

"""simple docstring"""




import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor
class 			_UpperCAmelCase					(				unittest.TestCase    ):



	def __init__(   self			:    str       ,  _lowercase			:    Optional[int]       ,  _lowercase			:    Optional[int]=13       ,  _lowercase			:    Optional[Any]=3       ,  _lowercase			:    Optional[Any]=2_24       ,  _lowercase			:    Optional[Any]=30       ,  _lowercase			:    Any=4_00       ,  _lowercase			:    int=True       ,  _lowercase			:    Optional[int]=None       ,  _lowercase			:    Any=True       ,  _lowercase			:    Optional[Any]=[0.5, 0.5, 0.5]       ,  _lowercase			:    Optional[int]=[0.5, 0.5, 0.5]       ,  ):
		__UpperCAmelCase							=      size if size is not None else {'''height''': 18, '''width''': 18}
		__UpperCAmelCase							=      parent
		__UpperCAmelCase							=      batch_size
		__UpperCAmelCase							=      num_channels
		__UpperCAmelCase							=      image_size
		__UpperCAmelCase							=      min_resolution
		__UpperCAmelCase							=      max_resolution
		__UpperCAmelCase							=      do_resize
		__UpperCAmelCase							=      size
		__UpperCAmelCase							=      do_normalize
		__UpperCAmelCase							=      image_mean
		__UpperCAmelCase							=      image_std



	def     a   (   self			:    List[Any]		):
		return {
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_normalize": self.do_normalize,
		    "do_resize": self.do_resize,
		    "size": self.size,
		}






@require_torch
@require_vision
class 			_UpperCAmelCase					(				__lowercase						,  unittest.TestCase    ):
	a__						:      Dict						       = ViTImageProcessor if is_vision_available() else None



	def     a   (   self			:    List[str]		):
		__UpperCAmelCase							=      EfficientFormerImageProcessorTester(self		)



	@property
	def     a   (   self			:    List[Any]		):
		return self.image_proc_tester.prepare_image_processor_dict()



	def     a   (   self			:    List[Any]		):
		__UpperCAmelCase							=      self.image_processing_class(**self.image_processor_dict		)
		self.assertTrue(hasattr(UpperCAmelCase__       ,  '''image_mean'''		)		)
		self.assertTrue(hasattr(UpperCAmelCase__       ,  '''image_std'''		)		)
		self.assertTrue(hasattr(UpperCAmelCase__       ,  '''do_normalize'''		)		)
		self.assertTrue(hasattr(UpperCAmelCase__       ,  '''do_resize'''		)		)
		self.assertTrue(hasattr(UpperCAmelCase__       ,  '''size'''		)		)



	def     a   (   self			:    int		):
		pass



	def     a   (   self			:    Union[str, Any]		):
		# Initialize image_processor
		__UpperCAmelCase							=      self.image_processing_class(**self.image_processor_dict		)
		# create random PIL images
		__UpperCAmelCase							=      prepare_image_inputs(self.image_proc_tester       ,  equal_resolution=UpperCAmelCase__		)
		for image in image_inputs:
			self.assertIsInstance(UpperCAmelCase__       ,  Image.Image		)

		# Test not batched input
		__UpperCAmelCase							=      image_processor(image_inputs[0]       ,  return_tensors='''pt'''		).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size['''height'''],
		        self.image_proc_tester.size['''width'''],
		    )       ,  )

		# Test batched
		__UpperCAmelCase							=      image_processor(UpperCAmelCase__       ,  return_tensors='''pt'''		).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size['''height'''],
		        self.image_proc_tester.size['''width'''],
		    )       ,  )



	def     a   (   self			:    str		):
		# Initialize image_processor
		__UpperCAmelCase							=      self.image_processing_class(**self.image_processor_dict		)
		# create random numpy tensors
		__UpperCAmelCase							=      prepare_image_inputs(self.image_proc_tester       ,  equal_resolution=UpperCAmelCase__       ,  numpify=UpperCAmelCase__		)
		for image in image_inputs:
			self.assertIsInstance(UpperCAmelCase__       ,  np.ndarray		)

		# Test not batched input
		__UpperCAmelCase							=      image_processor(image_inputs[0]       ,  return_tensors='''pt'''		).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size['''height'''],
		        self.image_proc_tester.size['''width'''],
		    )       ,  )

		# Test batched
		__UpperCAmelCase							=      image_processor(UpperCAmelCase__       ,  return_tensors='''pt'''		).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size['''height'''],
		        self.image_proc_tester.size['''width'''],
		    )       ,  )



	def     a   (   self			:    List[Any]		):
		# Initialize image_processor
		__UpperCAmelCase							=      self.image_processing_class(**self.image_processor_dict		)
		# create random PyTorch tensors
		__UpperCAmelCase							=      prepare_image_inputs(self.image_proc_tester       ,  equal_resolution=UpperCAmelCase__       ,  torchify=UpperCAmelCase__		)
		for image in image_inputs:
			self.assertIsInstance(UpperCAmelCase__       ,  torch.Tensor		)

		# Test not batched input
		__UpperCAmelCase							=      image_processor(image_inputs[0]       ,  return_tensors='''pt'''		).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size['''height'''],
		        self.image_proc_tester.size['''width'''],
		    )       ,  )

		# Test batched
		__UpperCAmelCase							=      image_processor(UpperCAmelCase__       ,  return_tensors='''pt'''		).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size['''height'''],
		        self.image_proc_tester.size['''width'''],
		    )       ,  )




'''simple docstring'''




import os

__snake_case    						={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}





def 	a_					( lowerCamelCase	:     str				):
   lowerCAmelCase     =			0

   lowerCAmelCase     =			0
   while index < len(lowerCamelCase				) - 1:
      lowerCAmelCase     =			SYMBOLS[numerals[index]]
      lowerCAmelCase     =			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 	a_					( lowerCamelCase	:     int				):
   lowerCAmelCase     =			''

   lowerCAmelCase     =			num // 1000
   numerals += m_count * "M"
   num %= 1000

   lowerCAmelCase     =			num // 100
   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 %= 100

   lowerCAmelCase     =			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 	a_					( lowerCamelCase	:     str = "/p089_roman.txt"				):
   lowerCAmelCase     =			0

   with open(os.path.dirname(lowerCamelCase				) + roman_numerals_filename				) as filea:
      lowerCAmelCase     =			filea.readlines()

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

   return savings


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





