



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 						lowercase__      (unittest.TestCase						):

     """simple docstring"""






     def __init__( self :	Union[str, Any]   ,			__a :	Tuple   ,			__a :	int=7   ,			__a :	Optional[int]=3   ,			__a :	Union[str, Any]=1_8   ,			__a :	int=3_0   ,			__a :	Tuple=4_0_0   ,			__a :	List[Any]=True   ,			__a :	int=None   ,			__a :	Optional[int]=True   ,			__a :	Dict=None   ,			__a :	List[Any]=True   ,			__a :	Optional[Any]=[0.5, 0.5, 0.5]   ,			__a :	Optional[int]=[0.5, 0.5, 0.5]   ,			):
      snake_case__				:							List[str]      							= size if size is not None else {"""shortest_edge""": 1_8}
      snake_case__				:							Tuple      							= crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
      snake_case__				:							Optional[int]      							= parent
      snake_case__				:							List[Any]      							= batch_size
      snake_case__				:							List[str]      							= num_channels
      snake_case__				:							str      							= image_size
      snake_case__				:							Tuple      							= min_resolution
      snake_case__				:							Optional[int]      							= max_resolution
      snake_case__				:							Dict      							= do_resize
      snake_case__				:							Union[str, Any]      							= size
      snake_case__				:							Optional[Any]      							= do_center_crop
      snake_case__				:							Optional[int]      							= crop_size
      snake_case__				:							Tuple      							= do_normalize
      snake_case__				:							Union[str, Any]      							= image_mean
      snake_case__				:							str      							= image_std


     def 		lowercase   ( self :	Union[str, Any] ):
      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 						lowercase__      (__snake_case						,      unittest.TestCase						):

     """simple docstring"""






     __UpperCamelCase    :	Optional[Any]		=       LevitImageProcessor if is_vision_available() else None


     def 		lowercase   ( self :	Union[str, Any] ):
      snake_case__				:							Optional[Any]      							= LevitImageProcessingTester(self )


     @property
     def 		lowercase   ( self :	Optional[int] ):
      return self.image_processor_tester.prepare_image_processor_dict()


     def 		lowercase   ( self :	int ):
      snake_case__				:							Tuple      							= self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(lowerCamelCase__   ,			"""image_mean""" ) )
      self.assertTrue(hasattr(lowerCamelCase__   ,			"""image_std""" ) )
      self.assertTrue(hasattr(lowerCamelCase__   ,			"""do_normalize""" ) )
      self.assertTrue(hasattr(lowerCamelCase__   ,			"""do_resize""" ) )
      self.assertTrue(hasattr(lowerCamelCase__   ,			"""do_center_crop""" ) )
      self.assertTrue(hasattr(lowerCamelCase__   ,			"""size""" ) )


     def 		lowercase   ( self :	int ):
      snake_case__				:							List[Any]      							= self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size   ,			{"""shortest_edge""": 1_8} )
      self.assertEqual(image_processor.crop_size   ,			{"""height""": 1_8, """width""": 1_8} )

      snake_case__				:							int      							= self.image_processing_class.from_dict(self.image_processor_dict   ,			size=4_2   ,			crop_size=8_4 )
      self.assertEqual(image_processor.size   ,			{"""shortest_edge""": 4_2} )
      self.assertEqual(image_processor.crop_size   ,			{"""height""": 8_4, """width""": 8_4} )


     def 		lowercase   ( self :	Dict ):
      pass


     def 		lowercase   ( self :	Union[str, Any] ):
      snake_case__				:							Any      							= self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      snake_case__				:							Union[str, Any]      							= prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=lowerCamelCase__ )
      for image in image_inputs:
       self.assertIsInstance(lowerCamelCase__   ,			Image.Image )

      # Test not batched input
      snake_case__				:							Union[str, Any]      							= 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
      snake_case__				:							Tuple      							= 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 		lowercase   ( self :	Union[str, Any] ):
      snake_case__				:							Any      							= self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      snake_case__				:							str      							= 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
      snake_case__				:							int      							= 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
      snake_case__				:							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 		lowercase   ( self :	str ):
      snake_case__				:							List[Any]      							= self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      snake_case__				:							Optional[int]      							= 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
      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
      snake_case__				:							Dict      							= 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      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: int )	->			list:





				"""simple docstring"""


				__lowerCamelCase									=       word.split()

				def justify(UpperCamelCase__							: list					,	UpperCamelCase__							: int					,	UpperCamelCase__							: int ) -> str:
								__lowerCamelCase									=       max_width - width
								__lowerCamelCase									=       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:
												__lowerCamelCase									=       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]
												__lowerCamelCase									=       spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowerCamelCase									=       (
												    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
												__lowerCamelCase									=       []
												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__ )

				__lowerCamelCase									=       []
				__lowerCamelCase									=       []
				__lowerCamelCase									=       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
												__lowerCamelCase			,		__lowerCamelCase									=       [word], len(UpperCamelCase__ )
				__lowerCamelCase									=       max_width - width - len(UpperCamelCase__ )
				answer.append(' '.join(UpperCamelCase__ ) + (remaining_spaces + 1) * ' ' )
				return answer


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

					testmod()


