
'''simple docstring'''







import random
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():
    import PIL

    from transformers import FlavaImageProcessor
    from transformers.image_utils import PILImageResampling
    from transformers.models.flava.image_processing_flava import (
        FLAVA_CODEBOOK_MEAN,
        FLAVA_CODEBOOK_STD,
        FLAVA_IMAGE_MEAN,
        FLAVA_IMAGE_STD,
    )
else:
    lowerCAmelCase_      =   None







class 		snake_case_       (						unittest.TestCase						):



   """simple docstring"""





   def __init__( self			,   UpperCamelCase			,   UpperCamelCase=7			,   UpperCamelCase=3			,   UpperCamelCase=30			,   UpperCamelCase=4_00			,   UpperCamelCase=True			,   UpperCamelCase=None			,   UpperCamelCase=True			,   UpperCamelCase=None			,   UpperCamelCase=None			,   UpperCamelCase=True			,   UpperCamelCase=1 / 2_55			,   UpperCamelCase=True			,   UpperCamelCase=FLAVA_IMAGE_MEAN			,   UpperCamelCase=FLAVA_IMAGE_STD			,   UpperCamelCase=14			,   UpperCamelCase=75			,   UpperCamelCase=None			,   UpperCamelCase=16			,   UpperCamelCase=0.3			,   UpperCamelCase=None			,   UpperCamelCase=True			,   UpperCamelCase=None			,   UpperCamelCase=None			,   UpperCamelCase=True			,   UpperCamelCase=None			,   UpperCamelCase=True			,   UpperCamelCase=True			,   UpperCamelCase=FLAVA_CODEBOOK_MEAN			,   UpperCamelCase=FLAVA_CODEBOOK_STD			,   ):
          lowerCamelCase__				   =      size if size is not None else {"height": 2_24, "width": 2_24}
          lowerCamelCase__				   =      crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          lowerCamelCase__				   =      codebook_size if codebook_size is not None else {"height": 1_12, "width": 1_12}
          lowerCamelCase__				   =      codebook_crop_size if codebook_crop_size is not None else {"height": 1_12, "width": 1_12}

          lowerCamelCase__				   =      parent
          lowerCamelCase__				   =      batch_size
          lowerCamelCase__				   =      num_channels
          lowerCamelCase__				   =      do_resize
          lowerCamelCase__				   =      do_rescale
          lowerCamelCase__				   =      rescale_factor
          lowerCamelCase__				   =      min_resolution
          lowerCamelCase__				   =      max_resolution
          lowerCamelCase__				   =      size
          lowerCamelCase__				   =      resample if resample is not None else PILImageResampling.BICUBIC
          lowerCamelCase__				   =      do_normalize
          lowerCamelCase__				   =      image_mean
          lowerCamelCase__				   =      image_std
          lowerCamelCase__				   =      do_center_crop
          lowerCamelCase__				   =      crop_size

          lowerCamelCase__				   =      input_size_patches
          lowerCamelCase__				   =      total_mask_patches
          lowerCamelCase__				   =      mask_group_max_patches
          lowerCamelCase__				   =      mask_group_min_patches
          lowerCamelCase__				   =      mask_group_min_aspect_ratio
          lowerCamelCase__				   =      mask_group_max_aspect_ratio

          lowerCamelCase__				   =      codebook_do_resize
          lowerCamelCase__				   =      codebook_size
          lowerCamelCase__				   =      codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
          lowerCamelCase__				   =      codebook_do_center_crop
          lowerCamelCase__				   =      codebook_crop_size
          lowerCamelCase__				   =      codebook_do_map_pixels
          lowerCamelCase__				   =      codebook_do_normalize
          lowerCamelCase__				   =      codebook_image_mean
          lowerCamelCase__				   =      codebook_image_std







   def     __UpperCAmelCase      ( self):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "resample": self.resample,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "input_size_patches": self.input_size_patches,
              "total_mask_patches": self.total_mask_patches,
              "mask_group_max_patches": self.mask_group_max_patches,
              "mask_group_min_patches": self.mask_group_min_patches,
              "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
              "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
              "codebook_do_resize": self.codebook_do_resize,
              "codebook_size": self.codebook_size,
              "codebook_resample": self.codebook_resample,
              "codebook_do_center_crop": self.codebook_do_center_crop,
              "codebook_crop_size": self.codebook_crop_size,
              "codebook_do_map_pixels": self.codebook_do_map_pixels,
              "codebook_do_normalize": self.codebook_do_normalize,
              "codebook_image_mean": self.codebook_image_mean,
              "codebook_image_std": self.codebook_image_std,
          }







   def     __UpperCAmelCase      ( self):
          return (self.size["height"], self.size["width"])







   def     __UpperCAmelCase      ( self):
          return (
              (self.input_size_patches, self.input_size_patches)
              if not isinstance(self.input_size_patches			,   A__)
              else self.input_size_patches
          )







   def     __UpperCAmelCase      ( self):
          return (self.codebook_size["height"], self.codebook_size["width"])







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



   """simple docstring"""





   __lowerCAmelCase	:							Optional[Any]								=FlavaImageProcessor if is_vision_available() else None
   __lowerCAmelCase	:							Dict								=None







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      FlavaImageProcessingTester(self)







   @property
   def     __UpperCAmelCase      ( self):
          return self.image_processor_tester.prepare_image_processor_dict()







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(A__			,   "image_mean"))
          self.assertTrue(hasattr(A__			,   "image_std"))
          self.assertTrue(hasattr(A__			,   "do_normalize"))
          self.assertTrue(hasattr(A__			,   "do_resize"))
          self.assertTrue(hasattr(A__			,   "resample"))
          self.assertTrue(hasattr(A__			,   "crop_size"))
          self.assertTrue(hasattr(A__			,   "do_center_crop"))
          self.assertTrue(hasattr(A__			,   "do_rescale"))
          self.assertTrue(hasattr(A__			,   "rescale_factor"))
          self.assertTrue(hasattr(A__			,   "masking_generator"))
          self.assertTrue(hasattr(A__			,   "codebook_do_resize"))
          self.assertTrue(hasattr(A__			,   "codebook_size"))
          self.assertTrue(hasattr(A__			,   "codebook_resample"))
          self.assertTrue(hasattr(A__			,   "codebook_do_center_crop"))
          self.assertTrue(hasattr(A__			,   "codebook_crop_size"))
          self.assertTrue(hasattr(A__			,   "codebook_do_map_pixels"))
          self.assertTrue(hasattr(A__			,   "codebook_do_normalize"))
          self.assertTrue(hasattr(A__			,   "codebook_image_mean"))
          self.assertTrue(hasattr(A__			,   "codebook_image_std"))







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size			,   {"height": 2_24, "width": 2_24})
          self.assertEqual(image_processor.crop_size			,   {"height": 2_24, "width": 2_24})
          self.assertEqual(image_processor.codebook_size			,   {"height": 1_12, "width": 1_12})
          self.assertEqual(image_processor.codebook_crop_size			,   {"height": 1_12, "width": 1_12})

          lowerCamelCase__				   =      self.image_processing_class.from_dict(
              self.image_processor_dict			,   size=42			,   crop_size=84			,   codebook_size=33			,   codebook_crop_size=66)
          self.assertEqual(image_processor.size			,   {"height": 42, "width": 42})
          self.assertEqual(image_processor.crop_size			,   {"height": 84, "width": 84})
          self.assertEqual(image_processor.codebook_size			,   {"height": 33, "width": 33})
          self.assertEqual(image_processor.codebook_crop_size			,   {"height": 66, "width": 66})







   def     __UpperCAmelCase      ( self):
          pass







   def     __UpperCAmelCase      ( self):
          # Initialize image_processing
          lowerCamelCase__				   =      self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          lowerCamelCase__				   =      prepare_image_inputs(self.image_processor_tester			,   equal_resolution=A__)
          for image in image_inputs:
                 self.assertIsInstance(A__			,   PIL.Image.Image)

          # Test not batched input
          lowerCamelCase__				   =      image_processing(image_inputs[0]			,   return_tensors="pt")

          # Test no bool masked pos
          self.assertFalse("bool_masked_pos" in encoded_images)

          lowerCamelCase__   ,		lowerCamelCase__				   =      self.image_processor_tester.get_expected_image_size()

          self.assertEqual(
              encoded_images.pixel_values.shape			,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,   )

          # Test batched
          lowerCamelCase__				   =      image_processing(A__			,   return_tensors="pt")
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.image_processor_tester.get_expected_image_size()

          # Test no bool masked pos
          self.assertFalse("bool_masked_pos" in encoded_images)

          self.assertEqual(
              encoded_images.pixel_values.shape			,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			,   )







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase):
          # Initialize image_processing
          lowerCamelCase__				   =      self.image_processing_class(**self.image_processor_dict)
          # create random tensors
          lowerCamelCase__				   =      prepare_image_inputs(self.image_processor_tester			,   equal_resolution=A__			,   **A__)
          for image in image_inputs:
                 self.assertIsInstance(A__			,   A__)

          # Test not batched input
          lowerCamelCase__				   =      image_processing(image_inputs[0]			,   return_tensors="pt")

          lowerCamelCase__   ,		lowerCamelCase__				   =      self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.pixel_values.shape			,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,   )

          lowerCamelCase__				   =      image_processing(A__			,   return_image_mask=A__			,   return_tensors="pt")

          lowerCamelCase__   ,		lowerCamelCase__				   =      self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.pixel_values.shape			,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			,   )

          lowerCamelCase__   ,		lowerCamelCase__				   =      self.image_processor_tester.get_expected_mask_size()
          self.assertEqual(
              encoded_images.bool_masked_pos.shape			,   (
                  self.image_processor_tester.batch_size,
                  expected_height,
                  expected_width,
              )			,   )

          # Test batched
          lowerCamelCase__				   =      image_processing(A__			,   return_tensors="pt").pixel_values

          lowerCamelCase__   ,		lowerCamelCase__				   =      self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.shape			,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			,   )

          # Test masking
          lowerCamelCase__				   =      image_processing(A__			,   return_image_mask=A__			,   return_tensors="pt")

          lowerCamelCase__   ,		lowerCamelCase__				   =      self.image_processor_tester.get_expected_image_size()
          self.assertEqual(
              encoded_images.pixel_values.shape			,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			,   )

          lowerCamelCase__   ,		lowerCamelCase__				   =      self.image_processor_tester.get_expected_mask_size()
          self.assertEqual(
              encoded_images.bool_masked_pos.shape			,   (
                  self.image_processor_tester.batch_size,
                  expected_height,
                  expected_width,
              )			,   )







   def     __UpperCAmelCase      ( self):
          self._test_call_framework(np.ndarray			,   prepare_kwargs={"numpify": True})







   def     __UpperCAmelCase      ( self):
          self._test_call_framework(torch.Tensor			,   prepare_kwargs={"torchify": True})







   def     __UpperCAmelCase      ( self):
          # Initialize image_processing
          random.seed(12_34)
          lowerCamelCase__				   =      self.image_processing_class(**self.image_processor_dict)
          lowerCamelCase__				   =      prepare_image_inputs(self.image_processor_tester			,   equal_resolution=A__			,   torchify=A__)

          # Test not batched input
          lowerCamelCase__				   =      image_processing(image_inputs[0]			,   return_image_mask=A__			,   return_tensors="pt")
          self.assertEqual(encoded_images.bool_masked_pos.sum().item()			,   75)







   def     __UpperCAmelCase      ( self):
          # Initialize image_processing
          lowerCamelCase__				   =      self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          lowerCamelCase__				   =      prepare_image_inputs(self.image_processor_tester			,   equal_resolution=A__)
          for image in image_inputs:
                 self.assertIsInstance(A__			,   PIL.Image.Image)

          # Test not batched input
          lowerCamelCase__				   =      image_processing(image_inputs[0]			,   return_codebook_pixels=A__			,   return_tensors="pt")
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.image_processor_tester.get_expected_codebook_image_size()
          self.assertEqual(
              encoded_images.codebook_pixel_values.shape			,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,   )

          # Test batched
          lowerCamelCase__				   =      image_processing(A__			,   return_codebook_pixels=A__			,   return_tensors="pt")
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.image_processor_tester.get_expected_codebook_image_size()
          self.assertEqual(
              encoded_images.codebook_pixel_values.shape			,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			,   )







import math

def       lowerCAmelCase_      (	lowerCamelCase:							int    )       ->      str:


      '''simple docstring'''




      if number == 0:
            lowerCamelCase__   	=					'Zero'
            return words
      else:
            lowerCamelCase__   	=					math.logaa(lowerCamelCase    )
            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(lowerCamelCase    )
            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 % 1_00) // 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 % 1_00) // 10) == 0:
                                          lowerCamelCase__   	=					''
                                    else:
                                          lowerCamelCase__   	=					placevalue[counter]
                              if ((temp_num % 1_00) // 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()