

import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
 from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor



class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				self.config_class(**self.inputs_dict )
  self.parent.assertTrue(hasattr(__a       ,       'hidden_sizes' ) )
  self.parent.assertTrue(hasattr(__a       ,       'num_encoder_blocks' ) )







class     lowercase						:



 '''simple docstring'''


 def __init__(self       ,       __a       ,       __a=13       ,       __a=64       ,       __a=3       ,       __a=4       ,       __a=[2, 2, 2, 2]       ,       __a=[8, 4, 2, 1]       ,       __a=[16, 32, 64, 128]       ,       __a=[1, 4, 8, 16]       ,       __a=False       ,       __a=True       ,       __a="gelu"       ,       __a=0.1       ,       __a=0.02       ,       __a=3       ,       __a=None       ,       ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				parent
  UpperCAmelCase__							=				batch_size
  UpperCAmelCase__							=				image_size
  UpperCAmelCase__							=				num_channels
  UpperCAmelCase__							=				num_encoder_blocks
  UpperCAmelCase__							=				sr_ratios
  UpperCAmelCase__							=				depths
  UpperCAmelCase__							=				hidden_sizes
  UpperCAmelCase__							=				downsampling_rates
  UpperCAmelCase__							=				is_training
  UpperCAmelCase__							=				use_labels
  UpperCAmelCase__							=				hidden_act
  UpperCAmelCase__							=				hidden_dropout_prob
  UpperCAmelCase__							=				initializer_range
  UpperCAmelCase__							=				num_labels
  UpperCAmelCase__							=				scope




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






  """simple docstring"""





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

  UpperCAmelCase__							=				None
  if self.use_labels:
   UpperCAmelCase__							=				ids_tensor([self.batch_size, self.image_size, self.image_size]       ,       self.num_labels )

  UpperCAmelCase__							=				PoolFormerConfig(
      image_size=self.image_size       ,       num_channels=self.num_channels       ,       num_encoder_blocks=self.num_encoder_blocks       ,       depths=self.depths       ,       hidden_sizes=self.hidden_sizes       ,       hidden_act=self.hidden_act       ,       hidden_dropout_prob=self.hidden_dropout_prob       ,       initializer_range=self.initializer_range       ,       )

  return config, pixel_values, labels




 def   UpperCamelCase__							(self       ,       __a       ,       __a       ,       __a ) ->					Tuple:






  """simple docstring"""





  UpperCAmelCase__							=				PoolFormerModel(config=__a )
  model.to(__a )
  model.eval()
  UpperCAmelCase__							=				model(__a )
  UpperCAmelCase__							=				UpperCAmelCase__							=				self.image_size // 32.0
  self.parent.assertEqual(
      result.last_hidden_state.shape       ,       (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )




 def   UpperCamelCase__							(self ) ->					Tuple:






  """simple docstring"""





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







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



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE												=				(
     {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
     if is_torch_available()
     else {}
 )

 __SCREAMING_SNAKE_CASE												=				False
 __SCREAMING_SNAKE_CASE												=				False
 __SCREAMING_SNAKE_CASE												=				False
 __SCREAMING_SNAKE_CASE												=				False
 __SCREAMING_SNAKE_CASE												=				False




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






  """simple docstring"""





  UpperCAmelCase__							=				PoolFormerModelTester(self )
  UpperCAmelCase__							=				PoolFormerConfigTester(self       ,       config_class=__a )




 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  self.config_tester.run_common_tests()




 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





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




 @unittest.skip('PoolFormer does not use inputs_embeds' )
 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  pass




 @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods' )
 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  pass




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  def check_hidden_states_output(__a       ,       __a       ,       __a ):
   UpperCAmelCase__							=				model_class(__a )
   model.to(__a )
   model.eval()

   with torch.no_grad():
    UpperCAmelCase__							=				model(**self._prepare_for_class(__a       ,       __a ) )

   UpperCAmelCase__							=				outputs.hidden_states

   UpperCAmelCase__							=				self.model_tester.num_encoder_blocks
   self.assertEqual(len(__a )       ,       __a )

   # verify the first hidden states (first block)
   self.assertListEqual(
       list(hidden_states[0].shape[-3:] )       ,       [
           self.model_tester.hidden_sizes[0],
           self.model_tester.image_size // 4,
           self.model_tester.image_size // 4,
       ]       ,       )

  UpperCAmelCase__				,    UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   UpperCAmelCase__							=				True
   check_hidden_states_output(__a       ,       __a       ,       __a )

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

   check_hidden_states_output(__a       ,       __a       ,       __a )




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






  """simple docstring"""





  if not self.model_tester.is_training:
   return

  UpperCAmelCase__				,    UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs_for_common()
  UpperCAmelCase__							=				True

  for model_class in self.all_model_classes:
   if model_class in get_values(__a ):
    continue
   UpperCAmelCase__							=				model_class(__a )
   model.to(__a )
   model.train()
   UpperCAmelCase__							=				self._prepare_for_class(__a       ,       __a       ,       return_labels=__a )
   UpperCAmelCase__							=				model(**__a ).loss
   loss.backward()




 def   UpperCamelCase__							(self ) ->					List[str]:






  """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.forward )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   UpperCAmelCase__							=				[*signature.parameters.keys()]

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




 @slow
 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   UpperCAmelCase__							=				PoolFormerModel.from_pretrained(__a )
   self.assertIsNotNone(__a )







def 						UpperCamelCase_(			)       -> Optional[int]:
 UpperCAmelCase__							=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
 return image







@require_torch
class     lowercase						(       unittest.TestCase					):



 '''simple docstring'''


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






  """simple docstring"""





  UpperCAmelCase__							=				PoolFormerImageProcessor()
  UpperCAmelCase__							=				PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12' ).to(__a )

  UpperCAmelCase__							=				image_processor(images=prepare_img()       ,       return_tensors='pt' ).to(__a )

  # forward pass
  with torch.no_grad():
   UpperCAmelCase__							=				model(**__a )

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

  UpperCAmelCase__							=				torch.tensor([-0.61_13, 0.16_85, -0.04_92] ).to(__a )
  self.assertTrue(torch.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))



