



'''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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase="shi-labs/oneformer_demo"		)		-> List[Any]:
      with open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		) as f:
            snake_case__  :    Optional[int]    						=      json.load(_lowerCAmelCase		)
      snake_case__  :    str    						=      {}
      snake_case__  :    Optional[int]    						=      []
      snake_case__  :    int    						=      []
      for key, info in class_info.items():
            snake_case__  :    Optional[int]    						=      info["""name"""]
            class_names.append(info["""name"""]		)
            if info["isthing"]:
                  thing_ids.append(int(_lowerCAmelCase		)		)
      snake_case__  :    Union[str, Any]    						=      thing_ids
      snake_case__  :    Union[str, Any]    						=      class_names
      return metadata







class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    def __init__(    self					:     str ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Optional[Any]=7 ,     snake_case_					:     Optional[Any]=3 ,     snake_case_					:     Optional[int]=30 ,     snake_case_					:     Optional[Any]=400 ,     snake_case_					:     int=None ,     snake_case_					:     Tuple=True ,     snake_case_					:     Dict=True ,     snake_case_					:     Any=[0.5, 0.5, 0.5] ,     snake_case_					:     List[Any]=[0.5, 0.5, 0.5] ,     snake_case_					:     str=10 ,     snake_case_					:     str=False ,     snake_case_					:     List[Any]=255 ,     snake_case_					:     List[str]="shi-labs/oneformer_demo" ,     snake_case_					:     Optional[Any]="ade20k_panoptic.json" ,     snake_case_					:     Any=10 ,     ):
          snake_case__  :    List[Any]    						=      parent
          snake_case__  :    Optional[Any]    						=      batch_size
          snake_case__  :    Dict    						=      num_channels
          snake_case__  :    Any    						=      min_resolution
          snake_case__  :    Dict    						=      max_resolution
          snake_case__  :    Dict    						=      do_resize
          snake_case__  :    str    						=      {"""shortest_edge""": 32, """longest_edge""": 1_333} if size is None else size
          snake_case__  :    Union[str, Any]    						=      do_normalize
          snake_case__  :    Union[str, Any]    						=      image_mean
          snake_case__  :    Tuple    						=      image_std
          snake_case__  :    List[Any]    						=      class_info_file
          snake_case__  :    Optional[int]    						=      prepare_metadata(snake_case_ ,     snake_case_						)
          snake_case__  :    Union[str, Any]    						=      num_text
          snake_case__  :    Optional[Any]    						=      repo_path

          # for the post_process_functions
          snake_case__  :    List[Any]    						=      2
          snake_case__  :    Optional[int]    						=      10
          snake_case__  :    Any    						=      10
          snake_case__  :    str    						=      3
          snake_case__  :    List[str]    						=      4
          snake_case__  :    Union[str, Any]    						=      num_labels
          snake_case__  :    Union[str, Any]    						=      do_reduce_labels
          snake_case__  :    Optional[Any]    						=      ignore_index




    def lowerCamelCase       (    self					:     List[str]						):
          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 lowerCamelCase       (    self					:     Any ,     snake_case_					:     Any ,     snake_case_					:     Tuple=False						):
          if not batched:
                snake_case__  :    List[str]    						=      image_inputs[0]
                if isinstance(snake_case_ ,     Image.Image						):
                      snake_case__						,				snake_case__  :    List[str]    						=      image.size
                else:
                      snake_case__						,				snake_case__  :    List[str]    						=      image.shape[1], image.shape[2]
                if w < h:
                      snake_case__  :    Any    						=      int(self.size["""shortest_edge"""] * h / w						)
                      snake_case__  :    Union[str, Any]    						=      self.size["""shortest_edge"""]
                elif w > h:
                      snake_case__  :    Union[str, Any]    						=      self.size["""shortest_edge"""]
                      snake_case__  :    Union[str, Any]    						=      int(self.size["""shortest_edge"""] * w / h						)
                else:
                      snake_case__  :    Union[str, Any]    						=      self.size["""shortest_edge"""]
                      snake_case__  :    int    						=      self.size["""shortest_edge"""]

          else:
                snake_case__  :    List[str]    						=      []
                for image in image_inputs:
                      snake_case__						,				snake_case__  :    int    						=      self.get_expected_values([image]						)
                      expected_values.append((expected_height, expected_width)						)
                snake_case__  :    int    						=      max(snake_case_ ,     key=lambda snake_case_						: item[0]						)[0]
                snake_case__  :    Any    						=      max(snake_case_ ,     key=lambda snake_case_						: item[1]						)[1]

          return expected_height, expected_width




    def lowerCamelCase       (    self					:     Union[str, 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     UpperCAmelCase_       (       _a		,		unittest.TestCase	):
    """simple docstring"""







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




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    Dict    						=      OneFormerImageProcessorTester(self						)




    @property
    def lowerCamelCase       (    self					:     Tuple						):
          return self.image_processing_tester.prepare_image_processor_dict()




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    str    						=      self.image_processing_class(**self.image_processor_dict						)
          self.assertTrue(hasattr(snake_case_ ,     """image_mean"""						)						)
          self.assertTrue(hasattr(snake_case_ ,     """image_std"""						)						)
          self.assertTrue(hasattr(snake_case_ ,     """do_normalize"""						)						)
          self.assertTrue(hasattr(snake_case_ ,     """do_resize"""						)						)
          self.assertTrue(hasattr(snake_case_ ,     """size"""						)						)
          self.assertTrue(hasattr(snake_case_ ,     """ignore_index"""						)						)
          self.assertTrue(hasattr(snake_case_ ,     """class_info_file"""						)						)
          self.assertTrue(hasattr(snake_case_ ,     """num_text"""						)						)
          self.assertTrue(hasattr(snake_case_ ,     """repo_path"""						)						)
          self.assertTrue(hasattr(snake_case_ ,     """metadata"""						)						)
          self.assertTrue(hasattr(snake_case_ ,     """do_reduce_labels"""						)						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          pass




    def lowerCamelCase       (    self					:     List[str]						):
          # Initialize image_processor
          snake_case__  :    int    						=      self.image_processing_class(**self.image_processor_dict						)
          # create random PIL images
          snake_case__  :    Any    						=      prepare_image_inputs(self.image_processing_tester ,     equal_resolution=snake_case_						)
          for image in image_inputs:
                self.assertIsInstance(snake_case_ ,     Image.Image						)

          # Test not batched input
          snake_case__  :    Union[str, Any]    						=      image_processor(image_inputs[0] ,     ["""semantic"""] ,     return_tensors="""pt"""						).pixel_values

          snake_case__						,				snake_case__  :    List[str]    						=      self.image_processing_tester.get_expected_values(snake_case_						)

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

          # Test batched
          snake_case__						,				snake_case__  :    str    						=      self.image_processing_tester.get_expected_values(snake_case_ ,     batched=snake_case_						)

          snake_case__  :    Union[str, Any]    						=      image_processor(
              snake_case_ ,     ["""semantic"""] * len(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 lowerCamelCase       (    self					:     Optional[int]						):
          # Initialize image_processor
          snake_case__  :    List[str]    						=      self.image_processing_class(**self.image_processor_dict						)
          # create random numpy tensors
          snake_case__  :    Any    						=      prepare_image_inputs(self.image_processing_tester ,     equal_resolution=snake_case_ ,     numpify=snake_case_						)
          for image in image_inputs:
                self.assertIsInstance(snake_case_ ,     np.ndarray						)

          # Test not batched input
          snake_case__  :    Any    						=      image_processor(image_inputs[0] ,     ["""semantic"""] ,     return_tensors="""pt"""						).pixel_values

          snake_case__						,				snake_case__  :    Tuple    						=      self.image_processing_tester.get_expected_values(snake_case_						)

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

          # Test batched
          snake_case__						,				snake_case__  :    Union[str, Any]    						=      self.image_processing_tester.get_expected_values(snake_case_ ,     batched=snake_case_						)

          snake_case__  :    Union[str, Any]    						=      image_processor(
              snake_case_ ,     ["""semantic"""] * len(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 lowerCamelCase       (    self					:     List[Any]						):
          # Initialize image_processor
          snake_case__  :    Optional[Any]    						=      self.image_processing_class(**self.image_processor_dict						)
          # create random PyTorch tensors
          snake_case__  :    Optional[Any]    						=      prepare_image_inputs(self.image_processing_tester ,     equal_resolution=snake_case_ ,     torchify=snake_case_						)
          for image in image_inputs:
                self.assertIsInstance(snake_case_ ,     torch.Tensor						)

          # Test not batched input
          snake_case__  :    str    						=      image_processor(image_inputs[0] ,     ["""semantic"""] ,     return_tensors="""pt"""						).pixel_values

          snake_case__						,				snake_case__  :    Optional[int]    						=      self.image_processing_tester.get_expected_values(snake_case_						)

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

          # Test batched
          snake_case__						,				snake_case__  :    Optional[Any]    						=      self.image_processing_tester.get_expected_values(snake_case_ ,     batched=snake_case_						)

          snake_case__  :    Optional[Any]    						=      image_processor(
              snake_case_ ,     ["""semantic"""] * len(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 lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     Tuple=False ,     snake_case_					:     Any=False ,     snake_case_					:     Tuple="np"						):
          snake_case__  :    int    						=      self.image_processing_class(**self.image_processor_dict						)
          # prepare image and target
          snake_case__  :    Any    						=      self.image_processing_tester.num_labels
          snake_case__  :    Dict    						=      None
          snake_case__  :    Dict    						=      None
          snake_case__  :    Union[str, Any]    						=      prepare_image_inputs(self.image_processing_tester ,     equal_resolution=snake_case_						)
          if with_segmentation_maps:
                snake_case__  :    List[Any]    						=      num_labels
                if is_instance_map:
                      snake_case__  :    str    						=      list(range(snake_case_						)						) * 2
                      snake_case__  :    List[str]    						=      dict(enumerate(snake_case_						)						)
                snake_case__  :    Union[str, Any]    						=      [
                    np.random.randint(0 ,     high * 2 ,     (img.size[1], img.size[0])						).astype(np.uinta						) for img in image_inputs
                ]
                if segmentation_type == "pil":
                      snake_case__  :    List[Any]    						=      [Image.fromarray(snake_case_						) for annotation in annotations]

          snake_case__  :    List[str]    						=      image_processor(
              snake_case_ ,     ["""semantic"""] * len(snake_case_						) ,     snake_case_ ,     return_tensors="""pt""" ,     instance_id_to_semantic_id=snake_case_ ,     pad_and_return_pixel_mask=snake_case_ ,     )

          return inputs




    def lowerCamelCase       (    self					:     Optional[int]						):
          pass




    def lowerCamelCase       (    self					:     Any						):
          def common(snake_case_					:     int=False ,     snake_case_					:     str=None						):
                snake_case__  :    str    						=      self.comm_get_image_processor_inputs(
                    with_segmentation_maps=snake_case_ ,     is_instance_map=snake_case_ ,     segmentation_type=snake_case_						)

                snake_case__  :    Optional[Any]    						=      inputs["""mask_labels"""]
                snake_case__  :    Union[str, Any]    						=      inputs["""class_labels"""]
                snake_case__  :    int    						=      inputs["""pixel_values"""]
                snake_case__  :    List[Any]    						=      inputs["""text_inputs"""]

                # check the batch_size
                for mask_label, class_label, text_input in zip(snake_case_ ,     snake_case_ ,     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(snake_case_						) ,     self.image_processing_tester.num_text						)

          common()
          common(is_instance_map=snake_case_						)
          common(is_instance_map=snake_case_ ,     segmentation_type="""pil"""						)
          common(is_instance_map=snake_case_ ,     segmentation_type="""pil"""						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    int    						=      np.zeros((20, 50)						)
          snake_case__  :    str    						=      1
          snake_case__  :    Tuple    						=      1
          snake_case__  :    Optional[int]    						=      1

          snake_case__  :    int    						=      binary_mask_to_rle(snake_case_						)
          self.assertEqual(len(snake_case_						) ,     4						)
          self.assertEqual(rle[0] ,     21						)
          self.assertEqual(rle[1] ,     45						)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    str    						=      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""" ,     )
          snake_case__  :    Any    						=      self.image_processing_tester.get_fake_oneformer_outputs()

          snake_case__  :    Dict    						=      fature_extractor.post_process_semantic_segmentation(snake_case_						)

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

          snake_case__  :    Optional[Any]    						=      [(1, 4) for i in range(self.image_processing_tester.batch_size						)]
          snake_case__  :    List[Any]    						=      fature_extractor.post_process_semantic_segmentation(snake_case_ ,     target_sizes=snake_case_						)

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




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    int    						=      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""" ,     )
          snake_case__  :    str    						=      self.image_processing_tester.get_fake_oneformer_outputs()
          snake_case__  :    Optional[int]    						=      image_processor.post_process_instance_segmentation(snake_case_ ,     threshold=0						)

          self.assertTrue(len(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"""]						) ,     snake_case_						)
                self.assertEqual(
                    el["""segmentation"""].shape ,     (self.image_processing_tester.height, self.image_processing_tester.width)						)




    def lowerCamelCase       (    self					:     str						):
          snake_case__  :    Tuple    						=      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""" ,     )
          snake_case__  :    List[str]    						=      self.image_processing_tester.get_fake_oneformer_outputs()
          snake_case__  :    Dict    						=      image_processor.post_process_panoptic_segmentation(snake_case_ ,     threshold=0						)

          self.assertTrue(len(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"""]						) ,     snake_case_						)
                self.assertEqual(
                    el["""segmentation"""].shape ,     (self.image_processing_tester.height, self.image_processing_tester.width)						)





'''simple docstring'''





# Function to print upper half of diamond (pyramid)





def 						__snake_case(				_lowerCAmelCase		)		-> Any:
      for i in range(0     ,   _lowerCAmelCase		):
            for _ in range(0     ,   n - i - 1		):  # printing spaces
                  print(""" """     ,   end=""""""		)
            for _ in range(0     ,   i + 1		):  # printing stars
                  print("""* """     ,   end=""""""		)
            print()





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      for i in range(_lowerCAmelCase     ,   0     ,   -1		):
            for _ in range(_lowerCAmelCase     ,   0     ,   -1		):  # printing stars
                  print("""* """     ,   end=""""""		)
            print()
            for _ in range(n - i + 1     ,   0     ,   -1		):  # printing spaces
                  print(""" """     ,   end=""""""		)





def 						__snake_case(				_lowerCAmelCase		)		-> List[Any]:
      if n <= 0:
            print("""       ...       ....        nothing printing :("""		)
            return
      floyd(_lowerCAmelCase		)  # upper half
      reverse_floyd(_lowerCAmelCase		)  # lower half


if __name__ == "__main__":
       print(R"| /\ | |- |  |-  |--| |\  /| |-")
       print(R"|/  \| |- |_ |_  |__| | \/ | |_")
       __a          =		1
       while K:
              __a          =		int(input("enter the number and , and see the magic : "))
              print()
              pretty_print(user_number)
              __a          =		int(input("press 0 to exit... and 1 to continue..."))

       print("Good Bye...")
