


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 OwlViTImageProcessor
class 	_lowercase				(      unittest.TestCase	):






				'''simple docstring'''



				def __init__(	self      :List[str]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :List[Any]=7      ,      lowerCAmelCase__      :List[Any]=3      ,      lowerCAmelCase__      :str=18      ,      lowerCAmelCase__      :List[str]=30      ,      lowerCAmelCase__      :List[Any]=400      ,      lowerCAmelCase__      :Optional[Any]=True      ,      lowerCAmelCase__      :List[str]=None      ,      lowerCAmelCase__      :List[Any]=True      ,      lowerCAmelCase__      :List[str]=None      ,      lowerCAmelCase__      :Dict=True      ,      lowerCAmelCase__      :Union[str, Any]=[0.4814_5466, 0.457_8275, 0.4082_1073]      ,      lowerCAmelCase__      :str=[0.2686_2954, 0.2613_0258, 0.2757_7711]      ,      lowerCAmelCase__      :Tuple=True      ,      )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		parent
										__SCREAMING_SNAKE_CASE						:					Any		      =		batch_size
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		num_channels
										__SCREAMING_SNAKE_CASE						:					int		      =		image_size
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		min_resolution
										__SCREAMING_SNAKE_CASE						:					int		      =		max_resolution
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		do_resize
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		size if size is not None else {'''height''': 18, '''width''': 18}
										__SCREAMING_SNAKE_CASE						:					int		      =		do_center_crop
										__SCREAMING_SNAKE_CASE						:					str		      =		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
										__SCREAMING_SNAKE_CASE						:					str		      =		do_normalize
										__SCREAMING_SNAKE_CASE						:					Dict		      =		image_mean
										__SCREAMING_SNAKE_CASE						:					int		      =		image_std
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		do_convert_rgb



				def 		__magic_name__(	self      :Tuple     )		->      Dict:
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}




@require_torch
@require_vision
class 	_lowercase				(      A__						,      unittest.TestCase	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		OwlViTImageProcessor if is_vision_available() else None


				def 		__magic_name__(	self      :int     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		OwlViTImageProcessingTester(self     )


				@property
				def 		__magic_name__(	self      :Any     )		->      Optional[Any]:
										return self.image_processor_tester.prepare_image_processor_dict()


				def 		__magic_name__(	self      :Any     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''do_resize'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''size'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''do_center_crop'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''center_crop'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''do_normalize'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''image_mean'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''image_std'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''do_convert_rgb'''     )     )


				def 		__magic_name__(	self      :str     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					int		      =		self.image_processing_class.from_dict(self.image_processor_dict     )
										self.assertEqual(image_processor.size      ,      {'''height''': 18, '''width''': 18}     )
										self.assertEqual(image_processor.crop_size      ,      {'''height''': 18, '''width''': 18}     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42      ,      crop_size=84     )
										self.assertEqual(image_processor.size      ,      {'''height''': 42, '''width''': 42}     )
										self.assertEqual(image_processor.crop_size      ,      {'''height''': 84, '''width''': 84}     )


				def 		__magic_name__(	self      :Union[str, Any]     )		->      str:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase__     )

										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__      ,      Image.Image     )

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,      )

										# Test batched
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		image_processing(lowerCAmelCase__      ,      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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,      )


				def 		__magic_name__(	self      :Optional[int]     )		->      Dict:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										__SCREAMING_SNAKE_CASE						:					int		      =		prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase__      ,      numpify=lowerCAmelCase__     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__      ,      np.ndarray     )

										# Test not batched input
										__SCREAMING_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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,      )

										# Test batched
										__SCREAMING_SNAKE_CASE						:					str		      =		image_processing(lowerCAmelCase__      ,      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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,      )



				def 		__magic_name__(	self      :Tuple     )		->      Union[str, Any]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase__      ,      torchify=lowerCAmelCase__     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__      ,      torch.Tensor     )

										# Test not batched input
										__SCREAMING_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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,      )

										# Test batched
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		image_processing(lowerCAmelCase__      ,      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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,      )




def 	_UpperCamelCase							(				lowercase__				):
						barcode //= 10  # exclude the last digit
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		False
						__SCREAMING_SNAKE_CASE						:					Dict		      =		0

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

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


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


def 	_UpperCamelCase							(				lowercase__				):
						if str(lowercase__				).isalpha():
												__SCREAMING_SNAKE_CASE						:					str		      =		F'''Barcode \'{barcode}\' has alphabetic characters.'''
												raise ValueError(lowercase__				)
						elif int(lowercase__				) < 0:
												raise ValueError('''The entered barcode has a negative value. Try again.'''				)
						else:
												return int(lowercase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase     :			Union[str, Any]      =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.""")
