code: 
  
  
  
  
  
  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 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_			,					UpperCamelCase_=3			,					UpperCamelCase_=32			,					UpperCamelCase_=3			,					UpperCamelCase_=10			,					UpperCamelCase_=[10, 20, 30, 40]			,					UpperCamelCase_=[1, 1, 2, 1]			,					UpperCamelCase_=True			,					UpperCamelCase_=True			,					UpperCamelCase_="relu"			,					UpperCamelCase_=3			,					UpperCamelCase_=None			,					)			->		Optional[Any]:
  		snake_case__  					=      parent
  		snake_case__  					=      batch_size
  		snake_case__  					=      image_size
  		snake_case__  					=      num_channels
  		snake_case__  					=      embeddings_size
  		snake_case__  					=      hidden_sizes
  		snake_case__  					=      depths
  		snake_case__  					=      is_training
  		snake_case__  					=      use_labels
  		snake_case__  					=      hidden_act
  		snake_case__  					=      num_labels
  		snake_case__  					=      scope
  		snake_case__  					=      len(UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
  
  		snake_case__  					=      self.get_config()
  
  		return config, pixel_values
  
  
  
  
  
  	def      _snake_case      (		self		)			->		int:
  		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      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_		)			->		List[str]:
  		snake_case__  					=      FlaxResNetModel(config=UpperCamelCase_		)
  		snake_case__  					=      model(UpperCamelCase_		)
  
  		# 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      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Optional[Any]:
  		snake_case__  					=      self.num_labels
  		snake_case__  					=      FlaxResNetForImageClassification(config=UpperCamelCase_		)
  		snake_case__  					=      model(UpperCamelCase_		)
  		self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		snake_case__  					=      self.prepare_config_and_inputs()
  		snake_case__					,     snake_case__  					=      config_and_inputs
  		snake_case__  					=      {'pixel_values': pixel_values}
  		return config, inputs_dict
  
  
  
  
  
  
  @require_flax
  class 							__snake_case    (				__magic_name__					,					unittest.TestCase       ):
  	__lowerCAmelCase						=     (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()
  
  	__lowerCAmelCase						=     False
  	__lowerCAmelCase						=     False
  	__lowerCAmelCase						=     False
  
  
  
  
  
  	def      _snake_case      (		self		)			->		None:
  		snake_case__  					=      FlaxResNetModelTester(self		)
  		snake_case__  					=      ConfigTester(self			,					config_class=UpperCamelCase_			,					has_text_modality=UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		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      _snake_case      (		self		)			->		Dict:
  		return
  
  
  
  
  
  	def      _snake_case      (		self		)			->		str:
  		snake_case__  					=      self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_model(*UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Any:
  		snake_case__  					=      self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_		)
  
  
  
  
  
  	@unittest.skip(reason='ResNet does not use inputs_embeds'		)
  	def      _snake_case      (		self		)			->		int:
  		pass
  
  
  
  
  
  	@unittest.skip(reason='ResNet does not support input and output embeddings'		)
  	def      _snake_case      (		self		)			->		Tuple:
  		pass
  
  
  
  
  
  	def      _snake_case      (		self		)			->		int:
  		snake_case__					,     snake_case__  					=      self.model_tester.prepare_config_and_inputs_for_common()
  
  		for model_class in self.all_model_classes:
  			snake_case__  					=      model_class(UpperCamelCase_		)
  			snake_case__  					=      inspect.signature(model.__call__		)
  			# signature.parameters is an OrderedDict => so arg_names order is deterministic
  			snake_case__  					=      [*signature.parameters.keys()]
  
  			snake_case__  					=      ['pixel_values']
  			self.assertListEqual(arg_names[:1]			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		def check_hidden_states_output(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		):
  			snake_case__  					=      model_class(UpperCamelCase_		)
  
  			snake_case__  					=      model(**self._prepare_for_class(UpperCamelCase_			,					UpperCamelCase_		)		)
  
  			snake_case__  					=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  			snake_case__  					=      self.model_tester.num_stages
  			self.assertEqual(len(UpperCamelCase_		)			,					expected_num_stages + 1		)
  
  
  
  
  
  	@unittest.skip(reason='ResNet does not use feedforward chunking'		)
  	def      _snake_case      (		self		)			->		str:
  		pass
  
  
  
  
  
  	def      _snake_case      (		self		)			->		str:
  		snake_case__					,     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__		):
  				snake_case__  					=      self._prepare_for_class(UpperCamelCase_			,					UpperCamelCase_		)
  				snake_case__  					=      model_class(UpperCamelCase_		)
  
  				@jax.jit
  				def model_jitted(UpperCamelCase_			,					**UpperCamelCase_		):
  					return model(pixel_values=UpperCamelCase_			,					**UpperCamelCase_		)
  
  				with self.subTest('JIT Enabled'		):
  					snake_case__  					=      model_jitted(**UpperCamelCase_		).to_tuple()
  
  				with self.subTest('JIT Disabled'		):
  					with jax.disable_jit():
  						snake_case__  					=      model_jitted(**UpperCamelCase_		).to_tuple()
  
  				self.assertEqual(len(UpperCamelCase_		)			,					len(UpperCamelCase_		)		)
  				for jitted_output, output in zip(UpperCamelCase_			,					UpperCamelCase_		):
  					self.assertEqual(jitted_output.shape			,					output.shape		)
  
  
  
  
  
  def 	__lowerCamelCase (  )      ->Optional[int]:
  	snake_case__  					=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  	return image
  
  
  
  
  
  
  
  @require_flax
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	@cached_property
  	def      _snake_case      (		self		)			->		Optional[Any]:
  		return AutoImageProcessor.from_pretrained('microsoft/resnet-50'		) if is_vision_available() else None
  
  
  
  
  
  	@slow
  	def      _snake_case      (		self		)			->		Tuple:
  		snake_case__  					=      FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'		)
  
  		snake_case__  					=      self.default_image_processor
  		snake_case__  					=      prepare_img()
  		snake_case__  					=      image_processor(images=UpperCamelCase_			,					return_tensors='np'		)
  
  		snake_case__  					=      model(**UpperCamelCase_		)
  
  		# verify the logits
  		snake_case__  					=      (1, 1000)
  		self.assertEqual(outputs.logits.shape			,					UpperCamelCase_		)
  
  		snake_case__  					=      jnp.array([-11.1069, -9.7_8_7_7, -8.3_7_7_7]		)
  
  		self.assertTrue(jnp.allclose(outputs.logits[0, :3]			,					UpperCamelCase_			,					atol=1E-4		)		)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  a__				: Any									=    {
      0: '''underwear''',
      1: '''pants''',
      2: '''belt''',
      3: '''suit''',
      4: '''shoe''',
      5: '''socks''',
      6: '''shirt''',
      7: '''tie''',
      8: '''watch''',
  }
  
  a__				: Optional[Any]									=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
  
  a__				: List[str]									=    [0 for x in range(len(graph))]
  a__				: List[Any]									=    []
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	while stack:
  		snake_case__  					=      stack.pop()
  		print(UpperCAmelCase_				,							clothes[current_clothing]       )
  		order += 1
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	for v in graph[u]:
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  	stack.append(UpperCAmelCase_       )
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Union[str, Any]:
  	for v in range(len(UpperCAmelCase_       )       ):
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  
  if __name__ == "__main__":
  			topological_sort(graph, visited)
  			print(stack)
  			print_stack(stack, clothes)
  
  
  
  