

"""simple docstring"""


import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import YolosForObjectDetection, YolosModel
  from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor





class lowerCamelCase__   :



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_=1_3 ,lowerCamelCase_=[3_0, 3_0] ,lowerCamelCase_=2 ,lowerCamelCase_=3 ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=3_2 ,lowerCamelCase_=5 ,lowerCamelCase_=4 ,lowerCamelCase_=3_7 ,lowerCamelCase_="gelu" ,lowerCamelCase_=0.1 ,lowerCamelCase_=0.1 ,lowerCamelCase_=1_0 ,lowerCamelCase_=0.02 ,lowerCamelCase_=3 ,lowerCamelCase_=None ,lowerCamelCase_=8 ,lowerCamelCase_=1_0 ,)    ->      str:
          A				  =		parent
          A				  =		batch_size
          A				  =		image_size
          A				  =		patch_size
          A				  =		num_channels
          A				  =		is_training
          A				  =		use_labels
          A				  =		hidden_size
          A				  =		num_hidden_layers
          A				  =		num_attention_heads
          A				  =		intermediate_size
          A				  =		hidden_act
          A				  =		hidden_dropout_prob
          A				  =		attention_probs_dropout_prob
          A				  =		type_sequence_label_size
          A				  =		initializer_range
          A				  =		num_labels
          A				  =		scope
          A				  =		n_targets
          A				  =		num_detection_tokens
          # we set the expected sequence length (which is used in several tests)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
          A				  =		(image_size[1] // patch_size) * (image_size[0] // patch_size)
          A				  =		num_patches + 1 + self.num_detection_tokens




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]    )

          A				  =		None
          if self.use_labels:
                # labels is a list of Dict (each Dict being the labels for a given example in the batch)
                A				  =		[]
                for i in range(self.batch_size    ):
                      A				  =		{}
                      A				  =		torch.randint(
                          high=self.num_labels ,size=(self.n_targets,) ,device=lowerCamelCase_    )
                      A				  =		torch.rand(self.n_targets ,4 ,device=lowerCamelCase_    )
                      labels.append(lowerCamelCase_    )

          A				  =		self.get_config()

          return config, pixel_values, labels




    def UpperCamelCase__		( self    )    ->      Dict:
          return YolosConfig(
              image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=lowerCamelCase_ ,initializer_range=self.initializer_range ,num_detection_tokens=self.num_detection_tokens ,num_labels=self.num_labels ,)




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )    ->      int:
          A				  =		YolosModel(config=lowerCamelCase_    )
          model.to(lowerCamelCase_    )
          model.eval()
          A				  =		model(lowerCamelCase_    )
          self.parent.assertEqual(
              result.last_hidden_state.shape ,(self.batch_size, self.expected_seq_len, self.hidden_size)    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )    ->      Optional[int]:
          A				  =		YolosForObjectDetection(lowerCamelCase_    )
          model.to(lowerCamelCase_    )
          model.eval()

          A				  =		model(pixel_values=lowerCamelCase_    )
          A				  =		model(lowerCamelCase_    )

          self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_detection_tokens, self.num_labels + 1)    )
          self.parent.assertEqual(result.pred_boxes.shape ,(self.batch_size, self.num_detection_tokens, 4)    )

          A				  =		model(pixel_values=lowerCamelCase_ ,labels=lowerCamelCase_    )

          self.parent.assertEqual(result.loss.shape ,()    )
          self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_detection_tokens, self.num_labels + 1)    )
          self.parent.assertEqual(result.pred_boxes.shape ,(self.batch_size, self.num_detection_tokens, 4)    )




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		self.prepare_config_and_inputs()
          A  ,			A  ,			A				  =		config_and_inputs
          A				  =		{"""pixel_values""": pixel_values}
          return config, inputs_dict





@require_torch
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
    _lowerCamelCase					=   (
        {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
    )

    _lowerCamelCase					=   False
    _lowerCamelCase					=   False
    _lowerCamelCase					=   False
    _lowerCamelCase					=   False




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_=False    )    ->      Tuple:
          A				  =		super()._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_ ,return_labels=lowerCamelCase_    )

          if return_labels:
                if model_class.__name__ == "YolosForObjectDetection":
                      A				  =		[]
                      for i in range(self.model_tester.batch_size    ):
                            A				  =		{}
                            A				  =		torch.ones(
                                size=(self.model_tester.n_targets,) ,device=lowerCamelCase_ ,dtype=torch.long    )
                            A				  =		torch.ones(
                                self.model_tester.n_targets ,4 ,device=lowerCamelCase_ ,dtype=torch.float    )
                            labels.append(lowerCamelCase_    )
                      A				  =		labels

          return inputs_dict




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		YolosModelTester(self    )
          A				  =		ConfigTester(self ,config_class=lowerCamelCase_ ,has_text_modality=lowerCamelCase_ ,hidden_size=3_7    )




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




    def UpperCamelCase__		( self    )    ->      Any:
          # YOLOS does not use inputs_embeds
          pass




    def UpperCamelCase__		( self    )    ->      str:
          A  ,			A				  =		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                A				  =		model_class(lowerCamelCase_    )
                self.assertIsInstance(model.get_input_embeddings() ,(nn.Module)    )
                A				  =		model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(lowerCamelCase_ ,nn.Linear    )    )




    def UpperCamelCase__		( self    )    ->      Tuple:
          A  ,			A				  =		self.model_tester.prepare_config_and_inputs_for_common()

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

                A				  =		["""pixel_values"""]
                self.assertListEqual(arg_names[:1] ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      Union[str, Any]:
          A				  =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A  ,			A				  =		self.model_tester.prepare_config_and_inputs_for_common()
          A				  =		True

          # in YOLOS, the seq_len is different
          A				  =		self.model_tester.expected_seq_len
          for model_class in self.all_model_classes:
                A				  =		True
                A				  =		False
                A				  =		True
                A				  =		model_class(lowerCamelCase_    )
                model.to(lowerCamelCase_    )
                model.eval()
                with torch.no_grad():
                      A				  =		model(**self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_    )    )
                A				  =		outputs.attentions
                self.assertEqual(len(lowerCamelCase_    ) ,self.model_tester.num_hidden_layers    )

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                A				  =		True
                A				  =		model_class(lowerCamelCase_    )
                model.to(lowerCamelCase_    )
                model.eval()
                with torch.no_grad():
                      A				  =		model(**self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_    )    )
                A				  =		outputs.attentions
                self.assertEqual(len(lowerCamelCase_    ) ,self.model_tester.num_hidden_layers    )

                self.assertListEqual(
                    list(attentions[0].shape[-3:]    ) ,[self.model_tester.num_attention_heads, seq_len, seq_len] ,)
                A				  =		len(lowerCamelCase_    )

                # Check attention is always last and order is fine
                A				  =		True
                A				  =		True
                A				  =		model_class(lowerCamelCase_    )
                model.to(lowerCamelCase_    )
                model.eval()
                with torch.no_grad():
                      A				  =		model(**self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_    )    )

                A				  =		1
                self.assertEqual(out_len + added_hidden_states ,len(lowerCamelCase_    )    )

                A				  =		outputs.attentions

                self.assertEqual(len(lowerCamelCase_    ) ,self.model_tester.num_hidden_layers    )
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]    ) ,[self.model_tester.num_attention_heads, seq_len, seq_len] ,)




    def UpperCamelCase__		( self    )    ->      str:
          def check_hidden_states_output(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    ):
                A				  =		model_class(lowerCamelCase_    )
                model.to(lowerCamelCase_    )
                model.eval()

                with torch.no_grad():
                      A				  =		model(**self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_    )    )

                A				  =		outputs.hidden_states

                A				  =		getattr(
                    self.model_tester ,"""expected_num_hidden_layers""" ,self.model_tester.num_hidden_layers + 1    )
                self.assertEqual(len(lowerCamelCase_    ) ,lowerCamelCase_    )

                # YOLOS has a different seq_length
                A				  =		self.model_tester.expected_seq_len

                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]    ) ,[seq_length, self.model_tester.hidden_size] ,)

          A  ,			A				  =		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                A				  =		True
                check_hidden_states_output(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )

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

                check_hidden_states_output(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_object_detection(*lowerCamelCase_    )




    @slow
    def UpperCamelCase__		( self    )    ->      List[Any]:
          for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                A				  =		YolosModel.from_pretrained(lowerCamelCase_    )
                self.assertIsNotNone(lowerCamelCase_    )




def   _A					(	):



      """simple docstring"""






      A				  =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
      return image





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



    '''simple docstring'''




    @cached_property
    def UpperCamelCase__		( self    )    ->      Union[str, Any]:
          return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""    ) if is_vision_available() else None




    @slow
    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""    ).to(lowerCamelCase_    )

          A				  =		self.default_image_processor
          A				  =		prepare_img()
          A				  =		image_processor(images=lowerCamelCase_ ,return_tensors="""pt"""    ).to(lowerCamelCase_    )

          # forward pass
          with torch.no_grad():
                A				  =		model(inputs.pixel_values    )

          # verify outputs
          A				  =		torch.Size((1, 1_0_0, 9_2)    )
          self.assertEqual(outputs.logits.shape ,lowerCamelCase_    )

          A				  =		torch.tensor(
              [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]] ,device=lowerCamelCase_ ,)
          A				  =		torch.tensor(
              [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]] ,device=lowerCamelCase_    )
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,lowerCamelCase_ ,atol=1E-4    )    )
          self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] ,lowerCamelCase_ ,atol=1E-4    )    )

          # verify postprocessing
          A				  =		image_processor.post_process_object_detection(
              lowerCamelCase_ ,threshold=0.3 ,target_sizes=[image.size[::-1]]    )[0]
          A				  =		torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]    ).to(lowerCamelCase_    )
          A				  =		[7_5, 7_5, 1_7, 6_3, 1_7]
          A				  =		torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95]    ).to(lowerCamelCase_    )

          self.assertEqual(len(results["""scores"""]    ) ,5    )
          self.assertTrue(torch.allclose(results["""scores"""] ,lowerCamelCase_ ,atol=1E-4    )    )
          self.assertSequenceEqual(results["""labels"""].tolist() ,lowerCamelCase_    )
          self.assertTrue(torch.allclose(results["""boxes"""][0, :] ,lowerCamelCase_    )    )







"""simple docstring"""


class lowerCamelCase__   :



    '''simple docstring'''




    def __init__( self    )    ->      Union[str, Any]:
          A				  =		{}




    def UpperCamelCase__		( self    )    ->      None:
          print(self.vertex    )
          for i in self.vertex:
                print(lowerCamelCase_ ,""" -> """ ,""" -> """.join([str(lowerCamelCase_    ) for j in self.vertex[i]]    )    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      None:
          # check if vertex is already present,
          if from_vertex in self.vertex:
                self.vertex[from_vertex].append(lowerCamelCase_    )
          else:
                # else make a new vertex
                A				  =		[to_vertex]




    def UpperCamelCase__		( self    )    ->      None:
          # visited array for storing already visited nodes
          A				  =		[False] * len(self.vertex    )

          # call the recursive helper function
          for i in range(len(self.vertex    )    ):
                if not visited[i]:
                      self.dfs_recursive(lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      None:
          # mark start vertex as visited
          A				  =		True

          print(lowerCamelCase_ ,end=""" """    )

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
                if not visited[i]:
                      self.dfs_recursive(lowerCamelCase_ ,lowerCamelCase_    )


if __name__ == "__main__":
  UpperCAmelCase													=Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("DFS:")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3




