
'''simple docstring'''



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class 				_a			(unittest.TestCase):





       """simple docstring"""




       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			tempfile.mkdtemp()

          _SCREAMING_SNAKE_CASE       	=			CLIPImageProcessor()
          _SCREAMING_SNAKE_CASE       	=			BertTokenizer.from_pretrained(
              """hf-internal-testing/tiny-random-BertModel"""    ,							model_input_names=["""input_ids""", """attention_mask"""] )

          _SCREAMING_SNAKE_CASE       	=			GitProcessor(A__    ,							A__ )

          processor.save_pretrained(self.tmpdirname )






       def     UpperCamelCase			(							self    ,							**A__ )      ->     List[Any]:
          return AutoProcessor.from_pretrained(self.tmpdirname    ,							**A__ ).tokenizer






       def     UpperCamelCase			(							self    ,							**A__ )      ->     List[str]:
          return AutoProcessor.from_pretrained(self.tmpdirname    ,							**A__ ).image_processor






       def     UpperCamelCase			(							self )      ->     int:
          shutil.rmtree(self.tmpdirname )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			[np.random.randint(2_55    ,							size=(3, 30, 4_00)    ,							dtype=np.uinta )]

          _SCREAMING_SNAKE_CASE       	=			[Image.fromarray(np.moveaxis(A__    ,							0    ,							-1 ) ) for x in image_inputs]

          return image_inputs






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			GitProcessor(tokenizer=self.get_tokenizer()    ,							image_processor=self.get_image_processor() )
          processor.save_pretrained(self.tmpdirname )

          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer(bos_token="""(BOS)"""    ,							eos_token="""(EOS)""" )
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor(do_normalize=A__    ,							padding_value=1.0 )

          _SCREAMING_SNAKE_CASE       	=			GitProcessor.from_pretrained(
              self.tmpdirname    ,							bos_token="""(BOS)"""    ,							eos_token="""(EOS)"""    ,							do_normalize=A__    ,							padding_value=1.0 )

          self.assertEqual(processor.tokenizer.get_vocab()    ,							tokenizer_add_kwargs.get_vocab() )
          self.assertIsInstance(processor.tokenizer    ,							A__ )

          self.assertEqual(processor.image_processor.to_json_string()    ,							image_processor_add_kwargs.to_json_string() )
          self.assertIsInstance(processor.image_processor    ,							A__ )






       def     UpperCamelCase			(							self )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor()
          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer()

          _SCREAMING_SNAKE_CASE       	=			GitProcessor(tokenizer=A__    ,							image_processor=A__ )

          _SCREAMING_SNAKE_CASE       	=			self.prepare_image_inputs()

          _SCREAMING_SNAKE_CASE       	=			image_processor(A__    ,							return_tensors="""np""" )
          _SCREAMING_SNAKE_CASE       	=			processor(images=A__    ,							return_tensors="""np""" )

          for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()    ,							input_processor[key].sum()    ,							delta=1E-2 )






       def     UpperCamelCase			(							self )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor()
          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer()

          _SCREAMING_SNAKE_CASE       	=			GitProcessor(tokenizer=A__    ,							image_processor=A__ )

          _SCREAMING_SNAKE_CASE       	=			"""lower newer"""

          _SCREAMING_SNAKE_CASE       	=			processor(text=A__ )

          _SCREAMING_SNAKE_CASE       	=			tokenizer(A__    ,							return_token_type_ids=A__ )

          for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]    ,							encoded_processor[key] )






       def     UpperCamelCase			(							self )      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor()
          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer()

          _SCREAMING_SNAKE_CASE       	=			GitProcessor(tokenizer=A__    ,							image_processor=A__ )

          _SCREAMING_SNAKE_CASE       	=			"""lower newer"""
          _SCREAMING_SNAKE_CASE       	=			self.prepare_image_inputs()

          _SCREAMING_SNAKE_CASE       	=			processor(text=A__    ,							images=A__ )

          self.assertListEqual(list(inputs.keys() )    ,							["""input_ids""", """attention_mask""", """pixel_values"""] )

          # test if it raises when no input is passed
          with pytest.raises(A__ ):
             processor()






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor()
          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer()

          _SCREAMING_SNAKE_CASE       	=			GitProcessor(tokenizer=A__    ,							image_processor=A__ )

          _SCREAMING_SNAKE_CASE       	=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          _SCREAMING_SNAKE_CASE       	=			processor.batch_decode(A__ )
          _SCREAMING_SNAKE_CASE       	=			tokenizer.batch_decode(A__ )

          self.assertListEqual(A__    ,							A__ )






       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self.get_image_processor()
          _SCREAMING_SNAKE_CASE       	=			self.get_tokenizer()

          _SCREAMING_SNAKE_CASE       	=			GitProcessor(tokenizer=A__    ,							image_processor=A__ )

          _SCREAMING_SNAKE_CASE       	=			"""lower newer"""
          _SCREAMING_SNAKE_CASE       	=			self.prepare_image_inputs()

          _SCREAMING_SNAKE_CASE       	=			processor(text=A__    ,							images=A__ )

          # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
          self.assertListEqual(list(inputs.keys() )    ,							["""input_ids""", """attention_mask""", """pixel_values"""] )








'''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)






