





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 					A     (unittest.TestCase					):



      def __init__(       self  ,	lowercase_  ,	lowercase_=7  ,	lowercase_=3  ,	lowercase_=18  ,	lowercase_=30  ,	lowercase_=400  ,	lowercase_=True  ,	lowercase_=None  ,	lowercase_=True  ,	)   -> str:



             '''simple docstring'''

             _snake_case   :				Optional[int]  					=    size if size is not None else {"height": 18, "width": 18}
             _snake_case   :				List[str]  					=    parent
             _snake_case   :				Union[str, Any]  					=    batch_size
             _snake_case   :				List[str]  					=    num_channels
             _snake_case   :				List[Any]  					=    image_size
             _snake_case   :				Tuple  					=    min_resolution
             _snake_case   :				Optional[int]  					=    max_resolution
             _snake_case   :				Any  					=    do_resize
             _snake_case   :				Any  					=    size
             _snake_case   :				int  					=    do_normalize



      def  __a			(       self       )   -> Tuple:



             '''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 					A     (__UpperCAmelCase							,unittest.TestCase					):
      _SCREAMING_SNAKE_CASE       =				ImageGPTImageProcessor if is_vision_available() else None



      def  __a			(       self       )   -> Any:



             '''simple docstring'''

             _snake_case   :				List[str]  					=    ImageGPTImageProcessingTester(self       )



      @property
      def  __a			(       self       )   -> str:



             '''simple docstring'''

             return self.image_processor_tester.prepare_image_processor_dict()



      def  __a			(       self       )   -> str:



             '''simple docstring'''

             _snake_case   :				List[str]  					=    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  __a			(       self       )   -> int:



             '''simple docstring'''

             _snake_case   :				str  					=    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size  ,	{'''height''': 18, '''width''': 18}       )

             _snake_case   :				Union[str, Any]  					=    self.image_processing_class.from_dict(self.image_processor_dict  ,	size=42       )
             self.assertEqual(image_processor.size  ,	{'''height''': 42, '''width''': 42}       )



      def  __a			(       self       )   -> str:



             '''simple docstring'''

             _snake_case   :				int  					=    self.image_processing_class(**self.image_processor_dict       )
             _snake_case   :				List[str]  					=    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  __a			(       self       )   -> Optional[int]:



             '''simple docstring'''

             _snake_case   :				Dict  					=    self.image_processing_class(**self.image_processor_dict       )

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _snake_case   :				Union[str, Any]  					=    os.path.join(snake_case__  ,	'''image_processor.json'''       )
                    image_processor_first.to_json_file(snake_case__       )
                    _snake_case   :				Any  					=    self.image_processing_class.from_json_file(snake_case__       ).to_dict()

             _snake_case   :				Union[str, Any]  					=    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  __a			(       self       )   -> str:



             '''simple docstring'''

             _snake_case   :				int  					=    self.image_processing_class(**self.image_processor_dict       )

             with tempfile.TemporaryDirectory() as tmpdirname:
                    image_processor_first.save_pretrained(snake_case__       )
                    _snake_case   :				List[str]  					=    self.image_processing_class.from_pretrained(snake_case__       ).to_dict()

             _snake_case   :				Tuple  					=    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  __a			(       self       )   -> Optional[int]:



             '''simple docstring'''

             pass




def     A_      (	)      ->    str:
       _snake_case   :				List[str]  					=    load_dataset('''hf-internal-testing/fixtures_image_utils'''   ,		split='''test'''	)

       _snake_case   :				Tuple  					=    Image.open(dataset[4]['''file''']	)
       _snake_case   :				List[Any]  					=    Image.open(dataset[5]['''file''']	)

       _snake_case   :				Optional[Any]  					=    [imagea, imagea]

       return images

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



      @slow
      def  __a			(       self       )   -> Optional[Any]:



             '''simple docstring'''

             _snake_case   :				Dict  					=    ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''       )

             _snake_case   :				Union[str, Any]  					=    prepare_images()

             # test non-batched
             _snake_case   :				Optional[Any]  					=    image_processing(images[0]  ,	return_tensors='''pt'''       )

             self.assertIsInstance(encoding.input_ids  ,	torch.LongTensor       )
             self.assertEqual(encoding.input_ids.shape  ,	(1, 1024)       )

             _snake_case   :				Union[str, Any]  					=    [306, 191, 191]
             self.assertEqual(encoding.input_ids[0, :3].tolist()  ,	snake_case__       )

             # test batched
             _snake_case   :				List[Any]  					=    image_processing(snake_case__  ,	return_tensors='''pt'''       )

             self.assertIsInstance(encoding.input_ids  ,	torch.LongTensor       )
             self.assertEqual(encoding.input_ids.shape  ,	(2, 1024)       )

             _snake_case   :				List[str]  					=    [303, 13, 13]
             self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,	snake_case__       )








'''simple docstring'''





import operator as op







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Optional[Any]				       =							[]
  UpperCAmelCase__      :					Any				       =							lambda lowercase__   ,       lowercase__		: int(x / y		)  # noqa: E731 integer division operation
  UpperCAmelCase__      :					Any				       =							{
      "^": op.pow,
      "*": op.mul,
      "/": div,
      "+": op.add,
      "-": op.sub,
  }  # operators & their respective operation

  # print table header
  print("Symbol".center(8		)   ,       "Action".center(1_2		)   ,       "Stack"   ,       sep=" | "		)
  print("-" * (3_0 + len(lowercase__		))		)

  for x in post_fix:
    if x.isdigit():  # if x in digit
      stack.append(lowercase__		)  # append x to stack
      # output in tabular format
      print(x.rjust(8		)   ,       ("push(" + x + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "		)
    else:
      UpperCAmelCase__      :					str				       =							stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8		)   ,       ("pop(" + b + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "		)

      UpperCAmelCase__      :					List[Any]				       =							stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8		)   ,       ("pop(" + a + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "		)

      stack.append(
          str(opr[x](int(lowercase__		)   ,       int(lowercase__		)		)		)		)  # evaluate the 2 values popped from stack & push result to stack
      # output in tabular format
      print(
          x.rjust(8		)   ,       ("push(" + a + x + b + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "   ,       )

  return int(stack[0]		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE     =       input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
      print("""\n\tResult = """, solve(Postfix))



