

import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
  from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =self.config_class(**self.inputs_dict      )
        self.parent.assertTrue(hasattr(lowerCAmelCase							,     'tf_padding'      )      )
        self.parent.assertTrue(hasattr(lowerCAmelCase							,     'depth_multiplier'      )      )





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase=13							,     lowerCAmelCase=3							,     lowerCAmelCase=32							,     lowerCAmelCase=0.25							,     lowerCAmelCase=8							,     lowerCAmelCase=8							,     lowerCAmelCase=6							,     lowerCAmelCase=32							,     lowerCAmelCase=True							,     lowerCAmelCase=True							,     lowerCAmelCase=True							,     lowerCAmelCase="relu6"							,     lowerCAmelCase=1_280							,     lowerCAmelCase=0.1							,     lowerCAmelCase=0.02							,     lowerCAmelCase=True							,     lowerCAmelCase=True							,     lowerCAmelCase=10							,     lowerCAmelCase=None							,     )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =parent
        _lowercase			 =batch_size
        _lowercase			 =num_channels
        _lowercase			 =image_size
        _lowercase			 =depth_multiplier
        _lowercase			 =depth_divisible_by
        _lowercase			 =min_depth
        _lowercase			 =expand_ratio
        _lowercase			 =tf_padding
        _lowercase			 =output_stride
        _lowercase			 =first_layer_is_expansion
        _lowercase			 =finegrained_output
        _lowercase			 =hidden_act
        _lowercase			 =last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier      )
        _lowercase			 =classifier_dropout_prob
        _lowercase			 =use_labels
        _lowercase			 =is_training
        _lowercase			 =num_labels
        _lowercase			 =initializer_range
        _lowercase			 =scope




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






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

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

        _lowercase			 =self.get_config()

        return config, pixel_values, labels, pixel_labels




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        return MobileNetVaConfig(
            num_channels=self.num_channels							,     image_size=self.image_size							,     depth_multiplier=self.depth_multiplier							,     depth_divisible_by=self.depth_divisible_by							,     min_depth=self.min_depth							,     expand_ratio=self.expand_ratio							,     output_stride=self.output_stride							,     first_layer_is_expansion=self.first_layer_is_expansion							,     finegrained_output=self.finegrained_output							,     hidden_act=self.hidden_act							,     tf_padding=self.tf_padding							,     classifier_dropout_prob=self.classifier_dropout_prob							,     initializer_range=self.initializer_range							,     )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				str:
        '''simple docstring'''






        _lowercase			 =MobileNetVaModel(config=lowerCAmelCase      )
        model.to(lowerCAmelCase      )
        model.eval()
        _lowercase			 =model(lowerCAmelCase      )
        self.parent.assertEqual(
            result.last_hidden_state.shape							,     (
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )							,     )
        self.parent.assertEqual(
            result.pooler_output.shape							,     (self.batch_size, self.last_hidden_size)							,     )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =self.num_labels
        _lowercase			 =MobileNetVaForImageClassification(lowerCAmelCase      )
        model.to(lowerCAmelCase      )
        model.eval()
        _lowercase			 =model(lowerCAmelCase							,     labels=lowerCAmelCase      )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.num_labels)      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =self.num_labels
        _lowercase			 =MobileNetVaForSemanticSegmentation(lowerCAmelCase      )
        model.to(lowerCAmelCase      )
        model.eval()
        _lowercase			 =model(lowerCAmelCase      )
        self.parent.assertEqual(
            result.logits.shape							,     (
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )							,     )
        _lowercase			 =model(lowerCAmelCase							,     labels=lowerCAmelCase      )
        self.parent.assertEqual(
            result.logits.shape							,     (
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )							,     )




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






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





@require_torch
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						(
      (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  _a		    =						(
      {
          """feature-extraction""": MobileNetVaModel,
          """image-classification""": MobileNetVaForImageClassification,
          """image-segmentation""": MobileNetVaForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  _a		    =						False
  _a		    =						False
  _a		    =						False
  _a		    =						False




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        _lowercase			 =MobileNetVaModelTester(self      )
        _lowercase			 =MobileNetVaConfigTester(self							,     config_class=lowerCAmelCase							,     has_text_modality=lowerCAmelCase      )




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        self.config_tester.run_common_tests()




  @unittest.skip(reason='MobileNetV2 does not use inputs_embeds'      )
  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        pass




  @unittest.skip(reason='MobileNetV2 does not support input and output embeddings'      )
  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        pass




  @unittest.skip(reason='MobileNetV2 does not output attentions'      )
  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        pass




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase , _lowercase			 =self.model_tester.prepare_config_and_inputs_for_common()

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

              _lowercase			 =['pixel_values']
              self.assertListEqual(arg_names[:1]							,     lowerCAmelCase      )




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase      )




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        def check_hidden_states_output(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      ):
              _lowercase			 =model_class(lowerCAmelCase      )
              model.to(lowerCAmelCase      )
              model.eval()

              with torch.no_grad():
                    _lowercase			 =model(**self._prepare_for_class(lowerCAmelCase							,     lowerCAmelCase      )      )

              _lowercase			 =outputs.hidden_states

              _lowercase			 =16
              self.assertEqual(len(lowerCAmelCase      )							,     lowerCAmelCase      )

        _lowercase , _lowercase			 =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _lowercase			 =True
              check_hidden_states_output(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )

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

              check_hidden_states_output(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase      )




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase      )




  @slow
  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowercase			 =MobileNetVaModel.from_pretrained(lowerCAmelCase      )
              self.assertIsNotNone(lowerCAmelCase      )
def   a   (    )					->   Optional[Any]:





      """simple docstring"""



      _lowercase			 =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      return image







@require_torch
@require_vision
class 				__lowerCAmelCase  (      unittest.TestCase							):




  @cached_property
  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        return (
            MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'      ) if is_vision_available() else None
        )




  @slow
  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'      ).to(lowerCAmelCase      )

        _lowercase			 =self.default_image_processor
        _lowercase			 =prepare_img()
        _lowercase			 =image_processor(images=lowerCAmelCase							,     return_tensors='pt'      ).to(lowerCAmelCase      )

        # forward pass
        with torch.no_grad():
              _lowercase			 =model(**lowerCAmelCase      )

        # verify the logits
        _lowercase			 =torch.Size((1, 1_001)      )
        self.assertEqual(outputs.logits.shape							,     lowerCAmelCase      )

        _lowercase			 =torch.tensor([0.2445, -1.1993, 0.1905]      ).to(lowerCAmelCase      )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]							,     lowerCAmelCase							,     atol=1e-4      )      )




  @slow
  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'      )
        _lowercase			 =model.to(lowerCAmelCase      )

        _lowercase			 =MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'      )

        _lowercase			 =prepare_img()
        _lowercase			 =image_processor(images=lowerCAmelCase							,     return_tensors='pt'      ).to(lowerCAmelCase      )

        # forward pass
        with torch.no_grad():
              _lowercase			 =model(**lowerCAmelCase      )
        _lowercase			 =outputs.logits

        # verify the logits
        _lowercase			 =torch.Size((1, 21, 65, 65)      )
        self.assertEqual(logits.shape							,     lowerCAmelCase      )

        _lowercase			 =torch.tensor(
            [
                [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
            ]							,     device=lowerCAmelCase							,     )

        self.assertTrue(torch.allclose(logits[0, :3, :3, :3]							,     lowerCAmelCase							,     atol=1e-4      )      )





from __future__ import annotations

from typing import Any





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        _lowercase			 =num_of_nodes
        _lowercase			 =[]
        _lowercase			 ={}




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        self.m_edges.append([u_node, v_node, weight]      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        if self.m_component[u_node] == u_node:
              return u_node
        return self.find_component(self.m_component[u_node]      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if self.m_component[u_node] != u_node:
              for k in self.m_component:
                    _lowercase			 =self.find_component(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if component_size[u_node] <= component_size[v_node]:
              _lowercase			 =v_node
              component_size[v_node] += component_size[u_node]
              self.set_component(lowerCAmelCase      )

        elif component_size[u_node] >= component_size[v_node]:
              _lowercase			 =self.find_component(lowerCAmelCase      )
              component_size[u_node] += component_size[v_node]
              self.set_component(lowerCAmelCase      )




  def 						A__  (       self      )						->				None:
        '''simple docstring'''






        _lowercase			 =[]
        _lowercase			 =0

        _lowercase			 =[-1] * self.m_num_of_nodes

        # A list of components (initialized to all of the nodes)
        for node in range(self.m_num_of_nodes      ):
              self.m_component.update({node: node}      )
              component_size.append(1      )

        _lowercase			 =self.m_num_of_nodes

        while num_of_components > 1:
              for edge in self.m_edges:
                    _lowercase , _lowercase , _lowercase			 =edge

                    _lowercase			 =self.m_component[u]
                    _lowercase			 =self.m_component[v]

                    if u_component != v_component:

                          for component in (u_component, v_component):
                                if (
                                    minimum_weight_edge[component] == -1
                                    or minimum_weight_edge[component][2] > w
                                ):
                                      _lowercase			 =[u, v, w]

              for edge in minimum_weight_edge:
                    if isinstance(lowerCAmelCase							,     lowerCAmelCase      ):
                          _lowercase , _lowercase , _lowercase			 =edge

                          _lowercase			 =self.m_component[u]
                          _lowercase			 =self.m_component[v]

                          if u_component != v_component:
                                mst_weight += w
                                self.union(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )
                                print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''      )
                                num_of_components -= 1

              _lowercase			 =[-1] * self.m_num_of_nodes
        print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''      )
def   a   (    )					->   None:





      """simple docstring"""





if __name__ == "__main__":
  import doctest

  doctest.testmod()


