





'''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      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   Union[str, Any]="shi-labs/oneformer_demo"   )						->			Union[str, Any]:

     """simple docstring"""




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

class        _a    (						unittest.TestCase							):






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

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






     def     _lowercase     (			self   )							->      Optional[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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False   )							->      Union[str, Any]:
          if not batched:
               _snake_case							=				image_inputs[0]
               if isinstance(__lowercase ,Image.Image   ):
                    _snake_case							=				image.size
               else:
                    _snake_case							=				image.shape[1], image.shape[2]
               if w < h:
                    _snake_case							=				int(self.size["shortest_edge"] * h / w   )
                    _snake_case							=				self.size['''shortest_edge''']
               elif w > h:
                    _snake_case							=				self.size['''shortest_edge''']
                    _snake_case							=				int(self.size["shortest_edge"] * w / h   )
               else:
                    _snake_case							=				self.size['''shortest_edge''']
                    _snake_case							=				self.size['''shortest_edge''']

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

          return expected_height, expected_width






     def     _lowercase     (			self   )							->      List[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    (						__lowerCamelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   str       =						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_							:   List[Any]       =						image_processing_class






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				OneFormerImageProcessorTester(self   )






     @property
     def     _lowercase     (			self   )							->      Union[str, Any]:
          return self.image_processing_tester.prepare_image_processor_dict()






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				self.image_processing_class(**self.image_processor_dict   )
          self.assertTrue(hasattr(__lowercase ,"image_mean"   )   )
          self.assertTrue(hasattr(__lowercase ,"image_std"   )   )
          self.assertTrue(hasattr(__lowercase ,"do_normalize"   )   )
          self.assertTrue(hasattr(__lowercase ,"do_resize"   )   )
          self.assertTrue(hasattr(__lowercase ,"size"   )   )
          self.assertTrue(hasattr(__lowercase ,"ignore_index"   )   )
          self.assertTrue(hasattr(__lowercase ,"class_info_file"   )   )
          self.assertTrue(hasattr(__lowercase ,"num_text"   )   )
          self.assertTrue(hasattr(__lowercase ,"repo_path"   )   )
          self.assertTrue(hasattr(__lowercase ,"metadata"   )   )
          self.assertTrue(hasattr(__lowercase ,"do_reduce_labels"   )   )






     def     _lowercase     (			self   )							->      Optional[int]:
          pass






     def     _lowercase     (			self   )							->      Optional[int]:
          # Initialize image_processor
          _snake_case							=				self.image_processing_class(**self.image_processor_dict   )
          # create random PIL images
          _snake_case							=				prepare_image_inputs(self.image_processing_tester ,equal_resolution=__lowercase   )
          for image in image_inputs:
               self.assertIsInstance(__lowercase ,Image.Image   )

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

          _snake_case							=				self.image_processing_tester.get_expected_values(__lowercase   )

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

          # Test batched
          _snake_case							=				self.image_processing_tester.get_expected_values(__lowercase ,batched=__lowercase   )

          _snake_case							=				image_processor(
              __lowercase ,["semantic"] * len(__lowercase   ) ,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     _lowercase     (			self   )							->      Dict:
          # Initialize image_processor
          _snake_case							=				self.image_processing_class(**self.image_processor_dict   )
          # create random numpy tensors
          _snake_case							=				prepare_image_inputs(self.image_processing_tester ,equal_resolution=__lowercase ,numpify=__lowercase   )
          for image in image_inputs:
               self.assertIsInstance(__lowercase ,np.ndarray   )

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

          _snake_case							=				self.image_processing_tester.get_expected_values(__lowercase   )

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

          # Test batched
          _snake_case							=				self.image_processing_tester.get_expected_values(__lowercase ,batched=__lowercase   )

          _snake_case							=				image_processor(
              __lowercase ,["semantic"] * len(__lowercase   ) ,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     _lowercase     (			self   )							->      Any:
          # Initialize image_processor
          _snake_case							=				self.image_processing_class(**self.image_processor_dict   )
          # create random PyTorch tensors
          _snake_case							=				prepare_image_inputs(self.image_processing_tester ,equal_resolution=__lowercase ,torchify=__lowercase   )
          for image in image_inputs:
               self.assertIsInstance(__lowercase ,torch.Tensor   )

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

          _snake_case							=				self.image_processing_tester.get_expected_values(__lowercase   )

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

          # Test batched
          _snake_case							=				self.image_processing_tester.get_expected_values(__lowercase ,batched=__lowercase   )

          _snake_case							=				image_processor(
              __lowercase ,["semantic"] * len(__lowercase   ) ,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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE="np"   )							->      Tuple:
          _snake_case							=				self.image_processing_class(**self.image_processor_dict   )
          # prepare image and target
          _snake_case							=				self.image_processing_tester.num_labels
          _snake_case							=				None
          _snake_case							=				None
          _snake_case							=				prepare_image_inputs(self.image_processing_tester ,equal_resolution=__lowercase   )
          if with_segmentation_maps:
               _snake_case							=				num_labels
               if is_instance_map:
                    _snake_case							=				list(range(__lowercase   )   ) * 2
                    _snake_case							=				dict(enumerate(__lowercase   )   )
               _snake_case							=				[
                   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							=				[Image.fromarray(__lowercase   ) for annotation in annotations]

          _snake_case							=				image_processor(
              __lowercase ,["semantic"] * len(__lowercase   ) ,__lowercase ,return_tensors="pt" ,instance_id_to_semantic_id=__lowercase ,pad_and_return_pixel_mask=__lowercase ,)

          return inputs






     def     _lowercase     (			self   )							->      List[Any]:
          pass






     def     _lowercase     (			self   )							->      Union[str, Any]:
          def common(_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=None   ):
               _snake_case							=				self.comm_get_image_processor_inputs(
                   with_segmentation_maps=__lowercase ,is_instance_map=__lowercase ,segmentation_type=__lowercase   )

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

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

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






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				np.zeros((20, 50)   )
          _snake_case							=				1
          _snake_case							=				1
          _snake_case							=				1

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






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				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							=				self.image_processing_tester.get_fake_oneformer_outputs()

          _snake_case							=				fature_extractor.post_process_semantic_segmentation(__lowercase   )

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

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

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






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				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							=				self.image_processing_tester.get_fake_oneformer_outputs()
          _snake_case							=				image_processor.post_process_instance_segmentation(__lowercase ,threshold=0   )

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






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				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							=				self.image_processing_tester.get_fake_oneformer_outputs()
          _snake_case							=				image_processor.post_process_panoptic_segmentation(__lowercase ,threshold=0   )

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








'''simple docstring'''





from __future__ import annotations

from typing import Any





def      __a     (       _UpperCamelCase:   list[Any]   )						->			None:

     """simple docstring"""




     create_state_space_tree(_UpperCamelCase ,						[] ,						0   )





def      __a     (       _UpperCamelCase:   list[Any] ,						_UpperCamelCase:   list[Any] ,						_UpperCamelCase:   int   )						->			None:

     """simple docstring"""




     if index == len(_UpperCamelCase   ):
          print(_UpperCamelCase   )
          return

     create_state_space_tree(_UpperCamelCase ,						_UpperCamelCase ,						index + 1   )
     current_subsequence.append(sequence[index]   )
     create_state_space_tree(_UpperCamelCase ,						_UpperCamelCase ,						index + 1   )
     current_subsequence.pop()


if __name__ == "__main__":
   UpperCamelCase_						:      list[Any]       =						[3, 1, 2, 4]
   generate_all_subsequences(seq)

   seq.clear()
   seq.extend(['''A''', '''B''', '''C'''])
   generate_all_subsequences(seq)


