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    A_ (       unittest.TestCase ):

  def __init__(		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      List[str]=7     ,							__SCREAMING_SNAKE_CASE :      int=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=18     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=30     ,							__SCREAMING_SNAKE_CASE :      Any=4_00     ,							__SCREAMING_SNAKE_CASE :      Dict=True     ,							__SCREAMING_SNAKE_CASE :      List[str]=None     ,							__SCREAMING_SNAKE_CASE :      Dict=True     ,							__SCREAMING_SNAKE_CASE :      int=False     ,							__SCREAMING_SNAKE_CASE :      Any=True     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=True     ,							__SCREAMING_SNAKE_CASE :      Tuple=[0.5, 0.5, 0.5]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=[0.5, 0.5, 0.5]     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  num_channels
   __a       =  image_size
   __a       =  min_resolution
   __a       =  max_resolution
   __a       =  do_resize
   __a       =  size if size is not None else {"height": 18, "width": 20}
   __a       =  do_thumbnail
   __a       =  do_align_axis
   __a       =  do_pad
   __a       =  do_normalize
   __a       =  image_mean
   __a       =  image_std

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   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    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			DonutImageProcessor if is_vision_available() else None

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  DonutImageProcessingTester(self       )

  @property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   return self.image_processor_tester.prepare_image_processor_dict()

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_resize"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"size"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_thumbnail"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_align_long_axis"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_pad"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_normalize"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"image_mean"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"image_std"       )       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  self.image_processing_class.from_dict(self.image_processor_dict       )
   self.assertEqual(image_processor.size     ,							{"height": 18, "width": 20}       )

   __a       =  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
   __a       =  self.image_processing_class.from_dict(self.image_processor_dict     ,							size=(42, 84)       )
   self.assertEqual(image_processor.size     ,							{"height": 84, "width": 42}       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   pass

  @is_flaky()
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __a       =  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
   __a       =  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
   __a       =  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.size["height"],
           self.image_processor_tester.size["width"],
       )     ,							)

  @is_flaky()
  def 		_UpperCAmelCase   (		self :      str       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random numpy tensors
   __a       =  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
   __a       =  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
   __a       =  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.size["height"],
           self.image_processor_tester.size["width"],
       )     ,							)

  @is_flaky()
  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PyTorch tensors
   __a       =  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
   __a       =  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
   __a       =  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.size["height"],
           self.image_processor_tester.size["width"],
       )     ,							)



from __future__ import annotations



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [True] * limit
 __a       =  False
 __a       =  False
 __a       =  True

 for i in range(3							,				int(limit**0.5 + 1					)							,				2					):
  __a       =  i * 2
  while index < limit:
   __a       =  False
   __a       =  index + i

 __a       =  [2]

 for i in range(3							,				_A							,				2					):
  if is_prime[i]:
   primes.append(_A					)

 return primes



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  prime_sieve(_A					)
 __a       =  0
 __a       =  0

 for i in range(len(_A					)					):
  for j in range(i + length							,				len(_A					)					):
   __a       =  sum(primes[i:j]					)
   if sol >= ceiling:
    break

   if sol in primes:
    __a       =  j - i
    __a       =  sol

 return largest


if __name__ == "__main__":
  print(f'''{solution() = }''')


