
"""simple docstring"""
import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor
class 					a	(  unittest.TestCase       ):



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=size if size is not None else {"""height""": 20, """width""": 20}
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
     SCREAMING_SNAKE_CASE_: List[Any]     			=parent
     SCREAMING_SNAKE_CASE_: List[Any]     			=batch_size
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=num_channels
     SCREAMING_SNAKE_CASE_: List[Any]     			=image_size
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=min_resolution
     SCREAMING_SNAKE_CASE_: List[Any]     			=max_resolution
     SCREAMING_SNAKE_CASE_: str     			=do_resize
     SCREAMING_SNAKE_CASE_: str     			=size
     SCREAMING_SNAKE_CASE_: str     			=do_center_crop
     SCREAMING_SNAKE_CASE_: int     			=crop_size
     SCREAMING_SNAKE_CASE_: Dict     			=do_normalize
     SCREAMING_SNAKE_CASE_: List[str]     			=image_mean
     SCREAMING_SNAKE_CASE_: Any     			=image_std
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=do_reduce_labels





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


     '''simple docstring'''




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







def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: List[str]     			=load_dataset("""hf-internal-testing/fixtures_ade20k"""							,			split="""test""" )

   SCREAMING_SNAKE_CASE_: List[str]     			=Image.open(dataset[0]["""file"""] )
   SCREAMING_SNAKE_CASE_: str     			=Image.open(dataset[1]["""file"""] )

   return image, map





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: str     			=load_dataset("""hf-internal-testing/fixtures_ade20k"""							,			split="""test""" )

   SCREAMING_SNAKE_CASE_: str     			=Image.open(ds[0]["""file"""] )
   SCREAMING_SNAKE_CASE_: Tuple     			=Image.open(ds[1]["""file"""] )
   SCREAMING_SNAKE_CASE_: Tuple     			=Image.open(ds[2]["""file"""] )
   SCREAMING_SNAKE_CASE_: List[str]     			=Image.open(ds[3]["""file"""] )

   return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class 					a	(  __UpperCAmelCase	,      unittest.TestCase       ):
  UpperCamelCase	:				Union[str, Any]     						=					BeitImageProcessor if is_vision_available() else None



  def 	lowerCamelCase__       (						self :					int		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=BeitImageProcessingTester(self		)



  @property
  def 	lowerCamelCase__       (						self :					int		)     ->					Optional[int]:


     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=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_center_crop"""		)		)
     self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,		"""center_crop"""		)		)
     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 	lowerCamelCase__       (						self :					str		)     ->					Dict:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: Tuple     			=self.image_processing_class.from_dict(
         self.image_processor_dict       ,		size=42       ,		crop_size=84       ,		reduce_labels=__SCREAMING_SNAKE_CASE		)
     self.assertEqual(image_processor.size       ,		{"""height""": 42, """width""": 42}		)
     self.assertEqual(image_processor.crop_size       ,		{"""height""": 84, """width""": 84}		)
     self.assertEqual(image_processor.do_reduce_labels       ,		__SCREAMING_SNAKE_CASE		)



  def 	lowerCamelCase__       (						self :					Dict		)     ->					List[str]:


     '''simple docstring'''




     pass



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=self.image_processing_class(**self.image_processor_dict		)
     # create random PIL images
     SCREAMING_SNAKE_CASE_: Dict     			=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
     SCREAMING_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
     SCREAMING_SNAKE_CASE_: Optional[int]     			=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 	lowerCamelCase__       (						self :					int		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=self.image_processing_class(**self.image_processor_dict		)
     # create random numpy tensors
     SCREAMING_SNAKE_CASE_: Optional[int]     			=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
     SCREAMING_SNAKE_CASE_: Optional[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
     SCREAMING_SNAKE_CASE_: Dict     			=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 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.image_processing_class(**self.image_processor_dict		)
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE_: Any     			=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
     SCREAMING_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
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=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 	lowerCamelCase__       (						self :					Dict		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.image_processing_class(**self.image_processor_dict		)
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE_: Optional[int]     			=prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=__SCREAMING_SNAKE_CASE       ,		torchify=__SCREAMING_SNAKE_CASE		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=[]
     for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,		torch.Tensor		)
        maps.append(torch.zeros(image.shape[-2:]		).long()		)

     # Test not batched input
     SCREAMING_SNAKE_CASE_: List[Any]     			=image_processing(image_inputs[0]       ,		maps[0]       ,		return_tensors="""pt"""		)
     self.assertEqual(
         encoding["""pixel_values"""].shape       ,		(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )       ,		)
     self.assertEqual(
         encoding["""labels"""].shape       ,		(
             1,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )       ,		)
     self.assertEqual(encoding["""labels"""].dtype       ,		torch.long		)
     self.assertTrue(encoding["""labels"""].min().item() >= 0		)
     self.assertTrue(encoding["""labels"""].max().item() <= 255		)

     # Test batched
     SCREAMING_SNAKE_CASE_: str     			=image_processing(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		return_tensors="""pt"""		)
     self.assertEqual(
         encoding["""pixel_values"""].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"""],
         )       ,		)
     self.assertEqual(
         encoding["""labels"""].shape       ,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )       ,		)
     self.assertEqual(encoding["""labels"""].dtype       ,		torch.long		)
     self.assertTrue(encoding["""labels"""].min().item() >= 0		)
     self.assertTrue(encoding["""labels"""].max().item() <= 255		)

     # Test not batched input (PIL images)
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[Any]     			=prepare_semantic_single_inputs()

     SCREAMING_SNAKE_CASE_: Any     			=image_processing(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		return_tensors="""pt"""		)
     self.assertEqual(
         encoding["""pixel_values"""].shape       ,		(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )       ,		)
     self.assertEqual(
         encoding["""labels"""].shape       ,		(
             1,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )       ,		)
     self.assertEqual(encoding["""labels"""].dtype       ,		torch.long		)
     self.assertTrue(encoding["""labels"""].min().item() >= 0		)
     self.assertTrue(encoding["""labels"""].max().item() <= 255		)

     # Test batched input (PIL images)
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Any     			=prepare_semantic_batch_inputs()

     SCREAMING_SNAKE_CASE_: Any     			=image_processing(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		return_tensors="""pt"""		)
     self.assertEqual(
         encoding["""pixel_values"""].shape       ,		(
             2,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )       ,		)
     self.assertEqual(
         encoding["""labels"""].shape       ,		(
             2,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )       ,		)
     self.assertEqual(encoding["""labels"""].dtype       ,		torch.long		)
     self.assertTrue(encoding["""labels"""].min().item() >= 0		)
     self.assertTrue(encoding["""labels"""].max().item() <= 255		)





  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=self.image_processing_class(**self.image_processor_dict		)

     # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=prepare_semantic_single_inputs()
     SCREAMING_SNAKE_CASE_: Dict     			=image_processing(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		return_tensors="""pt"""		)
     self.assertTrue(encoding["""labels"""].min().item() >= 0		)
     self.assertTrue(encoding["""labels"""].max().item() <= 150		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=True
     SCREAMING_SNAKE_CASE_: str     			=image_processing(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		return_tensors="""pt"""		)
     self.assertTrue(encoding["""labels"""].min().item() >= 0		)
     self.assertTrue(encoding["""labels"""].max().item() <= 255		)








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   return str(lowercase ) == str(lowercase )[::-1]





def      __magic_name__				(							lowercase ):
   return int(lowercase ) + int(str(lowercase )[::-1] )





def      __magic_name__				(							lowercase = 1_0000 ):
   SCREAMING_SNAKE_CASE_: List[str]     			=[]
   for num in range(1							,			lowercase ):
      SCREAMING_SNAKE_CASE_: List[Any]     			=0
      SCREAMING_SNAKE_CASE_: int     			=num
      while iterations < 50:
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=sum_reverse(lowercase )
         iterations += 1
         if is_palindrome(lowercase ):
            break
      else:
         lychrel_nums.append(lowercase )
   return len(lowercase )


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



