

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     lowercase						(       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       ,       ) ->					List[Any]:






  """simple docstring"""





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




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






  """simple docstring"""





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

  UpperCAmelCase__							=				self.get_config()

  return config, pixel_values




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






  """simple docstring"""





  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   UpperCamelCase__							(self       ,       __a       ,       __a ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				FlaxResNetModel(config=__a )
  UpperCAmelCase__							=				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   UpperCamelCase__							(self       ,       __a       ,       __a ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				self.num_labels
  UpperCAmelCase__							=				FlaxResNetForImageClassification(config=__a )
  UpperCAmelCase__							=				model(__a )
  self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.num_labels) )




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






  """simple docstring"""





  UpperCAmelCase__							=				self.prepare_config_and_inputs()
  UpperCAmelCase__				,    UpperCAmelCase__							=				config_and_inputs
  UpperCAmelCase__							=				{'pixel_values': pixel_values}
  return config, inputs_dict







@require_flax
class     lowercase						(       _UpperCamelCase ,				unittest.TestCase					):



 '''simple docstring'''


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

 __SCREAMING_SNAKE_CASE												=				False
 __SCREAMING_SNAKE_CASE												=				False
 __SCREAMING_SNAKE_CASE												=				False




 def   UpperCamelCase__							(self ) ->					None:






  """simple docstring"""





  UpperCAmelCase__							=				FlaxResNetModelTester(self )
  UpperCAmelCase__							=				ConfigTester(self       ,       config_class=__a       ,       has_text_modality=__a )




 def   UpperCamelCase__							(self ) ->					Tuple:






  """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   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  return




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






  """simple docstring"""





  UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*__a )




 def   UpperCamelCase__							(self ) ->					int:






  """simple docstring"""





  UpperCAmelCase__							=				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   UpperCamelCase__							(self ) ->					int:






  """simple docstring"""





  pass




 @unittest.skip(reason='ResNet does not support input and output embeddings' )
 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  pass




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






  """simple docstring"""





  UpperCAmelCase__				,    UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   UpperCAmelCase__							=				model_class(__a )
   UpperCAmelCase__							=				inspect.signature(model.__call__ )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   UpperCAmelCase__							=				[*signature.parameters.keys()]

   UpperCAmelCase__							=				['pixel_values']
   self.assertListEqual(arg_names[:1]       ,       __a )




 def   UpperCamelCase__							(self ) ->					int:






  """simple docstring"""





  def check_hidden_states_output(__a       ,       __a       ,       __a ):
   UpperCAmelCase__							=				model_class(__a )

   UpperCAmelCase__							=				model(**self._prepare_for_class(__a       ,       __a ) )

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

   UpperCAmelCase__							=				self.model_tester.num_stages
   self.assertEqual(len(__a )       ,       expected_num_stages + 1 )




 @unittest.skip(reason='ResNet does not use feedforward chunking' )
 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  pass




 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__				,    UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__ ):
    UpperCAmelCase__							=				self._prepare_for_class(__a       ,       __a )
    UpperCAmelCase__							=				model_class(__a )

    @jax.jit
    def model_jitted(__a       ,       **__a ):
     return model(pixel_values=__a       ,       **__a )

    with self.subTest('JIT Enabled' ):
     UpperCAmelCase__							=				model_jitted(**__a ).to_tuple()

    with self.subTest('JIT Disabled' ):
     with jax.disable_jit():
      UpperCAmelCase__							=				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 						UpperCamelCase_(			)       -> Optional[int]:
 UpperCAmelCase__							=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
 return image







@require_flax
class     lowercase						(       unittest.TestCase					):



 '''simple docstring'''


 @cached_property
 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  return AutoImageProcessor.from_pretrained('microsoft/resnet-50' ) if is_vision_available() else None




 @slow
 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50' )

  UpperCAmelCase__							=				self.default_image_processor
  UpperCAmelCase__							=				prepare_img()
  UpperCAmelCase__							=				image_processor(images=__a       ,       return_tensors='np' )

  UpperCAmelCase__							=				model(**__a )

  # verify the logits
  UpperCAmelCase__							=				(1, 1000)
  self.assertEqual(outputs.logits.shape       ,       __a )

  UpperCAmelCase__							=				jnp.array([-11.10_69, -9.78_77, -8.37_77] )

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






from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
_UpperCamelCase			       =			{
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}
def 						UpperCamelCase_(			snake_case__:  dict[str, list[str]]   )       -> set[tuple[str, str]]:
 UpperCAmelCase__							=				{node: {node} for node in graph}

 UpperCAmelCase__							=				{node: graph[node][:] for node in graph}

 while len(snake_case__   ) > 2:
  # Choose a random edge.
  UpperCAmelCase__							=				random.choice(list(graph_copy.keys()   )   )
  UpperCAmelCase__							=				random.choice(graph_copy[u]   )

  # Contract edge (u, v) to new node uv
  UpperCAmelCase__							=				u + v
  UpperCAmelCase__							=				list(set(graph_copy[u] + graph_copy[v]   )   )
  uv_neighbors.remove(snake_case__   )
  uv_neighbors.remove(snake_case__   )
  UpperCAmelCase__							=				uv_neighbors
  for neighbor in uv_neighbors:
   graph_copy[neighbor].append(snake_case__   )

  UpperCAmelCase__							=				set(contracted_nodes[u].union(contracted_nodes[v]   )   )

  # Remove nodes u and v.
  del graph_copy[u]
  del graph_copy[v]
  for neighbor in uv_neighbors:
   if u in graph_copy[neighbor]:
    graph_copy[neighbor].remove(snake_case__   )
   if v in graph_copy[neighbor]:
    graph_copy[neighbor].remove(snake_case__   )

    # Find cutset.
 UpperCAmelCase__							=				[contracted_nodes[node] for node in graph_copy]
 return {
     (node, neighbor)
     for node in groups[0]
     for neighbor in graph[node]
     if neighbor in groups[1]
 }


if __name__ == "__main__":
 print(partition_graph(TEST_GRAPH))



