'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFResNetForImageClassification, TFResNetModel
       from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor
class   SCREAMING_SNAKE_CASE			:







  """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		,						) ->     Union[str, Any]:
        _lowerCAmelCase										=parent
        _lowerCAmelCase										=batch_size
        _lowerCAmelCase										=image_size
        _lowerCAmelCase										=num_channels
        _lowerCAmelCase										=embeddings_size
        _lowerCAmelCase										=hidden_sizes
        _lowerCAmelCase										=depths
        _lowerCAmelCase										=is_training
        _lowerCAmelCase										=use_labels
        _lowerCAmelCase										=hidden_act
        _lowerCAmelCase										=num_labels
        _lowerCAmelCase										=scope
        _lowerCAmelCase										=len(__A				)






  def 		UpperCamelCase__       (   self				) ->     str:
        _lowerCAmelCase										=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

        _lowerCAmelCase										=self.get_config()

        return config, pixel_values, labels






  def 		UpperCamelCase__       (   self				) ->     int:
        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		,						__A				) ->     Tuple:
        _lowerCAmelCase										=TFResNetModel(config=__A				)
        _lowerCAmelCase										=model(__A				)
        # expected last hidden states: B, C, H // 32, W // 32
        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		,						__A				) ->     Union[str, Any]:
        _lowerCAmelCase										=self.num_labels
        _lowerCAmelCase										=TFResNetForImageClassification(__A				)
        _lowerCAmelCase										=model(__A		,						labels=__A				)
        self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)				)






  def 		UpperCamelCase__       (   self				) ->     List[str]:
        _lowerCAmelCase										=self.prepare_config_and_inputs()
        _lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=config_and_inputs
        _lowerCAmelCase										={'pixel_values': pixel_values}
        return config, inputs_dict

@require_tf
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase    ,			unittest.TestCase):







  """simple docstring"""
  lowercase   :      Optional[int]       =							(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
  lowercase   :      List[Any]       =							(
      {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
      if is_tf_available()
      else {}
  )

  lowercase   :      List[str]       =							False
  lowercase   :      Optional[Any]       =							False
  lowercase   :      List[str]       =							False
  lowercase   :      Union[str, Any]       =							False
  lowercase   :      List[str]       =							False






  def 		UpperCamelCase__       (   self				) ->     Any:
        _lowerCAmelCase										=TFResNetModelTester(self				)
        _lowerCAmelCase										=ConfigTester(self		,						config_class=__A		,						has_text_modality=__A				)






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






  @unittest.skip(reason='ResNet does not use inputs_embeds'				)
  def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
        pass






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






  def 		UpperCamelCase__       (   self				) ->     Any:
        _lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

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

              _lowerCAmelCase										=['pixel_values']
              self.assertListEqual(arg_names[:1]		,						__A				)






  def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
        _lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A				)






  def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
        def check_hidden_states_output(__A		,						__A		,						__A				):
              _lowerCAmelCase										=model_class(__A				)
              _lowerCAmelCase										=model(**self._prepare_for_class(__A		,						__A				)				)

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

              _lowerCAmelCase										=self.model_tester.num_stages
              self.assertEqual(len(__A				)		,						expected_num_stages + 1				)

              # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]				)		,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,						)

        _lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()
        _lowerCAmelCase										=['basic', 'bottleneck']
        for model_class in self.all_model_classes:
              for layer_type in layers_type:
                    _lowerCAmelCase										=layer_type
                    _lowerCAmelCase										=True
                    check_hidden_states_output(__A		,						__A		,						__A				)

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

                    check_hidden_states_output(__A		,						__A		,						__A				)






  def 		UpperCamelCase__       (   self				) ->     List[str]:
        _lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__A				)






  @slow
  def 		UpperCamelCase__       (   self				) ->     str:
        for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowerCAmelCase										=TFResNetModel.from_pretrained(__A				)
              self.assertIsNotNone(__A				)



def 							UpperCamelCase__			(			):


      '''simple docstring'''






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


@require_tf
@require_vision
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







  """simple docstring"""






  @cached_property
  def 		UpperCamelCase__       (   self				) ->     Dict:
        return (
            AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
            if is_vision_available()
            else None
        )






  @slow
  def 		UpperCamelCase__       (   self				) ->     Optional[int]:
        _lowerCAmelCase										=TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

        _lowerCAmelCase										=self.default_image_processor
        _lowerCAmelCase										=prepare_img()
        _lowerCAmelCase										=image_processor(images=__A		,						return_tensors='tf'				)

        # forward pass
        _lowerCAmelCase										=model(**__A				)

        # verify the logits
        _lowerCAmelCase										=tf.TensorShape((1, 1000)				)
        self.assertEqual(outputs.logits.shape		,						__A				)

        _lowerCAmelCase										=tf.constant([-11.1_069, -9.7_877, -8.3_777]				)

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






'''simple docstring'''

from __future__ import annotations

from typing import Any
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A				) ->     None:
								_lowerCAmelCase										=num_of_nodes
								_lowerCAmelCase										=[]
								_lowerCAmelCase										={}






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:
								self.m_edges.append([u_node, v_node, weight]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:

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






		def 		UpperCamelCase__       (   self		,						__A				) ->     None:

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:

								if component_size[u_node] <= component_size[v_node]:
														_lowerCAmelCase										=v_node
														component_size[v_node] += component_size[u_node]
														self.set_component(__A				)

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






		def 		UpperCamelCase__       (   self				) ->     None:
								_lowerCAmelCase										=[]
								_lowerCAmelCase										=0

								_lowerCAmelCase										=[-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				)

								_lowerCAmelCase										=self.m_num_of_nodes

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

																				_lowerCAmelCase										=self.m_component[u]
																				_lowerCAmelCase										=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
																																):
																																						_lowerCAmelCase										=[u, v, w]

														for edge in minimum_weight_edge:
																				if isinstance(__A		,						__A				):
																										_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

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

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

														_lowerCAmelCase										=[-1] * self.m_num_of_nodes
								print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''








if __name__ == "__main__":
							import doctest

							doctest.testmod()





