
'''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 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






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

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							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 		_lowerCamelCase						(self	:			List[str]     ) ->							Tuple:
        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 		_lowerCamelCase						(self	:			int						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Dict     ) ->							Optional[int]:
        snake_case_    							=				FlaxResNetModel(config=lowerCAmelCase_     )
        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 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Any     ) ->							List[Any]:
        snake_case_    							=				self.num_labels
        snake_case_    							=				FlaxResNetForImageClassification(config=lowerCAmelCase_     )
        snake_case_    							=				model(lowerCAmelCase_     )
        self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_labels)     )


  def 		_lowerCamelCase						(self	:			int     ) ->							List[str]:
        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 			lowerCAmelCase__ (							UpperCamelCase       ,				unittest.TestCase							):



  '''simple docstring'''






  _UpperCamelCase							=			(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

  _UpperCamelCase							=			False
  _UpperCamelCase							=			False
  _UpperCamelCase							=			False

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							None:
        snake_case_    							=				FlaxResNetModelTester(self     )
        snake_case_    							=				ConfigTester(self						,			config_class=lowerCAmelCase_						,			has_text_modality=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							int:
        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 		_lowerCamelCase						(self	:			Dict     ) ->							Optional[int]:
        return

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Dict:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							str:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_     )

  @unittest.skip(reason="ResNet does not use inputs_embeds"     )
  def 		_lowerCamelCase						(self	:			int     ) ->							Optional[int]:
        pass

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

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

  def 		_lowerCamelCase						(self	:			Dict     ) ->							List[Any]:
        def check_hidden_states_output(lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			Optional[int]     ):
              snake_case_    							=				model_class(lowerCAmelCase_     )

              snake_case_    							=				model(**self._prepare_for_class(lowerCAmelCase_						,			lowerCAmelCase_     )     )

              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(lowerCAmelCase_     )						,			expected_num_stages + 1     )

  @unittest.skip(reason="ResNet does not use feedforward chunking"     )
  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Tuple:
        pass


  def 		_lowerCamelCase						(self	:			Any     ) ->							Optional[int]:
        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(lowerCAmelCase_						,			lowerCAmelCase_     )
                    snake_case_    							=				model_class(lowerCAmelCase_     )

                    @jax.jit
                    def model_jitted(lowerCAmelCase_	:			int						,			**lowerCAmelCase_	:			Any     ):
                          return model(pixel_values=lowerCAmelCase_						,			**lowerCAmelCase_     )

                    with self.subTest("JIT Enabled"     ):
                          snake_case_    							=				model_jitted(**lowerCAmelCase_     ).to_tuple()

                    with self.subTest("JIT Disabled"     ):
                          with jax.disable_jit():
                                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 	snake_case_							(				):
      snake_case_    							=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
      return image




@require_flax
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  @cached_property
  def 		_lowerCamelCase						(self	:			int     ) ->							str:
        return AutoImageProcessor.from_pretrained("microsoft/resnet-50"     ) if is_vision_available() else None


  @slow
  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Union[str, Any]:
        snake_case_    							=				FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50"     )

        snake_case_    							=				self.default_image_processor
        snake_case_    							=				prepare_img()
        snake_case_    							=				image_processor(images=lowerCAmelCase_						,			return_tensors="np"     )

        snake_case_    							=				model(**lowerCAmelCase_     )

        # verify the logits
        snake_case_    							=				(1, 10_00)
        self.assertEqual(outputs.logits.shape						,			lowerCAmelCase_     )

        snake_case_    							=				jnp.array([-11.1_069, -9.7_877, -8.3_777]     )

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




lowerCAmelCase__							=			float("inf")






class     __magic_name__     :


 def __init__(    self		:			List[str]      ,    lowerCAmelCase__		:			Dict     )     ->   List[Any]:
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[[] for _ in range(lowerCAmelCase__     )]


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Dict      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Optional[Any]=0     )     ->   Any:
   self.adj[a].append([b, len(self.adj[b]     ), c, 0]     )
   self.adj[b].append([a, len(self.adj[a]     ) - 1, rcap, 0]     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Any     )     ->   List[str]:
   if vertex == sink or not flow:
     return flow

   for i in range(self.ptr[vertex]      ,    len(self.adj[vertex]     )     ):
     UpperCAmelCase          =	self.adj[vertex][i]
     if self.lvl[e[0]] == self.lvl[vertex] + 1:
       UpperCAmelCase          =	self.depth_first_search(e[0]      ,    lowerCAmelCase__      ,    min(lowerCAmelCase__      ,    e[2] - e[3]     )     )
       if p:
         self.adj[vertex][i][3] += p
         self.adj[e[0]][e[1]][3] -= p
         return p
     UpperCAmelCase          =	self.ptr[vertex] + 1
   return 0


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Tuple     )     ->   int:
   UpperCAmelCase   ,						UpperCAmelCase          =	0, source
   for l in range(3_1     ):  # noqa: E741  l = 30 maybe faster for random data
     while True:
       UpperCAmelCase   ,						UpperCAmelCase          =	[0] * len(self.q     ), [0] * len(self.q     )
       UpperCAmelCase   ,						UpperCAmelCase   ,						UpperCAmelCase          =	0, 1, 1
       while qi < qe and not self.lvl[sink]:
         UpperCAmelCase          =	self.q[qi]
         qi += 1
         for e in self.adj[v]:
           if not self.lvl[e[0]] and (e[2] - e[3]) >> (3_0 - l):
             UpperCAmelCase          =	e[0]
             qe += 1
             UpperCAmelCase          =	self.lvl[v] + 1

       UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )
       while p:
         flow += p
         UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )

       if not self.lvl[sink]:
         break

   return flow
lowerCAmelCase__							=			Dinic(10)
lowerCAmelCase__							=			0
lowerCAmelCase__							=			9
for vertex in range(1, 5):
      graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
      graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
      graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))



