




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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.config_class(**self.inputs_dict		)
       self.parent.assertTrue(hasattr(_lowerCAmelCase							,							'hidden_sizes'		)		)
       self.parent.assertTrue(hasattr(_lowerCAmelCase							,							'neck_hidden_sizes'		)		)
       self.parent.assertTrue(hasattr(_lowerCAmelCase							,							'num_attention_heads'		)		)





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Any							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						List[Any]=13							,							_lowerCAmelCase    :						str=32							,							_lowerCAmelCase    :						str=2							,							_lowerCAmelCase    :						List[Any]=3							,							_lowerCAmelCase    :						List[str]=640							,							_lowerCAmelCase    :						Union[str, Any]=4							,							_lowerCAmelCase    :						Dict="silu"							,							_lowerCAmelCase    :						List[str]=3							,							_lowerCAmelCase    :						str=32							,							_lowerCAmelCase    :						Any=0.1							,							_lowerCAmelCase    :						List[str]=0.1							,							_lowerCAmelCase    :						Union[str, Any]=0.1							,							_lowerCAmelCase    :						Optional[Any]=0.02							,							_lowerCAmelCase    :						Union[str, Any]=True							,							_lowerCAmelCase    :						Optional[int]=True							,							_lowerCAmelCase    :						Any=10							,							_lowerCAmelCase    :						str=None							,							):
       SCREAMING_SNAKE_CASE_      		=     parent
       SCREAMING_SNAKE_CASE_      		=     batch_size
       SCREAMING_SNAKE_CASE_      		=     image_size
       SCREAMING_SNAKE_CASE_      		=     patch_size
       SCREAMING_SNAKE_CASE_      		=     num_channels
       SCREAMING_SNAKE_CASE_      		=     last_hidden_size
       SCREAMING_SNAKE_CASE_      		=     num_attention_heads
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     conv_kernel_size
       SCREAMING_SNAKE_CASE_      		=     output_stride
       SCREAMING_SNAKE_CASE_      		=     hidden_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     classifier_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     use_labels
       SCREAMING_SNAKE_CASE_      		=     is_training
       SCREAMING_SNAKE_CASE_      		=     num_labels
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     scope



 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     None
       if self.use_labels:
             SCREAMING_SNAKE_CASE_      		=     ids_tensor([self.batch_size]							,							self.num_labels		)
             SCREAMING_SNAKE_CASE_      		=     ids_tensor([self.batch_size, self.image_size, self.image_size]							,							self.num_labels		)

       SCREAMING_SNAKE_CASE_      		=     self.get_config()

       return config, pixel_values, labels, pixel_labels



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       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  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     MobileViTModel(config=_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase		)
       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  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						str		):
       SCREAMING_SNAKE_CASE_      		=     self.num_labels
       SCREAMING_SNAKE_CASE_      		=     MobileViTForImageClassification(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							labels=_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)		)



 def  lowerCAmelCase_    (				self    :						int							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     self.num_labels
       SCREAMING_SNAKE_CASE_      		=     MobileViTForSemanticSegmentation(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase		)
       self.parent.assertEqual(
           result.logits.shape							,							(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )							,							)
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							labels=_lowerCAmelCase		)
       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  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     config_and_inputs
       SCREAMING_SNAKE_CASE_      		=     {'pixel_values': pixel_values}
       return config, inputs_dict





@require_torch
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







 lowercase_							       =       (
     (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 lowercase_							       =       (
     {
         "feature-extraction": MobileViTModel,
         "image-classification": MobileViTForImageClassification,
         "image-segmentation": MobileViTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False



 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     MobileViTModelTester(self		)
       SCREAMING_SNAKE_CASE_      		=     MobileViTConfigTester(self							,							config_class=_lowerCAmelCase							,							has_text_modality=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       self.config_tester.run_common_tests()



 @unittest.skip(reason='MobileViT does not use inputs_embeds'		)
 def  lowerCAmelCase_    (				self    :						Tuple		):
       pass



 @unittest.skip(reason='MobileViT does not support input and output embeddings'		)
 def  lowerCAmelCase_    (				self    :						Dict		):
       pass



 @unittest.skip(reason='MobileViT does not output attentions'		)
 def  lowerCAmelCase_    (				self    :						List[str]		):
       pass



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE_      		=     ['pixel_values']
             self.assertListEqual(arg_names[:1]							,							_lowerCAmelCase		)



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



 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       def check_hidden_states_output(_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						List[str]		):
             SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		)
             model.to(_lowerCAmelCase		)
             model.eval()

             with torch.no_grad():
                   SCREAMING_SNAKE_CASE_      		=     model(**self._prepare_for_class(_lowerCAmelCase							,							_lowerCAmelCase		)		)

             SCREAMING_SNAKE_CASE_      		=     outputs.hidden_states

             SCREAMING_SNAKE_CASE_      		=     5
             self.assertEqual(len(_lowerCAmelCase		)							,							_lowerCAmelCase		)

             # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
             # with the width and height being successively divided by 2.
             SCREAMING_SNAKE_CASE_      		=     2
             for i in range(len(_lowerCAmelCase		)		):
                   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		)

       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_      		=     True
             check_hidden_states_output(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             SCREAMING_SNAKE_CASE_      		=     True

             check_hidden_states_output(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCAmelCase		)




 @slow
 def  lowerCAmelCase_    (				self    :						Dict		):
       for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_      		=     MobileViTModel.from_pretrained(_lowerCAmelCase		)
             self.assertIsNotNone(_lowerCAmelCase		)


def    UpperCAmelCase_				( )   ->					Union[str, Any]:
      SCREAMING_SNAKE_CASE_      		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
      return image





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


 '''simple docstring'''







 @cached_property
 def  lowerCAmelCase_    (				self    :						List[Any]		):
       return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'		) if is_vision_available() else None



 @slow
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'		).to(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     self.default_image_processor
       SCREAMING_SNAKE_CASE_      		=     prepare_img()
       SCREAMING_SNAKE_CASE_      		=     image_processor(images=_lowerCAmelCase							,							return_tensors='pt'		).to(_lowerCAmelCase		)

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       # verify the logits
       SCREAMING_SNAKE_CASE_      		=     torch.Size((1, 1_000)		)
       self.assertEqual(outputs.logits.shape							,							_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     torch.tensor([-1.9364, -1.2327, -0.4653]		).to(_lowerCAmelCase		)

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



 @slow
 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'		)
       SCREAMING_SNAKE_CASE_      		=     model.to(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'		)

       SCREAMING_SNAKE_CASE_      		=     prepare_img()
       SCREAMING_SNAKE_CASE_      		=     image_processor(images=_lowerCAmelCase							,							return_tensors='pt'		).to(_lowerCAmelCase		)

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE_      		=     torch.Size((1, 21, 32, 32)		)
       self.assertEqual(logits.shape							,							_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     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=_lowerCAmelCase							,							)

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




 @slow
 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'		)
       SCREAMING_SNAKE_CASE_      		=     model.to(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'		)

       SCREAMING_SNAKE_CASE_      		=     prepare_img()
       SCREAMING_SNAKE_CASE_      		=     image_processor(images=_lowerCAmelCase							,							return_tensors='pt'		).to(_lowerCAmelCase		)

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     outputs.logits.detach().cpu()

       SCREAMING_SNAKE_CASE_      		=     image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase							,							target_sizes=[(50, 60)]		)
       SCREAMING_SNAKE_CASE_      		=     torch.Size((50, 60)		)
       self.assertEqual(segmentation[0].shape							,							_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.Size((32, 32)		)
       self.assertEqual(segmentation[0].shape							,							_lowerCAmelCase		)





import operator as op

def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					Any:
      SCREAMING_SNAKE_CASE_      		=     []
      SCREAMING_SNAKE_CASE_      		=     lambda __UpperCAmelCase     ,     __UpperCAmelCase				: int(x / y				)  # noqa: E731 integer division operation
      SCREAMING_SNAKE_CASE_      		=     {
          '^': op.pow,
          '*': op.mul,
          '/': div,
          '+': op.add,
          '-': op.sub,
      }  # operators & their respective operation

      # print table header
      print('Symbol'.center(8				)     ,     'Action'.center(12				)     ,     'Stack'     ,     sep=' | '				)
      print('-' * (30 + len(__UpperCAmelCase				))				)

      for x in post_fix:
            if x.isdigit():  # if x in digit
                  stack.append(__UpperCAmelCase				)  # append x to stack
                  # output in tabular format
                  print(x.rjust(8				)     ,     ('push(' + x + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '				)
            else:
                  SCREAMING_SNAKE_CASE_      		=     stack.pop()  # pop stack
                  # output in tabular format
                  print(''.rjust(8				)     ,     ('pop(' + b + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '				)

                  SCREAMING_SNAKE_CASE_      		=     stack.pop()  # pop stack
                  # output in tabular format
                  print(''.rjust(8				)     ,     ('pop(' + a + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '				)

                  stack.append(
                      str(opr[x](int(__UpperCAmelCase				)     ,     int(__UpperCAmelCase				)				)				)				)  # evaluate the 2 values popped from stack & push result to stack
                  # output in tabular format
                  print(
                      x.rjust(8				)     ,     ('push(' + a + x + b + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '     ,     )

      return int(stack[0]				)


if __name__ == "__main__":
   lowerCamelCase__					:  Tuple      							=				input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
   print('\n\tResult = ', solve(Postfix))