmelCase				:      Tuple			  =				output_stride
     _UpperCAmelCase				:      List[Any]			  =				hidden_dropout_prob
     _UpperCAmelCase				:      Optional[Any]			  =				attention_probs_dropout_prob
     _UpperCAmelCase				:      Optional[Any]			  =				classifier_dropout_prob
     _UpperCAmelCase				:      Dict			  =				use_labels
     _UpperCAmelCase				:      str			  =				is_training
     _UpperCAmelCase				:      Dict			  =				num_labels
     _UpperCAmelCase				:      List[Any]			  =				initializer_range
     _UpperCAmelCase				:      int			  =				scope


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

     _UpperCAmelCase				:      List[str]			  =				None
     _UpperCAmelCase				:      Any			  =				None
     if self.use_labels:
         _UpperCAmelCase				:      Tuple			  =				ids_tensor([self.batch_size]							,  self.num_labels)
         _UpperCAmelCase				:      Optional[Any]			  =				ids_tensor([self.batch_size, self.image_size, self.image_size]							,  self.num_labels)

     _UpperCAmelCase				:      str			  =				self.get_config()

     return config, pixel_values, labels, pixel_labels


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     return MobileViTConfig(
         image_size=self.image_size							,  patch_size=self.patch_size							,  num_channels=self.num_channels							,  num_attention_heads=self.num_attention_heads							,  hidden_act=self.hidden_act							,  conv_kernel_size=self.conv_kernel_size							,  output_stride=self.output_stride							,  hidden_dropout_prob=self.hidden_dropout_prob							,  attention_probs_dropout_prob=self.attention_probs_dropout_prob							,  classifier_dropout_prob=self.classifier_dropout_prob							,  initializer_range=self.initializer_range							,  )


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				MobileViTModel(config=_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase				:      int			  =				model(_A)
     self.parent.assertEqual(
         result.last_hidden_state.shape							,  (
             self.batch_size,
             self.last_hidden_size,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )							,  )


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self.num_labels
     _UpperCAmelCase				:      Union[str, Any]			  =				MobileViTForImageClassification(_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase				:      Any			  =				model(_A							,  labels=_A)
     self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels))


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				self.num_labels
     _UpperCAmelCase				:      Optional[Any]			  =				MobileViTForSemanticSegmentation(_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase				:      Dict			  =				model(_A)
     self.parent.assertEqual(
         result.logits.shape							,  (
             self.batch_size,
             self.num_labels,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )							,  )
     _UpperCAmelCase				:      Union[str, Any]			  =				model(_A							,  labels=_A)
     self.parent.assertEqual(
         result.logits.shape							,  (
             self.batch_size,
             self.num_labels,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )							,  )


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

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				self.prepare_config_and_inputs()
     _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      Any			  =				config_and_inputs
     _UpperCAmelCase				:      Optional[int]			  =				{'''pixel_values''': pixel_values}
     return config, inputs_dict



@require_torch
class 							A_    (						__lowercase ,   __lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			List[Any] =					(
     (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 _SCREAMING_SNAKE_CASE							:			List[str] =					(
     {
         "feature-extraction": MobileViTModel,
         "image-classification": MobileViTForImageClassification,
         "image-segmentation": MobileViTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 _SCREAMING_SNAKE_CASE							:			Union[str, Any] =					False
 _SCREAMING_SNAKE_CASE							:			str =					False
 _SCREAMING_SNAKE_CASE							:			List[Any] =					False
 _SCREAMING_SNAKE_CASE							:			List[str] =					False


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

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				MobileViTModelTester(self)
     _UpperCAmelCase				:      Optional[int]			  =				MobileViTConfigTester(self							,  config_class=_A							,  has_text_modality=_A)


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     self.config_tester.run_common_tests()


 @unittest.skip(reason='''MobileViT does not use inputs_embeds''')
 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     pass


 @unittest.skip(reason='''MobileViT does not support input and output embeddings''')
 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     pass


 @unittest.skip(reason='''MobileViT does not output attentions''')
 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     pass


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      Tuple			  =				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _UpperCAmelCase				:      str			  =				model_class(_A)
         _UpperCAmelCase				:      int			  =				inspect.signature(model.forward)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _UpperCAmelCase				:      Optional[int]			  =				[*signature.parameters.keys()]

         _UpperCAmelCase				:      List[str]			  =				['''pixel_values''']
         self.assertListEqual(arg_names[:1]							,  _A)


 @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     pass


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_A)


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     def check_hidden_states_output(_A							,  _A							,  _A):
         _UpperCAmelCase				:      str			  =				model_class(_A)
         model.to(_A)
         model.eval()

         with torch.no_grad():
             _UpperCAmelCase				:      Union[str, Any]			  =				model(**self._prepare_for_class(_A							,  _A))

         _UpperCAmelCase				:      Optional[Any]			  =				outputs.hidden_states

         _UpperCAmelCase				:      Union[str, Any]			  =				5
         self.assertEqual(len(_A)							,  _A)

         # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
         # with the width and height being successively divided by 2.
         _UpperCAmelCase				:      List[str]			  =				2
         for i in range(len(_A)):
             self.assertListEqual(
                 list(hidden_states[i].shape[-2:])							,  [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]							,  )
             divisor *= 2

         self.assertEqual(self.model_tester.output_stride							,  divisor // 2)

     _UpperCAmelCase  , _UpperCAmelCase				:      Any			  =				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _UpperCAmelCase				:      Union[str, Any]			  =				True
         check_hidden_states_output(_A							,  _A							,  _A)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _UpperCAmelCase				:      Optional[int]			  =				True

         check_hidden_states_output(_A							,  _A							,  _A)


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

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_A)


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*_A)


 @slow
 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _UpperCAmelCase				:      int			  =				MobileViTModel.from_pretrained(_A)
         self.assertIsNotNone(_A)



def      _lowerCamelCase	(						)      ->					Dict:
    _UpperCAmelCase				:      Optional[Any]			  =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
    return image


@require_torch
@require_vision
class 							A_    (						unittest.TestCase			):






 '''simple docstring'''




 @cached_property
 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''') if is_vision_available() else None


 @slow
 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''').to(_A)

     _UpperCAmelCase				:      Union[str, Any]			  =				self.default_image_processor
     _UpperCAmelCase				:      Union[str, Any]			  =				prepare_img()
     _UpperCAmelCase				:      List[str]			  =				image_processor(images=_A							,  return_tensors='''pt''').to(_A)

     # forward pass
     with torch.no_grad():
         _UpperCAmelCase				:      List[str]			  =				model(**_A)

     # verify the logits
     _UpperCAmelCase				:      Optional[int]			  =				torch.Size((1, 1000))
     self.assertEqual(outputs.logits.shape							,  _A)

     _UpperCAmelCase				:      List[str]			  =				torch.tensor([-1.9364, -1.2327, -0.4653]).to(_A)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]							,  _A							,  atol=1e-4))


 @slow
 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')
     _UpperCAmelCase				:      Any			  =				model.to(_A)

     _UpperCAmelCase				:      Optional[Any]			  =				MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')

     _UpperCAmelCase				:      Optional[Any]			  =				prepare_img()
     _UpperCAmelCase				:      Union[str, Any]			  =				image_processor(images=_A							,  return_tensors='''pt''').to(_A)

     # forward pass
     with torch.no_grad():
         _UpperCAmelCase				:      List[str]			  =				model(**_A)
     _UpperCAmelCase				:      str			  =				outputs.logits

     # verify the logits
     _UpperCAmelCase				:      Optional[int]			  =				torch.Size((1, 21, 32, 32))
     self.assertEqual(logits.shape							,  _A)

     _UpperCAmelCase				:      int			  =				torch.tensor(
         [
             [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
             [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
             [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
         ]							,  device=_A							,  )

     self.assertTrue(torch.allclose(logits[0, :3, :3, :3]							,  _A							,  atol=1e-4))


 @slow
 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')
     _UpperCAmelCase				:      List[Any]			  =				model.to(_A)

     _UpperCAmelCase				:      int			  =				MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')

     _UpperCAmelCase				:      Tuple			  =				prepare_img()
     _UpperCAmelCase				:      Union[str, Any]			  =				image_processor(images=_A							,  return_tensors='''pt''').to(_A)

     # forward pass
     with torch.no_grad():
         _UpperCAmelCase				:      int			  =				model(**_A)

     _UpperCAmelCase				:      List[str]			  =				outputs.logits.detach().cpu()

     _UpperCAmelCase				:      Tuple			  =				image_processor.post_process_semantic_segmentation(outputs=_A							,  target_sizes=[(50, 60)])
     _UpperCAmelCase				:      Union[str, Any]			  =				torch.Size((50, 60))
     self.assertEqual(segmentation[0].shape							,  _A)

     _UpperCAmelCase				:      Union[str, Any]			  =				image_processor.post_process_semantic_segmentation(outputs=_A)
     _UpperCAmelCase				:      str			  =				torch.Size((32, 32))
     self.assertEqual(segmentation[0].shape							,  _A)












import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
  # Create universe of discourse in Python using linspace ()
  SCREAMING_SNAKE_CASE 							=						np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

  # Create two fuzzy sets by defining any membership function
  # (trapmf(), gbellmf(), gaussmf(), etc).
  SCREAMING_SNAKE_CASE 							=						[0, 25, 50]
  SCREAMING_SNAKE_CASE 							=						[25, 50, 75]
  SCREAMING_SNAKE_CASE 							=						fuzz.membership.trimf(X, abca)
  SCREAMING_SNAKE_CASE 							=						fuzz.membership.trimf(X, abca)

  # Compute the different operations using inbuilt functions.
  SCREAMING_SNAKE_CASE 							=						np.ones(75)
  SCREAMING_SNAKE_CASE 							=						np.zeros((75,))
  # 1. Union = max(µA(x), µB(x))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_or(X, young, X, middle_aged)[1]
  # 2. Intersection = min(µA(x), µB(x))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_and(X, young, X, middle_aged)[1]
  # 3. Complement (A) = (1- min(µA(x))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_not(young)
  # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
  # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
  SCREAMING_SNAKE_CASE 							=						young + middle_aged - (young * middle_aged)
  # 6. Algebraic Product = (µA(x) * µB(x))
  SCREAMING_SNAKE_CASE 							=						young * middle_aged
  # 7. Bounded Sum = min[1,(µA(x), µB(x))]
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
  # 8. Bounded difference = min[0,(µA(x), µB(x))]
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

  # max-min composition
  # max-product composition

  # Plot each set A, set B and each operation result using plot() and subplot().
  from matplotlib import pyplot as plt

  plt.figure()

  plt.subplot(4, 3, 1)
  plt.plot(X, young)
  plt.title('Young')
  plt.grid(True)

  plt.subplot(4, 3, 2)
  plt.plot(X, middle_aged)
  plt.title('Middle aged')
  plt.grid(True)

  plt.subplot(4, 3, 3)
  plt.plot(X, union)
  plt.title('union')
  plt.grid(True)

  plt.subplot(4, 3, 4)
  plt.plot(X, intersection)
  plt.title('intersection')
  plt.grid(True)

  plt.subplot(4, 3, 5)
  plt.plot(X, complement_a)
  plt.title('complement_a')
  plt.grid(True)

  plt.subplot(4, 3, 6)
  plt.plot(X, difference)
  plt.title('difference a/b')
  plt.grid(True)

  plt.subplot(4, 3, 7)
  plt.plot(X, alg_sum)
  plt.title('alg_sum')
  plt.grid(True)

  plt.subplot(4, 3, 8)
  plt.plot(X, alg_product)
  plt.title('alg_product')
  plt.grid(True)

  plt.subplot(4, 3, 9)
  plt.plot(X, bdd_sum)
  plt.title('bdd_sum')
  plt.grid(True)

  plt.subplot(4, 3, 10)
  plt.plot(X, bdd_difference)
  plt.title('bdd_difference')
  plt.grid(True)

  plt.subplots_adjust(hspace=0.5)
  plt.show()




