






from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     k_size // 2
   UpperCamelCase_		,      UpperCamelCase_									=     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
   UpperCamelCase_									=     1 / (2 * pi * sigma) * exp(-(square(__lowercase) + square(__lowercase)) / (2 * square(__lowercase)))
   return g






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_		,      UpperCamelCase_									=     image.shape[0], image.shape[1]
   # dst image height and width
   UpperCamelCase_									=     height - k_size + 1
   UpperCamelCase_									=     width - k_size + 1

   # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
   UpperCamelCase_									=     zeros((dst_height * dst_width, k_size * k_size))
   UpperCamelCase_									=     0
   for i, j in product(range(__lowercase)    ,      range(__lowercase)):
      UpperCamelCase_									=     ravel(image[i : i + k_size, j : j + k_size])
      UpperCamelCase_									=     window
      row += 1

   #  turn the kernel into shape(k*k, 1)
   UpperCamelCase_									=     gen_gaussian_kernel(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     ravel(__lowercase)

   # reshape and get the dst image
   UpperCamelCase_									=     dot(__lowercase    ,      __lowercase).reshape(__lowercase    ,      __lowercase).astype(__lowercase)

   return dst


if __name__ == "__main__":
     # read original image
     snake_case__      :	Optional[Any]       	=      imread(R"""../image_data/lena.jpg""")
     # turn image in gray scale value
     snake_case__      :	List[str]       	=      cvtColor(img, COLOR_BGR2GRAY)

     # get values with two different mask size
     snake_case__      :	List[str]       	=      gaussian_filter(gray, 3, sigma=1)
     snake_case__      :	Dict       	=      gaussian_filter(gray, 5, sigma=0.8)

     # show result images
     imshow("""gaussian filter with 3x3 mask""", gaussianaxa)
     imshow("""gaussian filter with 5x5 mask""", gaussianaxa)
     waitKey()













import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     LevitForImageClassification,
     LevitForImageClassificationWithTeacher,
     LevitModel,
 )
 from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     self.config_class(**self.inputs_dict )
     self.parent.assertTrue(hasattr(_UpperCAmelCase		,		'hidden_sizes' ) )
     self.parent.assertTrue(hasattr(_UpperCAmelCase		,		'num_attention_heads' ) )




class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=64		,		_UpperCAmelCase=3		,		_UpperCAmelCase=3		,		_UpperCAmelCase=2		,		_UpperCAmelCase=1		,		_UpperCAmelCase=16		,		_UpperCAmelCase=[128, 256, 384]		,		_UpperCAmelCase=[4, 6, 8]		,		_UpperCAmelCase=[2, 3, 4]		,		_UpperCAmelCase=[16, 16, 16]		,		_UpperCAmelCase=0		,		_UpperCAmelCase=[2, 2, 2]		,		_UpperCAmelCase=[2, 2, 2]		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=2		,		)			-> int:
     UpperCamelCase_									=     parent
     UpperCamelCase_									=     batch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     kernel_size
     UpperCamelCase_									=     stride
     UpperCamelCase_									=     padding
     UpperCamelCase_									=     hidden_sizes
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     depths
     UpperCamelCase_									=     key_dim
     UpperCamelCase_									=     drop_path_rate
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     attention_ratio
     UpperCamelCase_									=     mlp_ratio
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     [
         ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
         ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
     ]
     UpperCamelCase_									=     is_training
     UpperCamelCase_									=     use_labels
     UpperCamelCase_									=     num_labels
     UpperCamelCase_									=     initializer_range




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

     UpperCamelCase_									=     self.get_config()
     return config, pixel_values, labels




  def    _UpperCAmelCase     (     self )			-> str:
     return LevitConfig(
         image_size=self.image_size		,		num_channels=self.num_channels		,		kernel_size=self.kernel_size		,		stride=self.stride		,		padding=self.padding		,		patch_size=self.patch_size		,		hidden_sizes=self.hidden_sizes		,		num_attention_heads=self.num_attention_heads		,		depths=self.depths		,		key_dim=self.key_dim		,		drop_path_rate=self.drop_path_rate		,		mlp_ratio=self.mlp_ratio		,		attention_ratio=self.attention_ratio		,		initializer_range=self.initializer_range		,		down_ops=self.down_ops		,		)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> int:
     UpperCamelCase_									=     LevitModel(config=_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase )
     UpperCamelCase_									=     (self.image_size, self.image_size)
     UpperCamelCase_		,      UpperCamelCase_									=     image_size[0], image_size[1]
     for _ in range(4 ):
        UpperCamelCase_									=     floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
        UpperCamelCase_									=     floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
     self.parent.assertEqual(
         result.last_hidden_state.shape		,		(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1])		,		)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Tuple:
     UpperCamelCase_									=     self.num_labels
     UpperCamelCase_									=     LevitForImageClassification(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase		,		labels=_UpperCAmelCase )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_labels) )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.prepare_config_and_inputs()
     UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     config_and_inputs
     UpperCamelCase_									=     {'pixel_values': pixel_values}
     return config, inputs_dict




@require_torch
class 						_a       (  UpperCAmelCase__						,       UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   (
      (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
      if is_torch_available()
      else ()
  )
  A_						=   (
      {
          """feature-extraction""": LevitModel,
          """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
      }
      if is_torch_available()
      else {}
  )

  A_						=   False
  A_						=   False
  A_						=   False
  A_						=   False
  A_						=   False




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     LevitModelTester(self )
     UpperCamelCase_									=     ConfigTester(self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase		,		hidden_size=37 )




  def    _UpperCAmelCase     (     self )			-> Dict:
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()




  def    _UpperCAmelCase     (     self )			-> Any:
     return




  @unittest.skip(reason='Levit does not use inputs_embeds' )
  def    _UpperCAmelCase     (     self )			-> str:
     pass




  @unittest.skip(reason='Levit does not support input and output embeddings' )
  def    _UpperCAmelCase     (     self )			-> Dict:
     pass




  @unittest.skip(reason='Levit does not output attentions' )
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     pass




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase_									=     ['pixel_values']
        self.assertListEqual(arg_names[:1]		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     def check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase ):
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        model.to(_UpperCAmelCase )
        model.eval()

        with torch.no_grad():
           UpperCamelCase_									=     model(**self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase ) )

        UpperCamelCase_									=     outputs.hidden_states

        UpperCamelCase_									=     len(self.model_tester.depths ) + 1
        self.assertEqual(len(_UpperCAmelCase )		,		_UpperCAmelCase )

        UpperCamelCase_									=     (self.model_tester.image_size, self.model_tester.image_size)
        UpperCamelCase_		,      UpperCamelCase_									=     image_size[0], image_size[1]
        for _ in range(4 ):
           UpperCamelCase_									=     floor(
               (
                   (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                   / self.model_tester.stride
               )
               + 1 )
           UpperCamelCase_									=     floor(
               (
                   (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                   / self.model_tester.stride
               )
               + 1 )
        # verify the first hidden states (first block)
        self.assertListEqual(
            list(hidden_states[0].shape[-2:] )		,		[
                height * width,
                self.model_tester.hidden_sizes[0],
            ]		,		)

     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_									=     True
        check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )

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

        check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )




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




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=False )			-> Tuple:
     UpperCamelCase_									=     super()._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )

     if return_labels:
        if model_class.__name__ == "LevitForImageClassificationWithTeacher":
           del inputs_dict["labels"]

     return inputs_dict




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     if not self.model_tester.is_training:
        return

     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()
     UpperCamelCase_									=     True

     for model_class in self.all_model_classes:
        # LevitForImageClassificationWithTeacher supports inference-only
        if (
            model_class in get_values(_UpperCAmelCase )
            or model_class.__name__ == "LevitForImageClassificationWithTeacher"
        ):
           continue
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        model.to(_UpperCAmelCase )
        model.train()
        UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )
        UpperCamelCase_									=     model(**_UpperCAmelCase ).loss
        loss.backward()




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
        return

     UpperCamelCase_									=     False
     UpperCamelCase_									=     True

     for model_class in self.all_model_classes:
        if model_class in get_values(_UpperCAmelCase ) or not model_class.supports_gradient_checkpointing:
           continue
        # LevitForImageClassificationWithTeacher supports inference-only
        if model_class.__name__ == "LevitForImageClassificationWithTeacher":
           continue
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        model.gradient_checkpointing_enable()
        model.to(_UpperCAmelCase )
        model.train()
        UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )
        UpperCamelCase_									=     model(**_UpperCAmelCase ).loss
        loss.backward()




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     UpperCamelCase_									=     [
         {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
         {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
         {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
     ]

     for model_class in self.all_model_classes:
        if (
            model_class
            not in [
                *get_values(_UpperCAmelCase ),
            ]
            or model_class.__name__ == "LevitForImageClassificationWithTeacher"
        ):
           continue

        for problem_type in problem_types:
           with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}""" ):
              UpperCamelCase_									=     problem_type['title']
              UpperCamelCase_									=     problem_type['num_labels']

              UpperCamelCase_									=     model_class(_UpperCAmelCase )
              model.to(_UpperCAmelCase )
              model.train()

              UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )

              if problem_type["num_labels"] > 1:
                 UpperCamelCase_									=     inputs['labels'].unsqueeze(1 ).repeat(1		,		problem_type['num_labels'] )

              UpperCamelCase_									=     inputs['labels'].to(problem_type['dtype'] )

              # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
              # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
              # they have the same size." which is a symptom something in wrong for the regression problem.
              # See https://github.com/huggingface/transformers/issues/11780
              with warnings.catch_warnings(record=_UpperCAmelCase ) as warning_list:
                 UpperCamelCase_									=     model(**_UpperCAmelCase ).loss
              for w in warning_list:
                 if "Using a target size that is different to the input size" in str(w.message ):
                    raise ValueError(
                        f"""Something is going wrong in the regression problem: intercepted {w.message}""" )

              loss.backward()




  @slow
  def    _UpperCAmelCase     (     self )			-> List[str]:
     for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_									=     LevitModel.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )






def     _snake_case    ():
   UpperCamelCase_									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image


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






  """simple docstring"""





  @cached_property
  def    _UpperCAmelCase     (     self )			-> str:
     return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )




  @slow
  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
         _UpperCAmelCase )

     UpperCamelCase_									=     self.default_image_processor
     UpperCamelCase_									=     prepare_img()
     UpperCamelCase_									=     image_processor(images=_UpperCAmelCase		,		return_tensors='pt' ).to(_UpperCAmelCase )

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

     # verify the logits
     UpperCamelCase_									=     torch.Size((1, 1000) )
     self.assertEqual(outputs.logits.shape		,		_UpperCAmelCase )

     UpperCamelCase_									=     torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(_UpperCAmelCase )

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





