




import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ImageGPTImageProcessor




class      UpperCAmelCase_  (						unittest.TestCase							):




    def __init__(   self						:	List[Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	str=7     ,			snake_case_						:	List[Any]=3     ,			snake_case_						:	Union[str, Any]=18     ,			snake_case_						:	int=30     ,			snake_case_						:	Optional[Any]=400     ,			snake_case_						:	List[str]=True     ,			snake_case_						:	Tuple=None     ,			snake_case_						:	List[Any]=True     ,			)				->							List[str]:




     '''simple docstring'''

     A__	=    size if size is not None else {"height": 18, "width": 18}
     A__	=    parent
     A__	=    batch_size
     A__	=    num_channels
     A__	=    image_size
     A__	=    min_resolution
     A__	=    max_resolution
     A__	=    do_resize
     A__	=    size
     A__	=    do_normalize




    def 							__magic_name__							(   self						:	List[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                 [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
             ]				),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }






@require_torch
@require_vision
class      UpperCAmelCase_  (						A_,       unittest.TestCase							):
    lowercase__						 =				ImageGPTImageProcessor if is_vision_available() else None




    def 							__magic_name__							(   self						:	int				)				->							Tuple:




     '''simple docstring'''

     A__	=    ImageGPTImageProcessingTester(self				)




    @property
    def 							__magic_name__							(   self						:	int				)				->							Dict:




     '''simple docstring'''

     return self.image_processor_tester.prepare_image_processor_dict()




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




     '''simple docstring'''

     A__	=    self.image_processing_class(**self.image_processor_dict				)
     self.assertTrue(hasattr(snake_case_     ,			"clusters"				)				)
     self.assertTrue(hasattr(snake_case_     ,			"do_resize"				)				)
     self.assertTrue(hasattr(snake_case_     ,			"size"				)				)
     self.assertTrue(hasattr(snake_case_     ,			"do_normalize"				)				)




    def 							__magic_name__							(   self						:	int				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    self.image_processing_class.from_dict(self.image_processor_dict				)
     self.assertEqual(image_processor.size     ,			{"height": 18, "width": 18}				)

     A__	=    self.image_processing_class.from_dict(self.image_processor_dict     ,			size=42				)
     self.assertEqual(image_processor.size     ,			{"height": 42, "width": 42}				)




    def 							__magic_name__							(   self						:	Dict				)				->							Dict:




     '''simple docstring'''

     A__	=    self.image_processing_class(**self.image_processor_dict				)
     A__	=    json.loads(image_processor.to_json_string()				)
     for key, value in self.image_processor_dict.items():
      if key == "clusters":
       self.assertTrue(np.array_equal(snake_case_     ,			obj[key]				)				)
      else:
       self.assertEqual(obj[key]     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.image_processing_class(**self.image_processor_dict				)

     with tempfile.TemporaryDirectory() as tmpdirname:
      A__	=    os.path.join(snake_case_     ,			"image_processor.json"				)
      image_processor_first.to_json_file(snake_case_				)
      A__	=    self.image_processing_class.from_json_file(snake_case_				).to_dict()

     A__	=    image_processor_first.to_dict()
     for key, value in image_processor_first.items():
      if key == "clusters":
       self.assertTrue(np.array_equal(snake_case_     ,			image_processor_second[key]				)				)
      else:
       self.assertEqual(image_processor_first[key]     ,			snake_case_				)




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




     '''simple docstring'''

     A__	=    self.image_processing_class(**self.image_processor_dict				)

     with tempfile.TemporaryDirectory() as tmpdirname:
      image_processor_first.save_pretrained(snake_case_				)
      A__	=    self.image_processing_class.from_pretrained(snake_case_				).to_dict()

     A__	=    image_processor_first.to_dict()
     for key, value in image_processor_first.items():
      if key == "clusters":
       self.assertTrue(np.array_equal(snake_case_     ,			image_processor_second[key]				)				)
      else:
       self.assertEqual(image_processor_first[key]     ,			snake_case_				)




    @unittest.skip("ImageGPT requires clusters at initialization"				)
    def 							__magic_name__							(   self						:	int				)				->							Optional[Any]:




     '''simple docstring'''

     pass







def   _SCREAMING_SNAKE_CASE       (							)						->		str:
 A__	=    load_dataset("hf-internal-testing/fixtures_image_utils"      ,			split="test"						)

 A__	=    Image.open(dataset[4]["file"]						)
 A__	=    Image.open(dataset[5]["file"]						)

 A__	=    [imagea, imagea]

 return images






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




    @slow
    def 							__magic_name__							(   self						:	Optional[int]				)				->							str:




     '''simple docstring'''

     A__	=    ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"				)

     A__	=    prepare_images()

     # test non-batched
     A__	=    image_processing(images[0]     ,			return_tensors="pt"				)

     self.assertIsInstance(encoding.input_ids     ,			torch.LongTensor				)
     self.assertEqual(encoding.input_ids.shape     ,			(1, 1_024)				)

     A__	=    [306, 191, 191]
     self.assertEqual(encoding.input_ids[0, :3].tolist()     ,			snake_case_				)

     # test batched
     A__	=    image_processing(snake_case_     ,			return_tensors="pt"				)

     self.assertIsInstance(encoding.input_ids     ,			torch.LongTensor				)
     self.assertEqual(encoding.input_ids.shape     ,			(2, 1_024)				)

     A__	=    [303, 13, 13]
     self.assertEqual(encoding.input_ids[1, -3:].tolist()     ,			snake_case_				)











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"Alexander Joslin"

import operator as op

from .stack import Stack







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 A__	=    {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

 A__	=    Stack()
 A__	=    Stack()

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(lowercase_						)						)
  elif i in operators:
   # RULE 2
   operator_stack.push(lowercase_						)
  elif i == ")":
   # RULE 4
   A__	=    operator_stack.peek()
   operator_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()

   A__	=    operators[opr](lowercase_      ,			lowercase_						)
   operand_stack.push(lowercase_						)

    # RULE 5
 return operand_stack.peek()


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	"(5 + ((4 * 2) * (2 + 3)))"
      # answer = 45
      print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')





