
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						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			tempfile.mkdtemp()

					lowerCamelCase_						=			BlipImageProcessor()
					lowerCamelCase_						=			GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )

					lowerCamelCase_						=			BlipaProcessor(lowercase						,  lowercase )

					processor.save_pretrained(self.tmpdirname )



	def     SCREAMING_SNAKE_CASE_(    self						,  **lowercase )				->       Union[str, Any]:
					return AutoProcessor.from_pretrained(self.tmpdirname						,  **lowercase ).tokenizer



	def     SCREAMING_SNAKE_CASE_(    self						,  **lowercase )				->       str:
					return AutoProcessor.from_pretrained(self.tmpdirname						,  **lowercase ).image_processor



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					shutil.rmtree(self.tmpdirname )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			[np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta )]

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

					return image_inputs



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			BlipaProcessor(tokenizer=self.get_tokenizer()						,  image_processor=self.get_image_processor() )
					processor.save_pretrained(self.tmpdirname )

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

					lowerCamelCase_						=			BlipaProcessor.from_pretrained(
					    self.tmpdirname						,  bos_token="(BOS)"						,  eos_token="(EOS)"						,  do_normalize=lowercase						,  padding_value=1.0 )

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

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

					lowerCamelCase_						=			self.prepare_image_inputs()

					lowerCamelCase_						=			image_processor(lowercase						,  return_tensors="np" )
					lowerCamelCase_						=			processor(images=lowercase						,  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 )				->       Optional[int]:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

					lowerCamelCase_						=			"lower newer"

					lowerCamelCase_						=			processor(text=lowercase )

					lowerCamelCase_						=			tokenizer(lowercase						,  return_token_type_ids=lowercase )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

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

					lowerCamelCase_						=			processor(text=lowercase						,  images=lowercase )

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

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

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

					lowerCamelCase_						=			processor.batch_decode(lowercase )
					lowerCamelCase_						=			tokenizer.batch_decode(lowercase )

					self.assertListEqual(lowercase						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

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

					lowerCamelCase_						=			processor(text=lowercase						,  images=lowercase )

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










"""simple docstring"""




from __future__ import annotations

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  print(F"""Vertex\tShortest Distance from vertex {src}"""				)
  for i, d in enumerate(UpperCamelCase__				):
    print(F"""{i}\t\t{d}"""				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for j in range(UpperCamelCase__				):
    _a      ,  _a      ,  _a  :       List[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__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Dict             =	[float("""inf"""				)] * vertex_count
  _a  :       Any             =	0.0

  for _ in range(vertex_count - 1				):
    for j in range(UpperCamelCase__				):
      _a      ,  _a      ,  _a  :       List[Any]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])

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

  _a  :       Union[str, Any]             =	check_negative_cycle(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  if negative_cycle_exists:
    raise Exception("""Negative cycle found"""				)

  return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _snake_case          =   int(input('Enter number of vertices: ').strip())
 _snake_case          =   int(input('Enter number of edges: ').strip())

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

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

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

