






'''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 LevitImageProcessor






class     a_						(      unittest.TestCase    ):

		def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=7      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=18      ,		_SCREAMING_SNAKE_CASE=30      ,		_SCREAMING_SNAKE_CASE=400      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		)  ->				List[str]:



								"""simple docstring"""






								UpperCamelCase      = size if size is not None else {"""shortest_edge""": 18}
								UpperCamelCase      = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								UpperCamelCase      = parent
								UpperCamelCase      = batch_size
								UpperCamelCase      = num_channels
								UpperCamelCase      = image_size
								UpperCamelCase      = min_resolution
								UpperCamelCase      = max_resolution
								UpperCamelCase      = do_resize
								UpperCamelCase      = size
								UpperCamelCase      = do_center_crop
								UpperCamelCase      = crop_size
								UpperCamelCase      = do_normalize
								UpperCamelCase      = image_mean
								UpperCamelCase      = image_std


		def   A__						(				self				)  ->				int:



								"""simple docstring"""






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






@require_torch
@require_vision
class     a_						(      a_     ,	unittest.TestCase    ):
		lowercase							=				LevitImageProcessor if is_vision_available() else None

		def   A__						(				self				)  ->				Tuple:



								"""simple docstring"""






								UpperCamelCase      = LevitImageProcessingTester(self				)

		@property
		def   A__						(				self				)  ->				int:



								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()

		def   A__						(				self				)  ->				Union[str, Any]:



								"""simple docstring"""






								UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""image_mean"""				)				)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""image_std"""				)				)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_normalize"""				)				)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_resize"""				)				)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_center_crop"""				)				)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""size"""				)				)

		def   A__						(				self				)  ->				Any:



								"""simple docstring"""






								UpperCamelCase      = self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size      ,		{"""shortest_edge""": 18}				)
								self.assertEqual(image_processor.crop_size      ,		{"""height""": 18, """width""": 18}				)

								UpperCamelCase      = 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   A__						(				self				)  ->				Dict:



								"""simple docstring"""






								pass

		def   A__						(				self				)  ->				Tuple:



								"""simple docstring"""






								UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								UpperCamelCase      = prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE				)
								for image in image_inputs:
														self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		Image.Image				)

								# Test not batched input
								UpperCamelCase      = 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
								UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		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   A__						(				self				)  ->				Dict:



								"""simple docstring"""






								UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
								# create random numpy tensors
								UpperCamelCase      = prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE      ,		numpify=_SCREAMING_SNAKE_CASE				)
								for image in image_inputs:
														self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		np.ndarray				)

								# Test not batched input
								UpperCamelCase      = 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
								UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		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   A__						(				self				)  ->				Dict:



								"""simple docstring"""






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

								# Test not batched input
								UpperCamelCase      = 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
								UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			list:
      UpperCamelCase      = word.split()

      def justify(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   ) -> str:
            UpperCamelCase      = max_width - width
            UpperCamelCase      = len(__UpperCamelCase   )
            if len(__UpperCamelCase   ) == 1:
                  # if there is only word in line
                  # just insert overall_spaces_count for the remainder of line
                  return line[0] + " " * overall_spaces_count
            else:
                  UpperCamelCase      = words_count - 1
                  # num_spaces_between_words_list[i] : tells you to insert
                  # num_spaces_between_words_list[i] spaces
                  # after word on line[i]
                  UpperCamelCase      = spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  UpperCamelCase      = (
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(__UpperCamelCase   ):
                        num_spaces_between_words_list[i] += 1
                  UpperCamelCase      = []
                  for i in range(__UpperCamelCase   ):
                        # add the word
                        aligned_words_list.append(line[i]   )
                        # add the spaces to insert
                        aligned_words_list.append(num_spaces_between_words_list[i] * """ """   )
                  # just add the last word to the sentence
                  aligned_words_list.append(line[-1]   )
                  # join the aligned words list to form a justified line
                  return "".join(__UpperCamelCase   )

      UpperCamelCase      = []
      UpperCamelCase      = []
      UpperCamelCase      = 0
      for word in words:
            if width + len(__UpperCamelCase   ) + len(__UpperCamelCase   ) <= max_width:
                  # keep adding words until we can fill out max_width
                  # width = sum of length of all words (without overall_spaces_count)
                  # len(word) = length of current word
                  # len(line) = number of overall_spaces_count to insert between words
                  line.append(__UpperCamelCase   )
                  width += len(__UpperCamelCase   )
            else:
                  # justify the line and add it to result
                  answer.append(justify(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )   )
                  # reset new line and new width
                  UpperCamelCase  ,UpperCamelCase      = [word], len(__UpperCamelCase   )
      UpperCamelCase      = max_width - width - len(__UpperCamelCase   )
      answer.append(""" """.join(__UpperCamelCase   ) + (remaining_spaces + 1) * """ """   )
      return answer


if __name__ == "__main__":
     from doctest import testmod

     testmod()
