
'''simple docstring'''



import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         MODEL_MAPPING,
         DataaVecVisionForImageClassification,
         DataaVecVisionForSemanticSegmentation,
         DataaVecVisionModel,
     )
     from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import BeitImageProcessor

class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__=1_00    ,							A__=13    ,							A__=30    ,							A__=2    ,							A__=3    ,							A__=True    ,							A__=True    ,							A__=32    ,							A__=4    ,							A__=4    ,							A__=37    ,							A__="gelu"    ,							A__=0.1    ,							A__=0.1    ,							A__=10    ,							A__=0.02    ,							A__=3    ,							A__=None    ,							A__=[0, 1, 2, 3]    ,							)      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			parent
          _SCREAMING_SNAKE_CASE       	=			1_00
          _SCREAMING_SNAKE_CASE       	=			batch_size
          _SCREAMING_SNAKE_CASE       	=			image_size
          _SCREAMING_SNAKE_CASE       	=			patch_size
          _SCREAMING_SNAKE_CASE       	=			num_channels
          _SCREAMING_SNAKE_CASE       	=			is_training
          _SCREAMING_SNAKE_CASE       	=			use_labels
          _SCREAMING_SNAKE_CASE       	=			hidden_size
          _SCREAMING_SNAKE_CASE       	=			num_hidden_layers
          _SCREAMING_SNAKE_CASE       	=			num_attention_heads
          _SCREAMING_SNAKE_CASE       	=			intermediate_size
          _SCREAMING_SNAKE_CASE       	=			hidden_act
          _SCREAMING_SNAKE_CASE       	=			hidden_dropout_prob
          _SCREAMING_SNAKE_CASE       	=			attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE       	=			type_sequence_label_size
          _SCREAMING_SNAKE_CASE       	=			initializer_range
          _SCREAMING_SNAKE_CASE       	=			scope
          _SCREAMING_SNAKE_CASE       	=			out_indices
          _SCREAMING_SNAKE_CASE       	=			num_labels

          # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          _SCREAMING_SNAKE_CASE       	=			(image_size // patch_size) ** 2
          _SCREAMING_SNAKE_CASE       	=			num_patches + 1






       def     UpperCamelCase			(							self )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

          _SCREAMING_SNAKE_CASE       	=			self.get_config()

          return config, pixel_values, labels, pixel_labels






       def     UpperCamelCase			(							self )      ->     Tuple:
          return DataaVecVisionConfig(
              vocab_size=self.vocab_size    ,							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=A__    ,							initializer_range=self.initializer_range    ,							out_indices=self.out_indices    ,							)






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			DataaVecVisionModel(config=A__ )
          model.to(A__ )
          model.eval()
          _SCREAMING_SNAKE_CASE       	=			model(A__ )
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
          _SCREAMING_SNAKE_CASE       	=			(self.image_size // self.patch_size) ** 2
          self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, num_patches + 1, self.hidden_size) )






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			self.type_sequence_label_size
          _SCREAMING_SNAKE_CASE       	=			DataaVecVisionForImageClassification(A__ )
          model.to(A__ )
          model.eval()
          _SCREAMING_SNAKE_CASE       	=			model(A__    ,							labels=A__ )
          self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.type_sequence_label_size) )






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.num_labels
          _SCREAMING_SNAKE_CASE       	=			DataaVecVisionForSemanticSegmentation(A__ )
          model.to(A__ )
          model.eval()
          _SCREAMING_SNAKE_CASE       	=			model(A__ )
          self.parent.assertEqual(
              result.logits.shape    ,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
          _SCREAMING_SNAKE_CASE       	=			model(A__    ,							labels=A__ )
          self.parent.assertEqual(
              result.logits.shape    ,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )






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






@require_torch
class 				_a			(_lowerCamelCase					,   _lowerCamelCase					,   unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					(
           (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       SCREAMING_SNAKE_CASE =					(
           {
               'feature-extraction': DataaVecVisionModel,
               'image-classification': DataaVecVisionForImageClassification,
               'image-segmentation': DataaVecVisionForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       SCREAMING_SNAKE_CASE =					False
       SCREAMING_SNAKE_CASE =					False
       SCREAMING_SNAKE_CASE =					False






       def     UpperCamelCase			(							self )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			DataaVecVisionModelTester(self )
          _SCREAMING_SNAKE_CASE       	=			ConfigTester(
              self    ,							config_class=A__    ,							has_text_modality=A__    ,							hidden_size=37 )






       def     UpperCamelCase			(							self )      ->     Dict:
          self.config_tester.run_common_tests()






       def     UpperCamelCase			(							self )      ->     Tuple:
          # Data2VecVision does not use inputs_embeds
          pass






       @require_torch_multi_gpu
       @unittest.skip(
           reason="""Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          pass






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs_for_common()

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






       def     UpperCamelCase			(							self )      ->     Any:
          _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs_for_common()

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

             _SCREAMING_SNAKE_CASE       	=			["""pixel_values"""]
             self.assertListEqual(arg_names[:1]    ,							A__ )






       def     UpperCamelCase			(							self )      ->     int:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A__ )






       def     UpperCamelCase			(							self )      ->     Any:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_segmentation(*A__ )






       def     UpperCamelCase			(							self )      ->     Any:
          if not self.model_tester.is_training:
             return

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

          for model_class in self.all_model_classes:
             if model_class in [*get_values(A__ )]:
                continue

             _SCREAMING_SNAKE_CASE       	=			model_class(A__ )
             model.to(A__ )
             model.train()
             _SCREAMING_SNAKE_CASE       	=			self._prepare_for_class(A__    ,							A__    ,							return_labels=A__ )
             _SCREAMING_SNAKE_CASE       	=			model(**A__ ).loss
             loss.backward()






       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs_for_common()
          if not self.model_tester.is_training:
             return

          _SCREAMING_SNAKE_CASE       	=			False
          _SCREAMING_SNAKE_CASE       	=			True

          for model_class in self.all_model_classes:
             if model_class in [*get_values(A__ )] or not model_class.supports_gradient_checkpointing:
                continue
             # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
             # this can then be incorporated into _prepare_for_class in test_modeling_common.py
             elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			inputs_dict["""pixel_values"""].shape
                _SCREAMING_SNAKE_CASE       	=			torch.zeros(
                    [self.model_tester.batch_size, height, width]    ,							device=A__ ).long()
             _SCREAMING_SNAKE_CASE       	=			model_class(A__ )
             model.gradient_checkpointing_enable()
             model.to(A__ )
             model.train()
             _SCREAMING_SNAKE_CASE       	=			self._prepare_for_class(A__    ,							A__    ,							return_labels=A__ )
             _SCREAMING_SNAKE_CASE       	=			model(**A__ ).loss
             loss.backward()






       def     UpperCamelCase			(							self )      ->     Tuple:
          _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE       	=			_config_zero_init(A__ )
          for model_class in self.all_model_classes:
             _SCREAMING_SNAKE_CASE       	=			model_class(config=A__ )
             for name, param in model.named_parameters():
                # we skip lambda parameters as these require special initial values
                # determined by config.layer_scale_init_value
                if "lambda" in name:
                   continue
                if param.requires_grad:
                   self.assertIn(
                       ((param.data.mean() * 1E9).round() / 1E9).item()    ,							[0.0, 1.0]    ,							msg=F"Parameter {name} of model {model_class} seems not properly initialized"    ,							)






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__=2E-4    ,							A__="outputs"    ,							A__=None )      ->     Tuple:
          # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
          super().check_pt_tf_outputs(A__    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__ )






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A__ )






       @slow
       def     UpperCamelCase			(							self )      ->     Dict:
          for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _SCREAMING_SNAKE_CASE       	=			DataaVecVisionModel.from_pretrained(A__ )
             self.assertIsNotNone(A__ )






def 				lowerCAmelCase_		(			)       ->       Dict:




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







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





       """simple docstring"""




       @cached_property
       def     UpperCamelCase			(							self )      ->     List[Any]:
          return (
              BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k""" ) if is_vision_available() else None
          )






       @slow
       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k""" ).to(
              A__ )

          _SCREAMING_SNAKE_CASE       	=			self.default_image_processor
          _SCREAMING_SNAKE_CASE       	=			prepare_img()
          _SCREAMING_SNAKE_CASE       	=			image_processor(images=A__    ,							return_tensors="""pt""" ).to(A__ )

          # forward pass
          with torch.no_grad():
             _SCREAMING_SNAKE_CASE       	=			model(**A__ )
          _SCREAMING_SNAKE_CASE       	=			outputs.logits

          # verify the logits
          _SCREAMING_SNAKE_CASE       	=			torch.Size((1, 10_00) )
          self.assertEqual(logits.shape    ,							A__ )

          _SCREAMING_SNAKE_CASE       	=			torch.tensor([0.3277, -0.1395, 0.0911] ).to(A__ )

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

          _SCREAMING_SNAKE_CASE       	=			[model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
          self.assertEqual(logits[0].topk(2 ).indices.cpu().tolist()    ,							A__ )








'''simple docstring'''



def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[int]:




   """simple docstring"""
   print("""\nVertex Distance"""  )
   for i in range(SCREAMING_SNAKE_CASE_  ):
      if dist[i] != float("""inf"""  ):
         print(SCREAMING_SNAKE_CASE_      , """\t"""      , int(dist[i]  )      , end="""\t"""  )
      else:
         print(SCREAMING_SNAKE_CASE_      , """\t"""      , """INF"""      , end="""\t"""  )
      print()
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			float("""inf"""  )
   _SCREAMING_SNAKE_CASE       	=			-1
   for i in range(SCREAMING_SNAKE_CASE_  ):
      if (not vset[i]) and mdist[i] < min_val:
         _SCREAMING_SNAKE_CASE       	=			i
         _SCREAMING_SNAKE_CASE       	=			mdist[i]
   return min_ind
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			[float("""inf"""  ) for _ in range(SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			[False for _ in range(SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			0.0

   for _ in range(v - 1  ):
      _SCREAMING_SNAKE_CASE       	=			min_dist(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			True

      for i in range(SCREAMING_SNAKE_CASE_  ):
         if (
             (not vset[i])
             and graph[u][i] != float("""inf"""  )
             and mdist[u] + graph[u][i] < mdist[i]
         ):
            _SCREAMING_SNAKE_CASE       	=			mdist[u] + graph[u][i]

   print_dist(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     UpperCamelCase__ :						int    							=					int(input("Enter number of vertices: ").strip())
     UpperCamelCase__ :						Tuple    							=					int(input("Enter number of edges: ").strip())

     UpperCamelCase__ :						Optional[Any]    							=					[[float("inf") for i in range(V)] for j in range(V)]

     for i in range(V):
          UpperCamelCase__ :						int    							=					0.0

     for i in range(E):
          print("\nEdge ", i + 1)
          UpperCamelCase__ :						Union[str, Any]    							=					int(input("Enter source:").strip())
          UpperCamelCase__ :						Tuple    							=					int(input("Enter destination:").strip())
          UpperCamelCase__ :						Any    							=					float(input("Enter weight:").strip())
          UpperCamelCase__ :						List[Any]    							=					weight

     UpperCamelCase__ :						Optional[Any]    							=					int(input("\nEnter shortest path source:").strip())
     dijkstra(graph, V, gsrc)






