





import math
import sys

import cva
import numpy as np



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       np.ndarray:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						math.sqrt(__lowerCAmelCase     )
      SCREAMING_SNAKE_CASE		=						1 / (sigma * math.sqrt(2 * math.pi     ))
      return cons * np.exp(-((img / sigma) ** 2) * 0.5     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       np.ndarray:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						kernel_size // 2
      return img[x - half : x + half + 1, y - half : y + half + 1]



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       np.ndarray:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						np.zeros((kernel_size, kernel_size)     )
      for i in range(0			,						__lowerCAmelCase     ):
            for j in range(0			,						__lowerCAmelCase     ):
                  SCREAMING_SNAKE_CASE		=						math.sqrt(
                      abs(i - kernel_size // 2     ) ** 2 + abs(j - kernel_size // 2     ) ** 2     )
      return vec_gaussian(__lowerCAmelCase			,						__lowerCAmelCase     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						)			->       np.ndarray:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						np.zeros(img.shape     )
      SCREAMING_SNAKE_CASE		=						get_gauss_kernel(__lowerCAmelCase			,						__lowerCAmelCase     )
      SCREAMING_SNAKE_CASE		=						img.shape
      for i in range(kernel_size // 2			,						size_x - kernel_size // 2     ):
            for j in range(kernel_size // 2			,						size_y - kernel_size // 2     ):
                  SCREAMING_SNAKE_CASE		=						get_slice(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase     )
                  SCREAMING_SNAKE_CASE		=						img_s - img_s[kernel_size // 2, kernel_size // 2]
                  SCREAMING_SNAKE_CASE		=						vec_gaussian(__lowerCAmelCase			,						__lowerCAmelCase     )
                  SCREAMING_SNAKE_CASE		=						np.multiply(__lowerCAmelCase			,						__lowerCAmelCase     )
                  SCREAMING_SNAKE_CASE		=						np.multiply(__lowerCAmelCase			,						__lowerCAmelCase     )
                  SCREAMING_SNAKE_CASE		=						np.sum(__lowerCAmelCase     ) / np.sum(__lowerCAmelCase     )
                  SCREAMING_SNAKE_CASE		=						val
      return imga



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						args[1] if args[1:] else '''../image_data/lena.jpg'''
      SCREAMING_SNAKE_CASE		=						float(args[2]     ) if args[2:] else 1.0
      SCREAMING_SNAKE_CASE		=						float(args[3]     ) if args[3:] else 1.0
      if args[4:]:
            SCREAMING_SNAKE_CASE		=						int(args[4]     )
            SCREAMING_SNAKE_CASE		=						kernel_size + abs(kernel_size % 2 - 1     )
      else:
            SCREAMING_SNAKE_CASE		=						5
      return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_						       =	parse_args(sys.argv)
      SCREAMING_SNAKE_CASE_						       =	cva.imread(filename, 0)
      cva.imshow("""input image""", img)

      SCREAMING_SNAKE_CASE_						       =	img / 2_5_5
      SCREAMING_SNAKE_CASE_						       =	out.astype("""float32""")
      SCREAMING_SNAKE_CASE_						       =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
      SCREAMING_SNAKE_CASE_						       =	out * 2_5_5
      SCREAMING_SNAKE_CASE_						       =	np.uinta(out)
      cva.imshow("""output image""", out)
      cva.waitKey(0)
      cva.destroyAllWindows()









import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[int]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]=3						,lowerCamelCase__						:   List[str]=32						,lowerCamelCase__						:   List[Any]=3						,lowerCamelCase__						:   str=10						,lowerCamelCase__						:   Any=[10, 20, 30, 40]						,lowerCamelCase__						:   Optional[Any]=[1, 1, 2, 1]						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   int=True						,lowerCamelCase__						:   Tuple="relu"						,lowerCamelCase__						:   Dict=3						,lowerCamelCase__						:   Optional[int]=None						,)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						embeddings_size
           SCREAMING_SNAKE_CASE		=						hidden_sizes
           SCREAMING_SNAKE_CASE		=						depths
           SCREAMING_SNAKE_CASE		=						is_training
           SCREAMING_SNAKE_CASE		=						use_labels
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						num_labels
           SCREAMING_SNAKE_CASE		=						scope
           SCREAMING_SNAKE_CASE		=						len(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           SCREAMING_SNAKE_CASE		=						self.get_config()

           return config, pixel_values






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Optional[int]:




           '''simple docstring'''


           return RegNetConfig(
               num_channels=self.num_channels						,embeddings_size=self.embeddings_size						,hidden_sizes=self.hidden_sizes						,depths=self.depths						,hidden_act=self.hidden_act						,num_labels=self.num_labels						,image_size=self.image_size						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Union[str, Any]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetModel(config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)

           # Output shape (b, c, h, w)
           self.parent.assertEqual(
               result.last_hidden_state.shape						,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   Optional[Any]	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.num_labels
           SCREAMING_SNAKE_CASE		=						FlaxRegNetForImageClassification(config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)
           self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						config_and_inputs
           SCREAMING_SNAKE_CASE		=						{"""pixel_values""": pixel_values}
           return config, inputs_dict

@require_flax
class 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Union[str, Any]		     =      (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

     __snake_case   :     Tuple		     =      False
     __snake_case   :     int		     =      False
     __snake_case   :     Tuple		     =      False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetModelTester(self	)
           SCREAMING_SNAKE_CASE		=						ConfigTester(self						,config_class=lowerCamelCase__						,has_text_modality=lowerCamelCase__	)






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




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		List[str]:




           '''simple docstring'''


           return






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__	)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__	)






     @unittest.skip(reason="""RegNet does not use inputs_embeds"""	)
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""RegNet does not support input and output embeddings"""	)
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




           '''simple docstring'''


           pass






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




           '''simple docstring'''


           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.__call__	)
                 # 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__	)






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




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]	):
                 SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)

                 SCREAMING_SNAKE_CASE		=						model(**self._prepare_for_class(lowerCamelCase__						,lowerCamelCase__	)	)

                 SCREAMING_SNAKE_CASE		=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 SCREAMING_SNAKE_CASE		=						self.model_tester.num_stages
                 self.assertEqual(len(lowerCamelCase__	)						,expected_num_stages + 1	)

           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     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                       SCREAMING_SNAKE_CASE		=						self._prepare_for_class(lowerCamelCase__						,lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)

                       @jax.jit
                       def model_jitted(lowerCamelCase__						:   Dict						,**lowerCamelCase__						:   Optional[Any]	):
                             return model(pixel_values=lowerCamelCase__						,**lowerCamelCase__	)

                       with self.subTest("""JIT Enabled"""	):
                             SCREAMING_SNAKE_CASE		=						model_jitted(**lowerCamelCase__	).to_tuple()

                       with self.subTest("""JIT Disabled"""	):
                             with jax.disable_jit():
                                   SCREAMING_SNAKE_CASE		=						model_jitted(**lowerCamelCase__	).to_tuple()

                       self.assertEqual(len(lowerCamelCase__	)						,len(lowerCamelCase__	)	)
                       for jitted_output, output in zip(lowerCamelCase__						,lowerCamelCase__	):
                             self.assertEqual(jitted_output.shape						,output.shape	)



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


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      return image



@require_flax
class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     @cached_property
     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		str:




           '''simple docstring'''


           return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""	) if is_vision_available() else None






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""	)

           SCREAMING_SNAKE_CASE		=						self.default_image_processor
           SCREAMING_SNAKE_CASE		=						prepare_img()
           SCREAMING_SNAKE_CASE		=						image_processor(images=lowerCamelCase__						,return_tensors="""np"""	)

           SCREAMING_SNAKE_CASE		=						model(**lowerCamelCase__	)

           # verify the logits
           SCREAMING_SNAKE_CASE		=						(1, 1000)
           self.assertEqual(outputs.logits.shape						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						jnp.array([-0.4180, -1.5051, -3.4836]	)

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


