code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import inspect
  import unittest
  
  from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel
  
  if is_vision_available():
        from PIL import Image
  
        from transformers import AutoImageProcessor
  
  
  
  
  
  
  class    __A       ( unittest.TestCase    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A				,					__A=3				,					__A=32				,					__A=3				,					__A=10				,					__A=[10, 20, 30, 40]				,					__A=[1, 1, 2, 1]				,					__A=True				,					__A=True				,					__A="relu"				,					__A=3				,					__A=None				,					)      ->  int:
       a					=parent
       a					=batch_size
       a					=image_size
       a					=num_channels
       a					=embeddings_size
       a					=hidden_sizes
       a					=depths
       a					=is_training
       a					=use_labels
       a					=hidden_act
       a					=num_labels
       a					=scope
       a					=len(__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       a					=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
  
       a					=self.get_config()
  
       return config, pixel_values
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
       return ResNetConfig(
           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				,					__A				,					__A		)      ->  List[Any]:
       a					=FlaxResNetModel(config=__A		)
       a					=model(__A		)
  
       # 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				,					__A				,					__A		)      ->  Dict:
       a					=self.num_labels
       a					=FlaxResNetForImageClassification(config=__A		)
       a					=model(__A		)
       self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.num_labels)		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       a					=self.prepare_config_and_inputs()
       a	,   a					=config_and_inputs
       a					={'''pixel_values''': pixel_values}
       return config, inputs_dict
  
  
  
  
  
  
  @require_flax
  class    __A       ( _SCREAMING_SNAKE_CASE,					unittest.TestCase    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()
  
   __lowerCAmelCase				      =						False
   __lowerCAmelCase				      =						False
   __lowerCAmelCase				      =						False
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  None:
       a					=FlaxResNetModelTester(self		)
       a					=ConfigTester(self				,					config_class=__A				,					has_text_modality=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       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		)      ->  Union[str, Any]:
       return
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       a					=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       a					=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__A		)
  
  
   @unittest.skip(reason='''ResNet does not use inputs_embeds'''		)
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
       pass
  
  
   @unittest.skip(reason='''ResNet does not support input and output embeddings'''		)
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       pass
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       a	,   a					=self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
           a					=model_class(__A		)
           a					=inspect.signature(model.__call__		)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           a					=[*signature.parameters.keys()]
  
           a					=['''pixel_values''']
           self.assertListEqual(arg_names[:1]				,					__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       def check_hidden_states_output(__A				,					__A				,					__A		):
           a					=model_class(__A		)
  
           a					=model(**self._prepare_for_class(__A				,					__A		)		)
  
           a					=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
           a					=self.model_tester.num_stages
           self.assertEqual(len(__A		)				,					expected_num_stages + 1		)
  
  
   @unittest.skip(reason='''ResNet does not use feedforward chunking'''		)
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
       pass
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
       a	,   a					=self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__		):
               a					=self._prepare_for_class(__A				,					__A		)
               a					=model_class(__A		)
  
               @jax.jit
               def model_jitted(__A				,					**__A		):
                   return model(pixel_values=__A				,					**__A		)
  
               with self.subTest('''JIT Enabled'''		):
                   a					=model_jitted(**__A		).to_tuple()
  
               with self.subTest('''JIT Disabled'''		):
                   with jax.disable_jit():
                       a					=model_jitted(**__A		).to_tuple()
  
               self.assertEqual(len(__A		)				,					len(__A		)		)
               for jitted_output, output in zip(__A				,					__A		):
                   self.assertEqual(jitted_output.shape				,					output.shape		)
  
  def _A		(		):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
      return image
  
  
  
  
  
  
  @require_flax
  class    __A       ( unittest.TestCase    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   @cached_property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       return AutoImageProcessor.from_pretrained('''microsoft/resnet-50'''		) if is_vision_available() else None
  
  
  
  
  
   @slow
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       a					=FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50'''		)
  
       a					=self.default_image_processor
       a					=prepare_img()
       a					=image_processor(images=__A				,					return_tensors='''np'''		)
  
       a					=model(**__A		)
  
       # verify the logits
       a					=(1, 1000)
       self.assertEqual(outputs.logits.shape				,					__A		)
  
       a					=jnp.array([-11.1_069, -9.7_877, -8.3_777]		)
  
       self.assertTrue(jnp.allclose(outputs.logits[0, :3]				,					__A				,					atol=1E-4		)		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from math import gcd
  def _A		(		lowercase = 1_20_00	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=0
      for d in range(max_d + 1	):
          for n in range(d // 3 + 1 ,					(d + 1) // 2	):
              if gcd(lowercase ,					lowercase	) == 1:
                  fractions_number += 1
      return fractions_number
  
  
  if __name__ == "__main__":
         print(F'{solution() = }')