





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 ConvNextImageProcessor



class       __lowerCAmelCase (    unittest.TestCase  ):






			def __init__(self      ,							__magic_name__      ,							__magic_name__=7      ,							__magic_name__=3      ,							__magic_name__=18      ,							__magic_name__=30      ,							__magic_name__=400      ,							__magic_name__=True      ,							__magic_name__=None      ,							__magic_name__=0.875      ,							__magic_name__=True      ,							__magic_name__=[0.5, 0.5, 0.5]      ,							__magic_name__=[0.5, 0.5, 0.5]      ,							)  ->     Union[str, Any]:


							'''simple docstring'''



							snake_case_		:					List[str]			    =						size if size is not None else {'''shortest_edge''': 20}
							snake_case_		:					List[str]			    =						parent
							snake_case_		:					Optional[Any]			    =						batch_size
							snake_case_		:					Optional[Any]			    =						num_channels
							snake_case_		:					Dict			    =						image_size
							snake_case_		:					Any			    =						min_resolution
							snake_case_		:					Optional[Any]			    =						max_resolution
							snake_case_		:					Any			    =						do_resize
							snake_case_		:					str			    =						size
							snake_case_		:					Dict			    =						crop_pct
							snake_case_		:					List[Any]			    =						do_normalize
							snake_case_		:					Optional[int]			    =						image_mean
							snake_case_		:					Optional[Any]			    =						image_std






			def     lowerCamelCase						(self					)  ->     Any:


							'''simple docstring'''



							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "crop_pct": self.crop_pct,
							}




@require_torch
@require_vision
class       __lowerCAmelCase (    _a,  unittest.TestCase  ):
			lowerCamelCase_  :  Union[str, Any]     =   ConvNextImageProcessor if is_vision_available() else None






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


							'''simple docstring'''



							snake_case_		:					Optional[Any]			    =						ConvNextImageProcessingTester(self					)






			@property
			def     lowerCamelCase						(self					)  ->     List[str]:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def     lowerCamelCase						(self					)  ->     int:


							'''simple docstring'''



							snake_case_		:					int			    =						self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(__magic_name__      ,							'''do_resize'''					)					)
							self.assertTrue(hasattr(__magic_name__      ,							'''size'''					)					)
							self.assertTrue(hasattr(__magic_name__      ,							'''crop_pct'''					)					)
							self.assertTrue(hasattr(__magic_name__      ,							'''do_normalize'''					)					)
							self.assertTrue(hasattr(__magic_name__      ,							'''image_mean'''					)					)
							self.assertTrue(hasattr(__magic_name__      ,							'''image_std'''					)					)






			def     lowerCamelCase						(self					)  ->     Any:


							'''simple docstring'''



							snake_case_		:					Tuple			    =						self.image_processing_class.from_dict(self.image_processor_dict					)
							self.assertEqual(image_processor.size      ,							{'''shortest_edge''': 20}					)

							snake_case_		:					int			    =						self.image_processing_class.from_dict(self.image_processor_dict      ,							size=42					)
							self.assertEqual(image_processor.size      ,							{'''shortest_edge''': 42}					)






			def     lowerCamelCase						(self					)  ->     int:


							'''simple docstring'''



							pass






			def     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					List[Any]			    =						self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							snake_case_		:					Optional[int]			    =						prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=__magic_name__					)
							for image in image_inputs:
											self.assertIsInstance(__magic_name__      ,							Image.Image					)

							# Test not batched input
							snake_case_		:					str			    =						image_processing(image_inputs[0]      ,							return_tensors='''pt'''					).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,							(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''shortest_edge'''],
							        self.image_processor_tester.size['''shortest_edge'''],
							    )      ,							)

							# Test batched
							snake_case_		:					Dict			    =						image_processing(__magic_name__      ,							return_tensors='''pt'''					).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,							(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''shortest_edge'''],
							        self.image_processor_tester.size['''shortest_edge'''],
							    )      ,							)






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


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							snake_case_		:					Tuple			    =						prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=__magic_name__      ,							numpify=__magic_name__					)
							for image in image_inputs:
											self.assertIsInstance(__magic_name__      ,							np.ndarray					)

							# Test not batched input
							snake_case_		:					Optional[Any]			    =						image_processing(image_inputs[0]      ,							return_tensors='''pt'''					).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,							(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''shortest_edge'''],
							        self.image_processor_tester.size['''shortest_edge'''],
							    )      ,							)

							# Test batched
							snake_case_		:					Tuple			    =						image_processing(__magic_name__      ,							return_tensors='''pt'''					).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,							(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''shortest_edge'''],
							        self.image_processor_tester.size['''shortest_edge'''],
							    )      ,							)






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


							'''simple docstring'''



							snake_case_		:					str			    =						self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							snake_case_		:					str			    =						prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=__magic_name__      ,							torchify=__magic_name__					)
							for image in image_inputs:
											self.assertIsInstance(__magic_name__      ,							torch.Tensor					)

							# Test not batched input
							snake_case_		:					Dict			    =						image_processing(image_inputs[0]      ,							return_tensors='''pt'''					).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,							(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''shortest_edge'''],
							        self.image_processor_tester.size['''shortest_edge'''],
							    )      ,							)

							# Test batched
							snake_case_		:					Tuple			    =						image_processing(__magic_name__      ,							return_tensors='''pt'''					).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,							(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''shortest_edge'''],
							        self.image_processor_tester.size['''shortest_edge'''],
							    )      ,							)







def   lowerCamelCase_		(       _UpperCamelCase				)	->		int:






				"""simple docstring"""


				barcode //= 10  # exclude the last digit
				snake_case_		:					str			    =						False
				snake_case_		:					str			    =						0

				# extract and check each digit
				while barcode != 0:
								snake_case_		:					Any			    =						1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								snake_case_		:					Tuple			    =						not checker

				return (10 - (s % 10)) % 10






def   lowerCamelCase_		(       _UpperCamelCase				)	->		bool:






				"""simple docstring"""


				return len(str(_UpperCamelCase				)				) == 13 and get_check_digit(_UpperCamelCase				) == barcode % 10






def   lowerCamelCase_		(       _UpperCamelCase				)	->		int:






				"""simple docstring"""


				if str(_UpperCamelCase				).isalpha():
								snake_case_		:					Optional[Any]			    =						f'''Barcode \'{barcode}\' has alphabetic characters.'''
								raise ValueError(_UpperCamelCase				)
				elif int(_UpperCamelCase				) < 0:
								raise ValueError('''The entered barcode has a negative value. Try again.'''				)
				else:
								return int(_UpperCamelCase				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	lowerCAmelCase_												=       get_barcode(input('''Barcode: ''').strip())

	if is_valid(barcode):
		print(F'''\'{barcode}\' is a valid barcode.''')
	else:
		print(F'''\'{barcode}\' is NOT a valid barcode.''')
