






"""simple docstring"""





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      lowerCamelCase__       (							_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Optional[int]="shi-labs/oneformer_demo" )      ->				Union[str, Any]:
  with open(hf_hub_download(_lowerCamelCase		,				_lowerCamelCase		,				repo_type='dataset' )		,				'r' ) as f:
    lowerCamelCase_         =						json.load(_lowerCamelCase )
  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(_lowerCamelCase ) )
  lowerCamelCase_         =						thing_ids
  lowerCamelCase_         =						class_names
  return metadata






class      a						(					unittest.TestCase		):






    def __init__(       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=7       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=3       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=30       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=400       ,						__SCREAMING_SNAKE_CASE     :							Dict=None       ,						__SCREAMING_SNAKE_CASE     :							Dict=True       ,						__SCREAMING_SNAKE_CASE     :							int=True       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=[0.5, 0.5, 0.5]       ,						__SCREAMING_SNAKE_CASE     :							Dict=[0.5, 0.5, 0.5]       ,						__SCREAMING_SNAKE_CASE     :							Dict=10       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=False       ,						__SCREAMING_SNAKE_CASE     :							List[str]=255       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]="shi-labs/oneformer_demo"       ,						__SCREAMING_SNAKE_CASE     :							Tuple="ade20k_panoptic.json"       ,						__SCREAMING_SNAKE_CASE     :							Dict=10       ,						)     ->		Optional[Any]:
      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(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      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    UpperCamelCase       (       self     :							List[Any]					)     ->		Union[str, Any]:
      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    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Any=False					)     ->		List[str]:
      if not batched:
        lowerCamelCase_         =						image_inputs[0]
        if isinstance(__SCREAMING_SNAKE_CASE       ,						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(__SCREAMING_SNAKE_CASE       ,						key=lambda __SCREAMING_SNAKE_CASE					: item[0]					)[0]
        lowerCamelCase_         =						max(__SCREAMING_SNAKE_CASE       ,						key=lambda __SCREAMING_SNAKE_CASE					: item[1]					)[1]

      return expected_height, expected_width






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Any:
      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						(					__snake_case  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     Any						= OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
    # only for test_image_processing_common.test_image_proc_to_json_string
    SCREAMING_SNAKE_CASE						:     int						= image_processing_class






    def    UpperCamelCase       (       self     :							Tuple					)     ->		Union[str, Any]:
      lowerCamelCase_         =						OneFormerImageProcessorTester(self					)






    @property
    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Any:
      return self.image_processing_tester.prepare_image_processor_dict()






    def    UpperCamelCase       (       self     :							str					)     ->		Dict:
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'image_mean'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'image_std'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'do_normalize'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'do_resize'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'size'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'ignore_index'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'class_info_file'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'num_text'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'repo_path'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'metadata'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'do_reduce_labels'					)					)






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Dict:
      pass






    def    UpperCamelCase       (       self     :							Tuple					)     ->		List[Any]:
      # Initialize image_processor
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      lowerCamelCase_         =						prepare_image_inputs(self.image_processing_tester       ,						equal_resolution=__SCREAMING_SNAKE_CASE					)
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,						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(__SCREAMING_SNAKE_CASE					)

      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(__SCREAMING_SNAKE_CASE       ,						batched=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						image_processor(
          __SCREAMING_SNAKE_CASE       ,						['semantic'] * len(__SCREAMING_SNAKE_CASE					)       ,						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    UpperCamelCase       (       self     :							List[Any]					)     ->		Union[str, Any]:
      # Initialize image_processor
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      lowerCamelCase_         =						prepare_image_inputs(self.image_processing_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
      lowerCamelCase_         =						image_processor(image_inputs[0]       ,						['semantic']       ,						return_tensors='pt'					).pixel_values

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

      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(__SCREAMING_SNAKE_CASE       ,						batched=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						image_processor(
          __SCREAMING_SNAKE_CASE       ,						['semantic'] * len(__SCREAMING_SNAKE_CASE					)       ,						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    UpperCamelCase       (       self     :							str					)     ->		Any:
      # Initialize image_processor
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      lowerCamelCase_         =						prepare_image_inputs(self.image_processing_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
      lowerCamelCase_         =						image_processor(image_inputs[0]       ,						['semantic']       ,						return_tensors='pt'					).pixel_values

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

      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(__SCREAMING_SNAKE_CASE       ,						batched=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						image_processor(
          __SCREAMING_SNAKE_CASE       ,						['semantic'] * len(__SCREAMING_SNAKE_CASE					)       ,						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    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							int=False       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=False       ,						__SCREAMING_SNAKE_CASE     :							Dict="np"					)     ->		Any:
      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=__SCREAMING_SNAKE_CASE					)
      if with_segmentation_maps:
        lowerCamelCase_         =						num_labels
        if is_instance_map:
          lowerCamelCase_         =						list(range(__SCREAMING_SNAKE_CASE					)					) * 2
          lowerCamelCase_         =						dict(enumerate(__SCREAMING_SNAKE_CASE					)					)
        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(__SCREAMING_SNAKE_CASE					) for annotation in annotations]

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

      return inputs






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Optional[Any]:
      pass






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Any:
      def common(__SCREAMING_SNAKE_CASE     :							List[str]=False       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=None					):
        lowerCamelCase_         =						self.comm_get_image_processor_inputs(
            with_segmentation_maps=__SCREAMING_SNAKE_CASE       ,						is_instance_map=__SCREAMING_SNAKE_CASE       ,						segmentation_type=__SCREAMING_SNAKE_CASE					)

        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(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
          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(__SCREAMING_SNAKE_CASE					)       ,						self.image_processing_tester.num_text					)

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






    def    UpperCamelCase       (       self     :							str					)     ->		List[str]:
      lowerCamelCase_         =						np.zeros((20, 50)					)
      lowerCamelCase_         =						1
      lowerCamelCase_         =						1
      lowerCamelCase_         =						1

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






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

      self.assertEqual(len(__SCREAMING_SNAKE_CASE					)       ,						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(__SCREAMING_SNAKE_CASE       ,						target_sizes=__SCREAMING_SNAKE_CASE					)

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






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Optional[int]:
      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(__SCREAMING_SNAKE_CASE       ,						threshold=0					)

      self.assertTrue(len(__SCREAMING_SNAKE_CASE					) == 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']					)       ,						__SCREAMING_SNAKE_CASE					)
        self.assertEqual(
            el['segmentation'].shape       ,						(self.image_processing_tester.height, self.image_processing_tester.width)					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Union[str, Any]:
      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(__SCREAMING_SNAKE_CASE       ,						threshold=0					)

      self.assertTrue(len(__SCREAMING_SNAKE_CASE					) == 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']					)       ,						__SCREAMING_SNAKE_CASE					)
        self.assertEqual(
            el['segmentation'].shape       ,						(self.image_processing_tester.height, self.image_processing_tester.width)					)












"""simple docstring"""





import string







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				None:
  for key in range(len(string.ascii_uppercase ) ):
    lowerCamelCase_         =						''
    for symbol in message:
      if symbol in string.ascii_uppercase:
        lowerCamelCase_         =						string.ascii_uppercase.find(_lowerCamelCase )
        lowerCamelCase_         =						num - key
        if num < 0:
          lowerCamelCase_         =						num + len(string.ascii_uppercase )
        lowerCamelCase_         =						translated + string.ascii_uppercase[num]
      else:
        lowerCamelCase_         =						translated + symbol
    print(F'''Decryption using Key #{key}: {translated}''' )







def      lowerCamelCase__       (							)      ->				None:
  lowerCamelCase_         =						input('Encrypted message: ' )
  lowerCamelCase_         =						message.upper()
  decrypt(_lowerCamelCase )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




