




'''simple docstring'''



import os

UpperCAmelCase_							:	Tuple	     =		{'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}







def       _UpperCamelCase			(_lowerCamelCase				:  str						)-> int:




     '''simple docstring'''





     __snake_case            =	0

     __snake_case            =	0
     while index < len(_lowerCamelCase						) - 1:
          __snake_case            =	SYMBOLS[numerals[index]]
          __snake_case            =	SYMBOLS[numerals[index + 1]]
          if current_value < next_value:
               total_value -= current_value
          else:
               total_value += current_value
          index += 1
     total_value += SYMBOLS[numerals[index]]

     return total_value







def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> str:




     '''simple docstring'''





     __snake_case            =	''''''

     __snake_case            =	num // 10_00
     numerals += m_count * "M"
     num %= 10_00

     __snake_case            =	num // 1_00
     if c_count == 9:
          numerals += "CM"
          c_count -= 9
     elif c_count == 4:
          numerals += "CD"
          c_count -= 4
     if c_count >= 5:
          numerals += "D"
          c_count -= 5
     numerals += c_count * "C"
     num %= 1_00

     __snake_case            =	num // 10
     if x_count == 9:
          numerals += "XC"
          x_count -= 9
     elif x_count == 4:
          numerals += "XL"
          x_count -= 4
     if x_count >= 5:
          numerals += "L"
          x_count -= 5
     numerals += x_count * "X"
     num %= 10

     if num == 9:
          numerals += "IX"
          num -= 9
     elif num == 4:
          numerals += "IV"
          num -= 4
     if num >= 5:
          numerals += "V"
          num -= 5
     numerals += num * "I"

     return numerals







def       _UpperCamelCase			(_lowerCamelCase				:  str = "/p089_roman.txt"						)-> int:




     '''simple docstring'''





     __snake_case            =	0

     with open(os.path.dirname(_lowerCamelCase						) + roman_numerals_filename						) as filea:
          __snake_case            =	filea.readlines()

     for line in lines:
          __snake_case            =	line.strip()
          __snake_case            =	parse_roman_numerals(_lowerCamelCase						)
          __snake_case            =	generate_roman_numerals(_lowerCamelCase						)
          savings += len(_lowerCamelCase						) - len(_lowerCamelCase						)

     return savings


if __name__ == "__main__":
     print(F"""{solution() = }""")








'''simple docstring'''





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				):

 '''simple docstring'''







 def __init__(					self		:  Optional[int]			,				snake_case__		:  int			,				snake_case__		:  Optional[Any]=7			,				snake_case__		:  Tuple=3			,				snake_case__		:  Optional[Any]=18			,				snake_case__		:  Dict=30			,				snake_case__		:  Optional[int]=4_00			,				snake_case__		:  str=True			,				snake_case__		:  str=None			,				snake_case__		:  Optional[int]=True			,				):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return {
       # here we create 2 clusters for the sake of simplicity
       "clusters": np.asarray(
           [
               [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
               [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
           ] ),
       "do_resize": self.do_resize,
       "size": self.size,
       "do_normalize": self.do_normalize,
   }






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

 '''simple docstring'''
 lowercase_					:    Any				=							ImageGPTImageProcessor if is_vision_available() else None







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							ImageGPTImageProcessingTester(self )







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.image_processor_tester.prepare_image_processor_dict()







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					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 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




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

   UpperCAmelCase__      :					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 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.image_processing_class(**self.image_processor_dict )
   UpperCAmelCase__      :					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 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




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

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

   UpperCAmelCase__      :					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 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




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

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

   UpperCAmelCase__      :					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 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   pass







def snake_case_     (					):
  UpperCAmelCase__      :					List[str]				       =							load_dataset("hf-internal-testing/fixtures_image_utils"   ,       split="test"		)

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

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

  return images






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

 '''simple docstring'''







 @slow
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" )

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

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

   self.assertIsInstance(encoding.input_ids			,				torch.LongTensor )
   self.assertEqual(encoding.input_ids.shape			,				(1, 10_24) )

   UpperCAmelCase__      :					Union[str, Any]				       =							[3_06, 1_91, 1_91]
   self.assertEqual(encoding.input_ids[0, :3].tolist()			,				snake_case__ )

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

   self.assertIsInstance(encoding.input_ids			,				torch.LongTensor )
   self.assertEqual(encoding.input_ids.shape			,				(2, 10_24) )

   UpperCAmelCase__      :					List[str]				       =							[3_03, 13, 13]
   self.assertEqual(encoding.input_ids[1, -3:].tolist()			,				snake_case__ )



