
"""simple docstring"""

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
      import torch

      from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin

class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase="resnet50"     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	parent
      __UpperCamelCase      =	out_indices if out_indices is not None else [4]
      __UpperCamelCase      =	stage_names
      __UpperCamelCase      =	out_features
      __UpperCamelCase      =	backbone
      __UpperCamelCase      =	batch_size
      __UpperCamelCase      =	image_size
      __UpperCamelCase      =	num_channels
      __UpperCamelCase      =	use_pretrained_backbone
      __UpperCamelCase      =	is_training





     def 						UpperCAmelCase (     self				):

      '''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				):

      '''simple docstring'''




      return TimmBackboneConfig(
          image_size=self.image_size     ,					num_channels=self.num_channels     ,					out_features=self.out_features     ,					out_indices=self.out_indices     ,					stage_names=self.stage_names     ,					use_pretrained_backbone=self.use_pretrained_backbone     ,					backbone=self.backbone     ,					)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	TimmBackbone(config=__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      with torch.no_grad():
       __UpperCamelCase      =	model(__UpperCAmelCase				)
      self.parent.assertEqual(
          result.feature_map[-1].shape     ,					(self.batch_size, model.channels[-1], 14, 14)     ,					)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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


@require_torch
@require_timm
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						(TimmBackbone,) if is_torch_available() else ()
     lowercase						       =						{"feature-extraction": TimmBackbone} if is_torch_available() else {}
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	TimmBackboneModelTester(self				)
      __UpperCamelCase      =	ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      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				):

      '''simple docstring'''




      __UpperCamelCase      =	'resnet18'
      __UpperCamelCase      =	'microsoft/resnet-18'

      __UpperCamelCase      =	AutoBackbone.from_pretrained(__UpperCAmelCase     ,					use_timm_backbone=__UpperCAmelCase				)
      __UpperCamelCase      =	AutoBackbone.from_pretrained(__UpperCAmelCase				)

      self.assertEqual(len(timm_model.out_features				)     ,					len(transformers_model.out_features				)				)
      self.assertEqual(len(timm_model.stage_names				)     ,					len(transformers_model.stage_names				)				)
      self.assertEqual(timm_model.channels     ,					transformers_model.channels				)
      # Out indices are set to the last layer by default. For timm models, we don't know
      # the number of layers in advance, so we set it to (-1,), whereas for transformers
      # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
      self.assertEqual(timm_model.out_indices     ,					(-1,)				)
      self.assertEqual(transformers_model.out_indices     ,					[len(timm_model.stage_names				) - 1]				)

      __UpperCamelCase      =	AutoBackbone.from_pretrained(__UpperCAmelCase     ,					use_timm_backbone=__UpperCAmelCase     ,					out_indices=[1, 2, 3]				)
      __UpperCamelCase      =	AutoBackbone.from_pretrained(__UpperCAmelCase     ,					out_indices=[1, 2, 3]				)

      self.assertEqual(timm_model.out_indices     ,					transformers_model.out_indices				)
      self.assertEqual(len(timm_model.out_features				)     ,					len(transformers_model.out_features				)				)
      self.assertEqual(timm_model.channels     ,					transformers_model.channels				)





     @unittest.skip('TimmBackbone doesn\'t support feed forward chunking'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('TimmBackbone initialization is managed on the timm side'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('model weights aren\'t tied in TimmBackbone.'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('model weights aren\'t tied in TimmBackbone.'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('TimmBackbone doesn\'t support output_attentions.'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('Safetensors is not supported by timm.'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       __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]     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase      =	True
      __UpperCamelCase      =	self.has_attentions

      # no need to test all models as different heads yield the same functionality
      __UpperCamelCase      =	self.all_model_classes[0]
      __UpperCamelCase      =	model_class(__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)

      __UpperCamelCase      =	self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	model(**__UpperCAmelCase				)
      __UpperCamelCase      =	outputs[0][-1]

      # Encoder-/Decoder-only models
      __UpperCamelCase      =	outputs.hidden_states[0]
      hidden_states.retain_grad()

      if self.has_attentions:
       __UpperCamelCase      =	outputs.attentions[0]
       attentions.retain_grad()

      output.flatten()[0].backward(retain_graph=__UpperCAmelCase				)

      self.assertIsNotNone(hidden_states.grad				)

      if self.has_attentions:
       self.assertIsNotNone(attentions.grad				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.eval()
       __UpperCamelCase      =	model(**__UpperCAmelCase				)

       self.assertEqual(len(result.feature_maps				)     ,					len(config.out_indices				)				)
       self.assertEqual(len(model.channels				)     ,					len(config.out_indices				)				)

       # Check output of last stage is taken if out_features=None, out_indices=None
       __UpperCamelCase      =	copy.deepcopy(__UpperCAmelCase				)
       __UpperCamelCase      =	None
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.eval()
       __UpperCamelCase      =	model(**__UpperCAmelCase				)

       self.assertEqual(len(result.feature_maps				)     ,					1				)
       self.assertEqual(len(model.channels				)     ,					1				)

       # Check backbone can be initialized with fresh weights
       __UpperCamelCase      =	copy.deepcopy(__UpperCAmelCase				)
       __UpperCamelCase      =	False
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.eval()
       __UpperCamelCase      =	model(**__UpperCAmelCase				)





"""simple docstring"""

UpperCamelCase     :    Union[str, Any]       =			[
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def 							A						(    snake_case				:Dict    , snake_case				:Tuple    , snake_case				:str    , snake_case				:Optional[int]		)				->     Union[str, Any]:
 # Return True if there is node that has not iterated.
 __UpperCamelCase      =	[False] * len(snake_case		)
 __UpperCamelCase      =	[s]
 __UpperCamelCase      =	True

 while queue:
  __UpperCamelCase      =	queue.pop(0		)
  for ind in range(len(graph[u]		)		):
   if visited[ind] is False and graph[u][ind] > 0:
    queue.append(snake_case		)
    __UpperCamelCase      =	True
    __UpperCamelCase      =	u

 return visited[t]






def 							A						(    snake_case				:int    , snake_case				:Any    , snake_case				:Union[str, Any]		)				->     Optional[int]:
 __UpperCamelCase      =	[-1] * (len(snake_case		))
 __UpperCamelCase      =	0
 __UpperCamelCase      =	[]
 __UpperCamelCase      =	[i[:] for i in graph]  # Record original cut, copy.
 while bfs(snake_case    , snake_case    , snake_case    , snake_case		):
  __UpperCamelCase      =	float('Inf'		)
  __UpperCamelCase      =	sink

  while s != source:
   # Find the minimum value in select path
   __UpperCamelCase      =	min(snake_case    , graph[parent[s]][s]		)
   __UpperCamelCase      =	parent[s]

  max_flow += path_flow
  __UpperCamelCase      =	sink

  while v != source:
   __UpperCamelCase      =	parent[v]
   graph[u][v] -= path_flow
   graph[v][u] += path_flow
   __UpperCamelCase      =	parent[v]

 for i in range(len(snake_case		)		):
  for j in range(len(graph[0]		)		):
   if graph[i][j] == 0 and temp[i][j] > 0:
    res.append((i, j)		)

 return res


if __name__ == "__main__":
      print(mincut(test_graph, source=0, sink=5))



