





'''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 DeiTImageProcessor

class 				lowerCAmelCase_						(						unittest.TestCase						):




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase=7     ,       _lowerCAmelCase=3     ,       _lowerCAmelCase=18     ,       _lowerCAmelCase=30     ,       _lowerCAmelCase=400     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=[0.5, 0.5, 0.5]     ,       _lowerCAmelCase=[0.5, 0.5, 0.5]     ,       )      ->							Optional[Any]:
     _lowerCAmelCase		     = size if size is not None else {"height": 20, "width": 20}
     _lowerCAmelCase		     = crop_size if crop_size is not None else {"height": 18, "width": 18}

     _lowerCAmelCase		     = parent
     _lowerCAmelCase		     = batch_size
     _lowerCAmelCase		     = num_channels
     _lowerCAmelCase		     = image_size
     _lowerCAmelCase		     = min_resolution
     _lowerCAmelCase		     = max_resolution
     _lowerCAmelCase		     = do_resize
     _lowerCAmelCase		     = size
     _lowerCAmelCase		     = do_center_crop
     _lowerCAmelCase		     = crop_size
     _lowerCAmelCase		     = do_normalize
     _lowerCAmelCase		     = image_mean
     _lowerCAmelCase		     = image_std





 def 							_snake_case				(		self						)      ->							str:
     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,
     }







@require_torch
@require_vision
class 				lowerCAmelCase_						(						lowercase__  ,unittest.TestCase						):
 __lowerCamelCase  :							Any    					=   DeiTImageProcessor if is_vision_available() else None
 __lowerCamelCase  :							Dict    					=   True




 def 							_snake_case				(		self						)      ->							Any:
     _lowerCAmelCase		     = DeiTImageProcessingTester(self						)




 @property
 def 							_snake_case				(		self						)      ->							List[str]:
     return self.image_processor_tester.prepare_image_processor_dict()




 def 							_snake_case				(		self						)      ->							Optional[Any]:
     _lowerCAmelCase		     = 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"						)						)




 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size     ,       {"height": 20, "width": 20}						)
     self.assertEqual(image_processor.crop_size     ,       {"height": 18, "width": 18}						)

     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							List[Any]:
     pass




 def 							_snake_case				(		self						)      ->							str:
     _lowerCAmelCase		     = self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     _lowerCAmelCase		     = prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=lowerCAmelCase__						)
     for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase__     ,       Image.Image						)

     # Test not batched input
     _lowerCAmelCase		     = 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
     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							Dict:
     _lowerCAmelCase		     = self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     _lowerCAmelCase		     = 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
     _lowerCAmelCase		     = 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
     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							Tuple:
     _lowerCAmelCase		     = self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     _lowerCAmelCase		     = 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
     _lowerCAmelCase		     = 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
     _lowerCAmelCase		     = 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"],
         )     ,       )









'''simple docstring'''


import math


def 		__a(SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





    if number == 0:
        _lowerCAmelCase		     = "Zero"
        return words
    else:
        _lowerCAmelCase		     = math.logaa(SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = digits + 1
        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = "One"
        _lowerCAmelCase		     = "Two"
        _lowerCAmelCase		     = "Three"
        _lowerCAmelCase		     = "Four"
        _lowerCAmelCase		     = "Five"
        _lowerCAmelCase		     = "Six"
        _lowerCAmelCase		     = "Seven"
        _lowerCAmelCase		     = "Eight"
        _lowerCAmelCase		     = "Nine"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = "Twenty"
        _lowerCAmelCase		     = "Thirty"
        _lowerCAmelCase		     = "Forty"
        _lowerCAmelCase		     = "Fifty"
        _lowerCAmelCase		     = "Sixty"
        _lowerCAmelCase		     = "Seventy"
        _lowerCAmelCase		     = "Eighty"
        _lowerCAmelCase		     = "Ninety"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = "Ten"
        _lowerCAmelCase		     = "Eleven"
        _lowerCAmelCase		     = "Twelve"
        _lowerCAmelCase		     = "Thirteen"
        _lowerCAmelCase		     = "Fourteen"
        _lowerCAmelCase		     = "Fifteen"
        _lowerCAmelCase		     = "Sixteen"
        _lowerCAmelCase		     = "Seventeen"
        _lowerCAmelCase		     = "Eighteen"
        _lowerCAmelCase		     = "Nineteen"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = "Hundred,"
        _lowerCAmelCase		     = "Thousand,"
        _lowerCAmelCase		     = "Lakh,"
        _lowerCAmelCase		     = "Crore,"

        _lowerCAmelCase		     = number
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = 0
        _lowerCAmelCase		     = int(SCREAMING_SNAKE_CASE_      )
        while counter < digits:
            _lowerCAmelCase		     = temp_num % 10
            if counter % 2 == 0:
                _lowerCAmelCase		     = ""
                if counter in placevalue and current != 0:
                    _lowerCAmelCase		     = placevalue[counter]
                if counter == 2:
                    _lowerCAmelCase		     = singles[current] + addition + words
                elif counter == 0:
                    if ((temp_num % 100) // 10) == 1:
                        _lowerCAmelCase		     = teens[current] + addition + words
                        _lowerCAmelCase		     = temp_num // 10
                        counter += 1
                    else:
                        _lowerCAmelCase		     = singles[current] + addition + words

                else:
                    _lowerCAmelCase		     = doubles[current] + addition + words

            else:
                if counter == 1:
                    if current == 1:
                        _lowerCAmelCase		     = teens[number % 10] + words
                    else:
                        _lowerCAmelCase		     = ""
                        if counter in placevalue:
                            _lowerCAmelCase		     = placevalue[counter]
                        _lowerCAmelCase		     = doubles[current] + addition + words
                else:
                    _lowerCAmelCase		     = ""
                    if counter in placevalue:
                        if current == 0 and ((temp_num % 100) // 10) == 0:
                            _lowerCAmelCase		     = ""
                        else:
                            _lowerCAmelCase		     = placevalue[counter]
                    if ((temp_num % 100) // 10) == 1:
                        _lowerCAmelCase		     = teens[current] + addition + words
                        _lowerCAmelCase		     = temp_num // 10
                        counter += 1
                    else:
                        _lowerCAmelCase		     = singles[current] + addition + words
            counter += 1
            _lowerCAmelCase		     = temp_num // 10
    return words


if __name__ == "__main__":
       import doctest

       doctest.testmod()


