





'''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 MobileNetVaImageProcessor


class  a_					(    unittest.TestCase	):

    '''simple docstring'''


    def __init__(		self			,		A			,		A=7			,		A=3			,		A=18			,		A=30			,		A=400			,		A=True			,		A=None			,		A=True			,		A=None			,		)		->       Optional[int]:
        _SCREAMING_SNAKE_CASE     =      size if size is not None else {"""shortest_edge""": 20}
        _SCREAMING_SNAKE_CASE     =      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
        _SCREAMING_SNAKE_CASE     =      parent
        _SCREAMING_SNAKE_CASE     =      batch_size
        _SCREAMING_SNAKE_CASE     =      num_channels
        _SCREAMING_SNAKE_CASE     =      image_size
        _SCREAMING_SNAKE_CASE     =      min_resolution
        _SCREAMING_SNAKE_CASE     =      max_resolution
        _SCREAMING_SNAKE_CASE     =      do_resize
        _SCREAMING_SNAKE_CASE     =      size
        _SCREAMING_SNAKE_CASE     =      do_center_crop
        _SCREAMING_SNAKE_CASE     =      crop_size





    def       snake_case_(		self	)		->       Tuple:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }




@require_torch
@require_vision
class  a_					(    snake_case_				,   unittest.TestCase	):

    '''simple docstring'''
    UpperCamelCase			   =			MobileNetVaImageProcessor if is_vision_available() else None


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      MobileNetVaImageProcessingTester(self	)


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


    def       snake_case_(		self	)		->       Optional[int]:
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(A			,		"""do_resize"""	)	)
        self.assertTrue(hasattr(A			,		"""size"""	)	)
        self.assertTrue(hasattr(A			,		"""do_center_crop"""	)	)
        self.assertTrue(hasattr(A			,		"""center_crop"""	)	)


    def       snake_case_(		self	)		->       str:
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size			,		{"""shortest_edge""": 20}	)
        self.assertEqual(image_processor.crop_size			,		{"""height""": 18, """width""": 18}	)

        _SCREAMING_SNAKE_CASE     =      self.image_processing_class.from_dict(self.image_processor_dict			,		size=42			,		crop_size=84	)
        self.assertEqual(image_processor.size			,		{"""shortest_edge""": 42}	)
        self.assertEqual(image_processor.crop_size			,		{"""height""": 84, """width""": 84}	)


    def       snake_case_(		self	)		->       Dict:
        pass


    def       snake_case_(		self	)		->       Any:
        # Initialize image_processing
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _SCREAMING_SNAKE_CASE     =      prepare_image_inputs(self.image_processor_tester			,		equal_resolution=A	)
        for image in image_inputs:
            self.assertIsInstance(A			,		Image.Image	)

        # Test not batched input
        _SCREAMING_SNAKE_CASE     =      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     =      image_processing(A			,		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       snake_case_(		self	)		->       str:
        # Initialize image_processing
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        _SCREAMING_SNAKE_CASE     =      prepare_image_inputs(self.image_processor_tester			,		equal_resolution=A			,		numpify=A	)
        for image in image_inputs:
            self.assertIsInstance(A			,		np.ndarray	)

        # Test not batched input
        _SCREAMING_SNAKE_CASE     =      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     =      image_processing(A			,		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       snake_case_(		self	)		->       Union[str, Any]:
        # Initialize image_processing
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        _SCREAMING_SNAKE_CASE     =      prepare_image_inputs(self.image_processor_tester			,		equal_resolution=A			,		torchify=A	)
        for image in image_inputs:
            self.assertIsInstance(A			,		torch.Tensor	)

        # Test not batched input
        _SCREAMING_SNAKE_CASE     =      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     =      image_processing(A			,		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"""],
            )			,		)










'''simple docstring'''

def lowerCamelCase						(					__lowerCamelCase			:   int   )						->int:
    barcode //= 10  # exclude the last digit
    _SCREAMING_SNAKE_CASE     =      False
    _SCREAMING_SNAKE_CASE     =      0

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

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





def lowerCamelCase						(					__lowerCamelCase			:   int   )						->bool:
    return len(str(__lowerCamelCase   )   ) == 13 and get_check_digit(__lowerCamelCase   ) == barcode % 10





def lowerCamelCase						(					__lowerCamelCase			:   str   )						->int:
    if str(__lowerCamelCase   ).isalpha():
        _SCREAMING_SNAKE_CASE     =      F'Barcode \'{barcode}\' has alphabetic characters.'
        raise ValueError(__lowerCamelCase   )
    elif int(__lowerCamelCase   ) < 0:
        raise ValueError("""The entered barcode has a negative value. Try again."""   )
    else:
        return int(__lowerCamelCase   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase_         =			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.""")



