





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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast



@require_vision
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =tempfile.mkdtemp()

   lowerCamelCase__:				Union[str, Any]        =BlipImageProcessor()
   lowerCamelCase__:				Union[str, Any]        =GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")

   lowerCamelCase__:				Optional[int]        =BlipaProcessor(UpperCAmelCase_	,			UpperCAmelCase_)

   processor.save_pretrained(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			**UpperCAmelCase_    :       Optional[Any])	->int:



   '''simple docstring'''



   return AutoProcessor.from_pretrained(self.tmpdirname	,			**UpperCAmelCase_).tokenizer

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			**UpperCAmelCase_    :       Optional[int])	->List[str]:



   '''simple docstring'''



   return AutoProcessor.from_pretrained(self.tmpdirname	,			**UpperCAmelCase_).image_processor

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Union[str, Any]:



   '''simple docstring'''



   shutil.rmtree(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->str:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =[np.random.randint(255	,			size=(3, 30, 400)	,			dtype=np.uinta)]

   lowerCamelCase__:				Optional[int]        =[Image.fromarray(np.moveaxis(UpperCAmelCase_	,			0	,			-1)) for x in image_inputs]

   return image_inputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =BlipaProcessor(tokenizer=self.get_tokenizer()	,			image_processor=self.get_image_processor())
   processor.save_pretrained(self.tmpdirname)

   lowerCamelCase__:				Union[str, Any]        =self.get_tokenizer(bos_token="(BOS)"	,			eos_token="(EOS)")
   lowerCamelCase__:				List[Any]        =self.get_image_processor(do_normalize=UpperCAmelCase_	,			padding_value=1.0)

   lowerCamelCase__:				Tuple        =BlipaProcessor.from_pretrained(
       self.tmpdirname	,			bos_token="(BOS)"	,			eos_token="(EOS)"	,			do_normalize=UpperCAmelCase_	,			padding_value=1.0)

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

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =self.get_image_processor()
   lowerCamelCase__:				Union[str, Any]        =self.get_tokenizer()

   lowerCamelCase__:				Any        =BlipaProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Optional[Any]        =self.prepare_image_inputs()

   lowerCamelCase__:				List[str]        =image_processor(UpperCAmelCase_	,			return_tensors="np")
   lowerCamelCase__:				Union[str, Any]        =processor(images=UpperCAmelCase_	,			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 							SCREAMING_SNAKE_CASE_ (self    :       int)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =self.get_image_processor()
   lowerCamelCase__:				str        =self.get_tokenizer()

   lowerCamelCase__:				Optional[Any]        =BlipaProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Optional[Any]        ="lower newer"

   lowerCamelCase__:				Union[str, Any]        =processor(text=UpperCAmelCase_)

   lowerCamelCase__:				str        =tokenizer(UpperCAmelCase_	,			return_token_type_ids=UpperCAmelCase_)

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =self.get_image_processor()
   lowerCamelCase__:				Dict        =self.get_tokenizer()

   lowerCamelCase__:				str        =BlipaProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Tuple        ="lower newer"
   lowerCamelCase__:				str        =self.prepare_image_inputs()

   lowerCamelCase__:				Tuple        =processor(text=UpperCAmelCase_	,			images=UpperCAmelCase_)

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

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

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



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =self.get_image_processor()
   lowerCamelCase__:				Union[str, Any]        =self.get_tokenizer()

   lowerCamelCase__:				Tuple        =BlipaProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Dict        =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   lowerCamelCase__:				List[Any]        =processor.batch_decode(UpperCAmelCase_)
   lowerCamelCase__:				Union[str, Any]        =tokenizer.batch_decode(UpperCAmelCase_)

   self.assertListEqual(UpperCAmelCase_	,			UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->int:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =self.get_image_processor()
   lowerCamelCase__:				List[Any]        =self.get_tokenizer()

   lowerCamelCase__:				List[str]        =BlipaProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				List[str]        ="lower newer"
   lowerCamelCase__:				Optional[int]        =self.prepare_image_inputs()

   lowerCamelCase__:				Tuple        =processor(text=UpperCAmelCase_	,			images=UpperCAmelCase_)

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









from __future__ import annotations





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		List[Any]:







  """simple docstring"""





  print(F"""Vertex\tShortest Distance from vertex {src}"""		)
  for i, d in enumerate(__a		):
    print(F"""{i}\t\t{d}"""		)





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		Tuple:







  """simple docstring"""





  for j in range(__a		):
    lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				str        =(graph[j][k] for k in ["src", "dst", "weight"])
    if distance[u] != float("inf"		) and distance[u] + w < distance[v]:
      return True
  return False





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a		)					->		list[float]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =[float("inf"		)] * vertex_count
  lowerCamelCase__:				List[str]        =0.0

  for _ in range(vertex_count - 1		):
    for j in range(__a		):
      lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =(graph[j][k] for k in ["src", "dst", "weight"])

      if distance[u] != float("inf"		) and distance[u] + w < distance[v]:
        lowerCamelCase__:				int        =distance[u] + w

  lowerCamelCase__:				Tuple        =check_negative_cycle(__a     ,   __a     ,   __a		)
  if negative_cycle_exists:
    raise Exception("Negative cycle found"		)

  return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     __A     =    int(input("Enter number of vertices: ").strip())
     __A     =    int(input("Enter number of edges: ").strip())

     __A     =    [{} for _ in range(E)]

     for i in range(E):
          print("Edge ", i + 1)
          __A ,     __A ,     __A     =    (
              int(x)
              for x in input("Enter source, destination, weight: ").strip().split(" ")
          )
          __A     =    {"src": src, "dst": dest, "weight": weight}

     __A     =    int(input("\nEnter shortest path source:").strip())
     __A     =    bellman_ford(graph, V, E, source)
     print_distance(shortest_distance, 0)

