





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor



class      _SCREAMING_SNAKE_CASE		(			unittest.TestCase					):







 '''simple docstring'''

 def __init__(self       :     Dict ,				UpperCAmelCase_       :     str ,				UpperCAmelCase_       :     int=7 ,				UpperCAmelCase_       :     Optional[Any]=3 ,				UpperCAmelCase_       :     Tuple=18 ,				UpperCAmelCase_       :     Any=30 ,				UpperCAmelCase_       :     int=400 ,				UpperCAmelCase_       :     Optional[Any]=True ,				UpperCAmelCase_       :     int=None ,				UpperCAmelCase_       :     Union[str, Any]=True ,				UpperCAmelCase_       :     Any=False ,				UpperCAmelCase_       :     str=True ,				UpperCAmelCase_       :     Dict=True ,				UpperCAmelCase_       :     List[Any]=[0.5, 0.5, 0.5] ,				UpperCAmelCase_       :     Optional[Any]=[0.5, 0.5, 0.5] ,				) ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=parent
   lowerCamelCase__:   int    						=batch_size
   lowerCamelCase__:   Dict    						=num_channels
   lowerCamelCase__:   str    						=image_size
   lowerCamelCase__:   Any    						=min_resolution
   lowerCamelCase__:   Optional[Any]    						=max_resolution
   lowerCamelCase__:   List[Any]    						=do_resize
   lowerCamelCase__:   Tuple    						=size if size is not None else {"height": 18, "width": 20}
   lowerCamelCase__:   Optional[Any]    						=do_thumbnail
   lowerCamelCase__:   List[Any]    						=do_align_axis
   lowerCamelCase__:   Any    						=do_pad
   lowerCamelCase__:   Union[str, Any]    						=do_normalize
   lowerCamelCase__:   Optional[int]    						=image_mean
   lowerCamelCase__:   Tuple    						=image_std







 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->str:



   '''simple docstring'''



   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_thumbnail": self.do_thumbnail,
       "do_align_long_axis": self.do_align_axis,
       "do_pad": self.do_pad,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
   }



@require_torch
@require_vision
class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					,							unittest.TestCase					):







 '''simple docstring'''
 lowercase_	=    DonutImageProcessor if is_vision_available() else None

 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:   int    						=DonutImageProcessingTester(self)

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->Optional[Any]:



   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()

 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->Any:



   '''simple docstring'''



   lowerCamelCase__:   Any    						=self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(UpperCAmelCase_ ,				"do_resize"))
   self.assertTrue(hasattr(UpperCAmelCase_ ,				"size"))
   self.assertTrue(hasattr(UpperCAmelCase_ ,				"do_thumbnail"))
   self.assertTrue(hasattr(UpperCAmelCase_ ,				"do_align_long_axis"))
   self.assertTrue(hasattr(UpperCAmelCase_ ,				"do_pad"))
   self.assertTrue(hasattr(UpperCAmelCase_ ,				"do_normalize"))
   self.assertTrue(hasattr(UpperCAmelCase_ ,				"image_mean"))
   self.assertTrue(hasattr(UpperCAmelCase_ ,				"image_std"))

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any]) ->str:



   '''simple docstring'''



   lowerCamelCase__:   Any    						=self.image_processing_class.from_dict(self.image_processor_dict)
   self.assertEqual(image_processor.size ,				{"height": 18, "width": 20})

   lowerCamelCase__:   Any    						=self.image_processing_class.from_dict(self.image_processor_dict ,				size=42)
   self.assertEqual(image_processor.size ,				{"height": 42, "width": 42})

   # Previous config had dimensions in (width, height) order
   lowerCamelCase__:   Union[str, Any]    						=self.image_processing_class.from_dict(self.image_processor_dict ,				size=(42, 84))
   self.assertEqual(image_processor.size ,				{"height": 84, "width": 42})

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



   '''simple docstring'''



   pass

 @is_flaky()
 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int]) ->List[str]:



   '''simple docstring'''



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

   # Test not batched input
   lowerCamelCase__:   List[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.size["height"],
           self.image_processor_tester.size["width"],
       ) ,				)

   # Test batched
   lowerCamelCase__:   List[str]    						=image_processing(UpperCAmelCase_ ,				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.size["height"],
           self.image_processor_tester.size["width"],
       ) ,				)

 @is_flaky()
 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:   Optional[int]    						=self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowerCamelCase__:   List[Any]    						=prepare_image_inputs(self.image_processor_tester ,				equal_resolution=UpperCAmelCase_ ,				numpify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_ ,				np.ndarray)

   # Test not batched input
   lowerCamelCase__:   str    						=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.size["height"],
           self.image_processor_tester.size["width"],
       ) ,				)

   # Test batched
   lowerCamelCase__:   Optional[int]    						=image_processing(UpperCAmelCase_ ,				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.size["height"],
           self.image_processor_tester.size["width"],
       ) ,				)







 @is_flaky()
 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:   Tuple    						=self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowerCamelCase__:   Optional[int]    						=prepare_image_inputs(self.image_processor_tester ,				equal_resolution=UpperCAmelCase_ ,				torchify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_ ,				torch.Tensor)

   # Test not batched input
   lowerCamelCase__:   Tuple    						=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.size["height"],
           self.image_processor_tester.size["width"],
       ) ,				)

   # Test batched
   lowerCamelCase__:   List[Any]    						=image_processing(UpperCAmelCase_ ,				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.size["height"],
           self.image_processor_tester.size["width"],
       ) ,				)








from bisect import bisect
from itertools import accumulate





def   lowerCAmelCase_				( __a			,					__a			,					__a			,					__a	)  ->	List[Any]:







  """simple docstring"""





  lowerCamelCase__:   str    						=sorted(zip(__a			,					__a	)			,					key=lambda __a	: x[0] / x[1]			,					reverse=__a	)
  lowerCamelCase__						,      lowerCamelCase__:   str    						=[i[0] for i in r], [i[1] for i in r]
  lowerCamelCase__:   Tuple    						=list(accumulate(__a	)	)
  lowerCamelCase__:   Any    						=bisect(__a			,					__a	)
  return (
      0
      if k == 0
      else sum(vl[:k]	) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
      if k != n
      else sum(vl[:k]	)
  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

