





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




           '''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(lowercase_       )






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




           '''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						:    int       )     ->		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						:    Optional[int]						,lowerCamelCase__						:    List[Any]						,lowerCamelCase__						:    Union[str, Any]       )     ->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetModel(config=lowercase_       )
           SCREAMING_SNAKE_CASE		=						model(lowercase_       )

           # 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						:    Dict						,lowerCamelCase__						:    Tuple						,lowerCamelCase__						:    Tuple       )     ->		Optional[Any]:




           '''simple docstring'''


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






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




           '''simple docstring'''


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

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



     '''simple docstring'''

     __snake_case    :	Any			=			(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Any:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		Any:




           '''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						:    str       )     ->		int:




           '''simple docstring'''


           return






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




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int       )     ->		int:




           '''simple docstring'''


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






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




           '''simple docstring'''


           pass






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




           '''simple docstring'''


           pass






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




           '''simple docstring'''


           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(lowercase_       )
                 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]						,lowercase_       )






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




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__						:    Any						,lowerCamelCase__						:    List[str]						,lowerCamelCase__						:    Any       ):
                 SCREAMING_SNAKE_CASE		=						model_class(lowercase_       )

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

                 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(lowercase_       )						,expected_num_stages + 1       )

           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(lowercase_						,lowercase_						,lowercase_       )

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

                 check_hidden_states_output(lowercase_						,lowercase_						,lowercase_       )






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




           '''simple docstring'''


           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(lowercase_						,lowercase_       )
                       SCREAMING_SNAKE_CASE		=						model_class(lowercase_       )

                       @jax.jit
                       def model_jitted(lowerCamelCase__						:    List[Any]						,**lowerCamelCase__						:    Tuple       ):
                             return model(pixel_values=lowercase_						,**lowercase_       )

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

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

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



def    __lowercase   (      ) ->   Optional[int]:


      '''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						:    str       )     ->		Dict:




           '''simple docstring'''


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






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




           '''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=lowercase_						,return_tensors="""np"""       )

           SCREAMING_SNAKE_CASE		=						model(**lowercase_       )

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

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

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




"""simple docstring"""





def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					List[str]			     =    int(__lowerCamelCase  )
   if n_element < 1:
      UpperCAmelCase_					:					List[Any]			     =    ValueError("a should be a positive number"  )
      raise my_error

   UpperCAmelCase_					:					List[Any]			     =    [1]
   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    (0, 0, 0)
   UpperCAmelCase_					:					Dict			     =    1
   while index < n_element:
      while hamming_list[i] * 2 <= hamming_list[-1]:
         i += 1
      while hamming_list[j] * 3 <= hamming_list[-1]:
         j += 1
      while hamming_list[k] * 5 <= hamming_list[-1]:
         k += 1
      hamming_list.append(
          min(hamming_list[i] * 2,		hamming_list[j] * 3,		hamming_list[k] * 5  )  )
      index += 1
   return hamming_list


if __name__ == "__main__":
    _a		       =							input('Enter the last number (nth term) of the Hamming Number Series: ')
    print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
    _a		       =							hamming(int(n))
    print('-----------------------------------------------------')
    print(f"""The list with nth numbers is: {hamming_numbers}""")
    print('-----------------------------------------------------')


