

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
 from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
 _UpperCamelCase     =    False

if is_vision_available():
 from PIL import Image

 from transformers import (
     AutoProcessor,
     PixaStructImageProcessor,
     PixaStructProcessor,
     PreTrainedTokenizerFast,
     TaTokenizer,
 )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11				,       reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""				,       )
@require_vision
@require_torch
class 					lowercase		( unittest.TestCase      ):



 '''simple docstring'''


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






  """simple docstring"""





  UpperCAmelCase__           =       tempfile.mkdtemp()

  UpperCAmelCase__           =       PixaStructImageProcessor()
  UpperCAmelCase__           =       TaTokenizer.from_pretrained('t5-small'				)

  UpperCAmelCase__           =       PixaStructProcessor(__a , __a				)

  processor.save_pretrained(self.tmpdirname				)




 def 						UpperCamelCase__							(self , **__a				)				->      int:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self , **__a				)				->      Any:






  """simple docstring"""





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




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






  """simple docstring"""





  shutil.rmtree(self.tmpdirname				)




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta				)]

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

  return image_inputs




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       PixaStructProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor()				)
  processor.save_pretrained(self.tmpdirname				)

  UpperCAmelCase__           =       self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)'				)
  UpperCAmelCase__           =       self.get_image_processor(do_normalize=__a , padding_value=1.0				)

  UpperCAmelCase__           =       PixaStructProcessor.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				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_image_processor()
  UpperCAmelCase__           =       self.get_tokenizer()

  UpperCAmelCase__           =       PixaStructProcessor(tokenizer=__a , image_processor=__a				)

  UpperCAmelCase__           =       self.prepare_image_inputs()

  UpperCAmelCase__           =       image_processor(__a , return_tensors='np'				)
  UpperCAmelCase__           =       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				)				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_image_processor()
  UpperCAmelCase__           =       self.get_tokenizer()

  UpperCAmelCase__           =       PixaStructProcessor(tokenizer=__a , image_processor=__a				)

  UpperCAmelCase__           =       'lower newer'

  UpperCAmelCase__           =       processor(text=__a				)

  UpperCAmelCase__           =       tokenizer(__a , return_token_type_ids=__a , add_special_tokens=__a				)

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




 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_image_processor()
  UpperCAmelCase__           =       self.get_tokenizer()

  UpperCAmelCase__           =       PixaStructProcessor(tokenizer=__a , image_processor=__a				)

  UpperCAmelCase__           =       'lower newer'
  UpperCAmelCase__           =       self.prepare_image_inputs()

  UpperCAmelCase__           =       processor(text=__a , images=__a				)

  self.assertListEqual(
      list(inputs.keys()				) , ['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']				)

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




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_image_processor()
  UpperCAmelCase__           =       self.get_tokenizer()

  UpperCAmelCase__           =       PixaStructProcessor(tokenizer=__a , image_processor=__a				)

  UpperCAmelCase__           =       'lower newer'
  UpperCAmelCase__           =       self.prepare_image_inputs()

  UpperCAmelCase__           =       processor(text=__a , images=__a				)

  UpperCAmelCase__           =       [512, 1024, 2048, 4096]
  UpperCAmelCase__           =       [770, 770, 770, 770]
  # with text
  for i, max_patch in enumerate(__a				):
   UpperCAmelCase__           =       processor(text=__a , images=__a , max_patches=__a				)
   self.assertEqual(inputs['flattened_patches'][0].shape[0] , __a				)
   self.assertEqual(inputs['flattened_patches'][0].shape[1] , expected_hidden_size[i]				)

  # without text input
  for i, max_patch in enumerate(__a				):
   UpperCAmelCase__           =       processor(images=__a , max_patches=__a				)
   self.assertEqual(inputs['flattened_patches'][0].shape[0] , __a				)
   self.assertEqual(inputs['flattened_patches'][0].shape[1] , expected_hidden_size[i]				)




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_image_processor()
  UpperCAmelCase__           =       self.get_tokenizer()

  UpperCAmelCase__           =       PixaStructProcessor(tokenizer=__a , image_processor=__a				)

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

  UpperCAmelCase__           =       processor.batch_decode(__a				)
  UpperCAmelCase__           =       tokenizer.batch_decode(__a				)

  self.assertListEqual(__a , __a				)




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_image_processor()
  UpperCAmelCase__           =       self.get_tokenizer()

  UpperCAmelCase__           =       PixaStructProcessor(tokenizer=__a , image_processor=__a				)

  UpperCAmelCase__           =       'lower newer'
  UpperCAmelCase__           =       self.prepare_image_inputs()

  UpperCAmelCase__           =       processor(text=__a , images=__a				)

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

  UpperCAmelCase__           =       processor(text=__a				)

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






from __future__ import annotations



class 					lowercase		:



 '''simple docstring'''


 def __init__(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       vertices
  UpperCAmelCase__           =       [[0] * vertices for _ in range(__a				)]




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  print('Vertex \t Distance from Source'				)
  for vertex in range(self.vertices				):
   print(__a , '\t\t' , distances_from_source[vertex]				)




 def 						UpperCamelCase__							(self , __a , __a				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       1E7
  UpperCAmelCase__           =       0

  # Search not nearest vertex not in the shortest path tree
  for vertex in range(self.vertices				):
   if distances_from_source[vertex] < minimum and visited[vertex] is False:
    UpperCAmelCase__           =       distances_from_source[vertex]
    UpperCAmelCase__           =       vertex
  return min_index




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       [int(1E7				)] * self.vertices  # distances from the source
  UpperCAmelCase__           =       0
  UpperCAmelCase__           =       [False] * self.vertices

  for _ in range(self.vertices				):
   UpperCAmelCase__           =       self.minimum_distance(__a , __a				)
   UpperCAmelCase__           =       True

   # Update dist value of the adjacent vertices
   # of the picked vertex only if the current
   # distance is greater than new distance and
   # the vertex in not in the shortest path tree
   for v in range(self.vertices				):
    if (
        self.graph[u][v] > 0
        and visited[v] is False
        and distances[v] > distances[u] + self.graph[u][v]
    ):
     UpperCAmelCase__           =       distances[u] + self.graph[u][v]

  self.print_solution(__a				)


if __name__ == "__main__":
 _UpperCamelCase     =    Graph(9)
 _UpperCamelCase     =    [
     [0, 4, 0, 0, 0, 0, 0, 8, 0],
     [4, 0, 8, 0, 0, 0, 0, 11, 0],
     [0, 8, 0, 7, 0, 4, 0, 0, 2],
     [0, 0, 7, 0, 9, 14, 0, 0, 0],
     [0, 0, 0, 9, 0, 10, 0, 0, 0],
     [0, 0, 4, 14, 10, 0, 2, 0, 0],
     [0, 0, 0, 0, 0, 2, 0, 1, 6],
     [8, 11, 0, 0, 0, 0, 1, 0, 7],
     [0, 0, 2, 0, 0, 0, 6, 7, 0],
 ]
 graph.dijkstra(0)



