




import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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 transformers import OneFormerImageProcessor
  from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
  from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
 from PIL import Image

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[str, Any]	,							lowercase	:       Tuple="shi-labs/oneformer_demo"     ):


      '''simple docstring'''



      with open(hf_hub_download(lowercase	,							lowercase	,							repo_type='dataset'     )	,							'r'     ) as f:
            lowerCamelCase_		     =    json.load(lowercase     )
      lowerCamelCase_		     =    {}
      lowerCamelCase_		     =    []
      lowerCamelCase_		     =    []
      for key, info in class_info.items():
            lowerCamelCase_		     =    info['name']
            class_names.append(info['name']     )
            if info["isthing"]:
                  thing_ids.append(int(lowercase     )     )
      lowerCamelCase_		     =    thing_ids
      lowerCamelCase_		     =    class_names
      return metadata







class   A(						unittest.TestCase   ):



 '''simple docstring'''


 def __init__(				self					:  List[Any]						,							A_					:  Union[str, Any]						,							A_					:  List[str]=7						,							A_					:  Optional[Any]=3						,							A_					:  List[Any]=30						,							A_					:  List[Any]=400						,							A_					:  List[str]=None						,							A_					:  Optional[Any]=True						,							A_					:  str=True						,							A_					:  Optional[int]=[0.5, 0.5, 0.5]						,							A_					:  Optional[int]=[0.5, 0.5, 0.5]						,							A_					:  Any=10						,							A_					:  Union[str, Any]=False						,							A_					:  List[str]=255						,							A_					:  Any="shi-labs/oneformer_demo"						,							A_					:  List[Any]="ade20k_panoptic.json"						,							A_					:  List[Any]=10						,							)   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    parent
       lowerCamelCase_		     =    batch_size
       lowerCamelCase_		     =    num_channels
       lowerCamelCase_		     =    min_resolution
       lowerCamelCase_		     =    max_resolution
       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    {'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean
       lowerCamelCase_		     =    image_std
       lowerCamelCase_		     =    class_info_file
       lowerCamelCase_		     =    prepare_metadata(A_						,							A_  )
       lowerCamelCase_		     =    num_text
       lowerCamelCase_		     =    repo_path

       # for the post_process_functions
       lowerCamelCase_		     =    2
       lowerCamelCase_		     =    10
       lowerCamelCase_		     =    10
       lowerCamelCase_		     =    3
       lowerCamelCase_		     =    4
       lowerCamelCase_		     =    num_labels
       lowerCamelCase_		     =    do_reduce_labels
       lowerCamelCase_		     =    ignore_index

 def 				a__ (				self					:  Tuple  )   ->	Optional[int]:

       """simple docstring"""






       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "num_labels": self.num_labels,
           "do_reduce_labels": self.do_reduce_labels,
           "ignore_index": self.ignore_index,
           "class_info_file": self.class_info_file,
           "metadata": self.metadata,
           "num_text": self.num_text,
       }

 def 				a__ (				self					:  List[str]						,							A_					:  Dict						,							A_					:  Optional[int]=False  )   ->	Optional[Any]:

       """simple docstring"""






       if not batched:
             lowerCamelCase_		     =    image_inputs[0]
             if isinstance(A_						,							Image.Image  ):
                   lowerCamelCase_     ,				lowerCamelCase_		     =    image.size
             else:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    image.shape[1], image.shape[2]
             if w < h:
                   lowerCamelCase_		     =    int(self.size['shortest_edge'] * h / w  )
                   lowerCamelCase_		     =    self.size['shortest_edge']
             elif w > h:
                   lowerCamelCase_		     =    self.size['shortest_edge']
                   lowerCamelCase_		     =    int(self.size['shortest_edge'] * w / h  )
             else:
                   lowerCamelCase_		     =    self.size['shortest_edge']
                   lowerCamelCase_		     =    self.size['shortest_edge']

       else:
             lowerCamelCase_		     =    []
             for image in image_inputs:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    self.get_expected_values([image]  )
                   expected_values.append((expected_height, expected_width)  )
             lowerCamelCase_		     =    max(A_						,							key=lambda A_  : item[0]  )[0]
             lowerCamelCase_		     =    max(A_						,							key=lambda A_  : item[1]  )[1]

       return expected_height, expected_width





 def 				a__ (				self					:  List[str]  )   ->	Any:

       """simple docstring"""






       return OneFormerForUniversalSegmentationOutput(
           # +1 for null class
           class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)  )						,							masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)  )						,							)







@require_torch
@require_vision
class   A(						UpperCamelCase							,      unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
 # only for test_image_processing_common.test_image_proc_to_json_string
 UpperCamelCase						   =						image_processing_class

 def 				a__ (				self					:  Optional[Any]  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    OneFormerImageProcessorTester(self  )

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

       """simple docstring"""






       return self.image_processing_tester.prepare_image_processor_dict()

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

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(A_						,							'image_mean'  )  )
       self.assertTrue(hasattr(A_						,							'image_std'  )  )
       self.assertTrue(hasattr(A_						,							'do_normalize'  )  )
       self.assertTrue(hasattr(A_						,							'do_resize'  )  )
       self.assertTrue(hasattr(A_						,							'size'  )  )
       self.assertTrue(hasattr(A_						,							'ignore_index'  )  )
       self.assertTrue(hasattr(A_						,							'class_info_file'  )  )
       self.assertTrue(hasattr(A_						,							'num_text'  )  )
       self.assertTrue(hasattr(A_						,							'repo_path'  )  )
       self.assertTrue(hasattr(A_						,							'metadata'  )  )
       self.assertTrue(hasattr(A_						,							'do_reduce_labels'  )  )

 def 				a__ (				self					:  Dict  )   ->	int:

       """simple docstring"""






       pass

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

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       lowerCamelCase_		     =    prepare_image_inputs(self.image_processing_tester						,							equal_resolution=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							Image.Image  )

       # Test not batched input
       lowerCamelCase_		     =    image_processor(image_inputs[0]						,							['semantic']						,							return_tensors='pt'  ).pixel_values

       lowerCamelCase_     ,				lowerCamelCase_		     =    self.image_processing_tester.get_expected_values(A_  )

       self.assertEqual(
           encoded_images.shape						,							(1, self.image_processing_tester.num_channels, expected_height, expected_width)						,							)

       # Test batched
       lowerCamelCase_     ,				lowerCamelCase_		     =    self.image_processing_tester.get_expected_values(A_						,							batched=A_  )

       lowerCamelCase_		     =    image_processor(
           A_						,							['semantic'] * len(A_  )						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )						,							)

 def 				a__ (				self					:  str  )   ->	Dict:

       """simple docstring"""






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

       # Test not batched input
       lowerCamelCase_		     =    image_processor(image_inputs[0]						,							['semantic']						,							return_tensors='pt'  ).pixel_values

       lowerCamelCase_     ,				lowerCamelCase_		     =    self.image_processing_tester.get_expected_values(A_  )

       self.assertEqual(
           encoded_images.shape						,							(1, self.image_processing_tester.num_channels, expected_height, expected_width)						,							)

       # Test batched
       lowerCamelCase_     ,				lowerCamelCase_		     =    self.image_processing_tester.get_expected_values(A_						,							batched=A_  )

       lowerCamelCase_		     =    image_processor(
           A_						,							['semantic'] * len(A_  )						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )						,							)

 def 				a__ (				self					:  Dict  )   ->	str:

       """simple docstring"""






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

       # Test not batched input
       lowerCamelCase_		     =    image_processor(image_inputs[0]						,							['semantic']						,							return_tensors='pt'  ).pixel_values

       lowerCamelCase_     ,				lowerCamelCase_		     =    self.image_processing_tester.get_expected_values(A_  )

       self.assertEqual(
           encoded_images.shape						,							(1, self.image_processing_tester.num_channels, expected_height, expected_width)						,							)

       # Test batched
       lowerCamelCase_     ,				lowerCamelCase_		     =    self.image_processing_tester.get_expected_values(A_						,							batched=A_  )

       lowerCamelCase_		     =    image_processor(
           A_						,							['semantic'] * len(A_  )						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )						,							)

 def 				a__ (				self					:  List[str]						,							A_					:  str=False						,							A_					:  int=False						,							A_					:  Dict="np"  )   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       # prepare image and target
       lowerCamelCase_		     =    self.image_processing_tester.num_labels
       lowerCamelCase_		     =    None
       lowerCamelCase_		     =    None
       lowerCamelCase_		     =    prepare_image_inputs(self.image_processing_tester						,							equal_resolution=A_  )
       if with_segmentation_maps:
             lowerCamelCase_		     =    num_labels
             if is_instance_map:
                   lowerCamelCase_		     =    list(range(A_  )  ) * 2
                   lowerCamelCase_		     =    dict(enumerate(A_  )  )
             lowerCamelCase_		     =    [
                 np.random.randint(0						,							high * 2						,							(img.size[1], img.size[0])  ).astype(np.uinta  ) for img in image_inputs
             ]
             if segmentation_type == "pil":
                   lowerCamelCase_		     =    [Image.fromarray(A_  ) for annotation in annotations]

       lowerCamelCase_		     =    image_processor(
           A_						,							['semantic'] * len(A_  )						,							A_						,							return_tensors='pt'						,							instance_id_to_semantic_id=A_						,							pad_and_return_pixel_mask=A_						,							)

       return inputs

 def 				a__ (				self					:  Dict  )   ->	Tuple:

       """simple docstring"""






       pass

 def 				a__ (				self					:  List[Any]  )   ->	Any:

       """simple docstring"""






       def common(A_					:  int=False						,							A_					:  Tuple=None  ):
             lowerCamelCase_		     =    self.comm_get_image_processor_inputs(
                 with_segmentation_maps=A_						,							is_instance_map=A_						,							segmentation_type=A_  )

             lowerCamelCase_		     =    inputs['mask_labels']
             lowerCamelCase_		     =    inputs['class_labels']
             lowerCamelCase_		     =    inputs['pixel_values']
             lowerCamelCase_		     =    inputs['text_inputs']

             # check the batch_size
             for mask_label, class_label, text_input in zip(A_						,							A_						,							A_  ):
                   self.assertEqual(mask_label.shape[0]						,							class_label.shape[0]  )
                   # this ensure padding has happened
                   self.assertEqual(mask_label.shape[1:]						,							pixel_values.shape[2:]  )
                   self.assertEqual(len(A_  )						,							self.image_processing_tester.num_text  )

       common()
       common(is_instance_map=A_  )
       common(is_instance_map=A_						,							segmentation_type='pil'  )
       common(is_instance_map=A_						,							segmentation_type='pil'  )

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

       """simple docstring"""






       lowerCamelCase_		     =    np.zeros((20, 50)  )
       lowerCamelCase_		     =    1
       lowerCamelCase_		     =    1
       lowerCamelCase_		     =    1

       lowerCamelCase_		     =    binary_mask_to_rle(A_  )
       self.assertEqual(len(A_  )						,							4  )
       self.assertEqual(rle[0]						,							21  )
       self.assertEqual(rle[1]						,							45  )

 def 				a__ (				self					:  Optional[int]  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes						,							max_seq_length=77						,							task_seq_length=77						,							class_info_file='ade20k_panoptic.json'						,							num_text=self.image_processing_tester.num_text						,							repo_path='shi-labs/oneformer_demo'						,							)
       lowerCamelCase_		     =    self.image_processing_tester.get_fake_oneformer_outputs()

       lowerCamelCase_		     =    fature_extractor.post_process_semantic_segmentation(A_  )

       self.assertEqual(len(A_  )						,							self.image_processing_tester.batch_size  )
       self.assertEqual(
           segmentation[0].shape						,							(
               self.image_processing_tester.height,
               self.image_processing_tester.width,
           )						,							)

       lowerCamelCase_		     =    [(1, 4) for i in range(self.image_processing_tester.batch_size  )]
       lowerCamelCase_		     =    fature_extractor.post_process_semantic_segmentation(A_						,							target_sizes=A_  )

       self.assertEqual(segmentation[0].shape						,							target_sizes[0]  )

 def 				a__ (				self					:  List[Any]  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes						,							max_seq_length=77						,							task_seq_length=77						,							class_info_file='ade20k_panoptic.json'						,							num_text=self.image_processing_tester.num_text						,							repo_path='shi-labs/oneformer_demo'						,							)
       lowerCamelCase_		     =    self.image_processing_tester.get_fake_oneformer_outputs()
       lowerCamelCase_		     =    image_processor.post_process_instance_segmentation(A_						,							threshold=0  )

       self.assertTrue(len(A_  ) == self.image_processing_tester.batch_size  )
       for el in segmentation:
             self.assertTrue('segmentation' in el  )
             self.assertTrue('segments_info' in el  )
             self.assertEqual(type(el['segments_info']  )						,							A_  )
             self.assertEqual(
                 el['segmentation'].shape						,							(self.image_processing_tester.height, self.image_processing_tester.width)  )





 def 				a__ (				self					:  str  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes						,							max_seq_length=77						,							task_seq_length=77						,							class_info_file='ade20k_panoptic.json'						,							num_text=self.image_processing_tester.num_text						,							repo_path='shi-labs/oneformer_demo'						,							)
       lowerCamelCase_		     =    self.image_processing_tester.get_fake_oneformer_outputs()
       lowerCamelCase_		     =    image_processor.post_process_panoptic_segmentation(A_						,							threshold=0  )

       self.assertTrue(len(A_  ) == self.image_processing_tester.batch_size  )
       for el in segmentation:
             self.assertTrue('segmentation' in el  )
             self.assertTrue('segments_info' in el  )
             self.assertEqual(type(el['segments_info']  )						,							A_  )
             self.assertEqual(
                 el['segmentation'].shape						,							(self.image_processing_tester.height, self.image_processing_tester.width)  )









def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int = 10_00     ):


      '''simple docstring'''



      lowerCamelCase_		     =    2**power
      lowerCamelCase_		     =    0
      while n:
            lowerCamelCase_     ,				lowerCamelCase_		     =    r + n % 10, n // 10
      return r


if __name__ == "__main__":
 print(solution(int(str(input()).strip())))



