"""simple docstring"""






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard


@slow
@require_flax
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def _snake_case			(   self							):
       super().tearDown()
       gc.collect()






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      FlaxControlNetModel.from_pretrained(
           '''lllyasviel/sd-controlnet-canny'''    ,			from_pt=_UpperCAmelCase    ,			dtype=jnp.bfloataa							)
       lowercase__:   Tuple					=      FlaxStableDiffusionControlNetPipeline.from_pretrained(
           '''runwayml/stable-diffusion-v1-5'''    ,			controlnet=_UpperCAmelCase    ,			from_pt=_UpperCAmelCase    ,			dtype=jnp.bfloataa							)
       lowercase__:   List[Any]					=      controlnet_params

       lowercase__:   Optional[int]					=      "bird"
       lowercase__:   Tuple					=      jax.device_count()
       lowercase__:   Optional[Any]					=      pipe.prepare_text_inputs([prompts] * num_samples							)

       lowercase__:   List[Any]					=      load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''							)
       lowercase__:   Dict					=      pipe.prepare_image_inputs([canny_image] * num_samples							)

       lowercase__:   Any					=      jax.random.PRNGKey(0							)
       lowercase__:   int					=      jax.random.split(_UpperCAmelCase    ,			jax.device_count()							)

       lowercase__:   str					=      replicate(_UpperCAmelCase							)
       lowercase__:   Optional[int]					=      shard(_UpperCAmelCase							)
       lowercase__:   Union[str, Any]					=      shard(_UpperCAmelCase							)

       lowercase__:   Dict					=      pipe(
           prompt_ids=_UpperCAmelCase    ,			image=_UpperCAmelCase    ,			params=_UpperCAmelCase    ,			prng_seed=_UpperCAmelCase    ,			num_inference_steps=50    ,			jit=_UpperCAmelCase    ,			).images
       assert images.shape == (jax.device_count(), 1, 768, 512, 3)

       lowercase__:   str					=      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
       lowercase__:   Any					=      images[0, 253:256, 253:256, -1]

       lowercase__:   Optional[int]					=      jnp.asarray(jax.device_get(image_slice.flatten()							)							)
       lowercase__:   Dict					=      jnp.array(
           [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]							)
       print(F"""output_slice: {output_slice}"""							)
       assert jnp.abs(output_slice - expected_slice							).max() < 1e-2






    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      FlaxControlNetModel.from_pretrained(
           '''lllyasviel/sd-controlnet-openpose'''    ,			from_pt=_UpperCAmelCase    ,			dtype=jnp.bfloataa							)
       lowercase__:   Union[str, Any]					=      FlaxStableDiffusionControlNetPipeline.from_pretrained(
           '''runwayml/stable-diffusion-v1-5'''    ,			controlnet=_UpperCAmelCase    ,			from_pt=_UpperCAmelCase    ,			dtype=jnp.bfloataa							)
       lowercase__:   Optional[Any]					=      controlnet_params

       lowercase__:   Tuple					=      "Chef in the kitchen"
       lowercase__:   int					=      jax.device_count()
       lowercase__:   Any					=      pipe.prepare_text_inputs([prompts] * num_samples							)

       lowercase__:   Dict					=      load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'''							)
       lowercase__:   List[Any]					=      pipe.prepare_image_inputs([pose_image] * num_samples							)

       lowercase__:   List[Any]					=      jax.random.PRNGKey(0							)
       lowercase__:   Any					=      jax.random.split(_UpperCAmelCase    ,			jax.device_count()							)

       lowercase__:   Tuple					=      replicate(_UpperCAmelCase							)
       lowercase__:   Dict					=      shard(_UpperCAmelCase							)
       lowercase__:   Dict					=      shard(_UpperCAmelCase							)

       lowercase__:   Dict					=      pipe(
           prompt_ids=_UpperCAmelCase    ,			image=_UpperCAmelCase    ,			params=_UpperCAmelCase    ,			prng_seed=_UpperCAmelCase    ,			num_inference_steps=50    ,			jit=_UpperCAmelCase    ,			).images
       assert images.shape == (jax.device_count(), 1, 768, 512, 3)

       lowercase__:   Optional[Any]					=      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
       lowercase__:   List[str]					=      images[0, 253:256, 253:256, -1]

       lowercase__:   int					=      jnp.asarray(jax.device_get(image_slice.flatten()							)							)
       lowercase__:   Dict					=      jnp.array(
           [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]							)
       print(F"""output_slice: {output_slice}"""							)
       assert jnp.abs(output_slice - expected_slice							).max() < 1e-2








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





class 			__a  (    unittest.TestCase       ):







 def __init__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   List[str]=7							,   SCREAMING_SNAKE_CASE					:   Optional[int]=3							,   SCREAMING_SNAKE_CASE					:   Tuple=30							,   SCREAMING_SNAKE_CASE					:   Dict=4_00							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=True							,   SCREAMING_SNAKE_CASE					:   Dict=True							,   SCREAMING_SNAKE_CASE					:   Optional[int]=[0.5, 0.5, 0.5]							,   SCREAMING_SNAKE_CASE					:   List[str]=[0.5, 0.5, 0.5]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=10							,   SCREAMING_SNAKE_CASE					:   int=False							,   SCREAMING_SNAKE_CASE					:   int=2_55							,   SCREAMING_SNAKE_CASE					:   str="shi-labs/oneformer_demo"							,   SCREAMING_SNAKE_CASE					:   List[Any]="ade20k_panoptic.json"							,   SCREAMING_SNAKE_CASE					:   Tuple=10							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  parent
      UpperCamelCase__			:   Optional[int]       				=  batch_size
      UpperCamelCase__			:   Any       				=  num_channels
      UpperCamelCase__			:   Optional[int]       				=  min_resolution
      UpperCamelCase__			:   Union[str, Any]       				=  max_resolution
      UpperCamelCase__			:   Optional[int]       				=  do_resize
      UpperCamelCase__			:   List[Any]       				=  {"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
      UpperCamelCase__			:   Dict       				=  do_normalize
      UpperCamelCase__			:   Optional[int]       				=  image_mean
      UpperCamelCase__			:   Union[str, Any]       				=  image_std
      UpperCamelCase__			:   Union[str, Any]       				=  class_info_file
      UpperCamelCase__			:   Tuple       				=  prepare_metadata(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   str       				=  num_text
      UpperCamelCase__			:   int       				=  repo_path

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







 def 					__lowercase					(		self					:   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 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   List[str]=False						):


      '''simple docstring'''






      if not batched:
           UpperCamelCase__			:   str       				=  image_inputs[0]
           if isinstance(SCREAMING_SNAKE_CASE							,   Image.Image						):
                UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  image.size
           else:
                UpperCamelCase__				,     UpperCamelCase__			:   int       				=  image.shape[1], image.shape[2]
           if w < h:
                UpperCamelCase__			:   Any       				=  int(self.size["shortest_edge"] * h / w						)
                UpperCamelCase__			:   Union[str, Any]       				=  self.size["shortest_edge"]
           elif w > h:
                UpperCamelCase__			:   Union[str, Any]       				=  self.size["shortest_edge"]
                UpperCamelCase__			:   int       				=  int(self.size["shortest_edge"] * w / h						)
           else:
                UpperCamelCase__			:   Optional[Any]       				=  self.size["shortest_edge"]
                UpperCamelCase__			:   str       				=  self.size["shortest_edge"]

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

      return expected_height, expected_width







 def 					__lowercase					(		self					:   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  (    A__       ,   unittest.TestCase       ):
 _lowerCAmelCase       :       Tuple        = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
 # only for test_image_processing_common.test_image_proc_to_json_string
 _lowerCAmelCase       :       List[str]        = image_processing_class







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  OneFormerImageProcessorTester(self						)







 @property
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      return self.image_processing_tester.prepare_image_processor_dict()







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  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 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__			:   Optional[int]       				=  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
      UpperCamelCase__			:   Optional[int]       				=  image_processor(image_inputs[0]							,   ["semantic"]							,   return_tensors="pt"						).pixel_values

      UpperCamelCase__				,     UpperCamelCase__			:   Optional[Any]       				=  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
      UpperCamelCase__				,     UpperCamelCase__			:   Dict       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE							,   batched=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  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 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__			:   Union[str, Any]       				=  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
      UpperCamelCase__			:   Any       				=  image_processor(image_inputs[0]							,   ["semantic"]							,   return_tensors="pt"						).pixel_values

      UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  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
      UpperCamelCase__				,     UpperCamelCase__			:   Optional[Any]       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE							,   batched=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  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 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__			:   Optional[int]       				=  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
      UpperCamelCase__			:   List[Any]       				=  image_processor(image_inputs[0]							,   ["semantic"]							,   return_tensors="pt"						).pixel_values

      UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  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
      UpperCamelCase__				,     UpperCamelCase__			:   List[Any]       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE							,   batched=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Any       				=  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 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   Any="np"						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  self.image_processing_class(**self.image_processor_dict						)
      # prepare image and target
      UpperCamelCase__			:   Any       				=  self.image_processing_tester.num_labels
      UpperCamelCase__			:   Optional[Any]       				=  None
      UpperCamelCase__			:   Optional[Any]       				=  None
      UpperCamelCase__			:   str       				=  prepare_image_inputs(self.image_processing_tester							,   equal_resolution=SCREAMING_SNAKE_CASE						)
      if with_segmentation_maps:
           UpperCamelCase__			:   Tuple       				=  num_labels
           if is_instance_map:
                UpperCamelCase__			:   List[str]       				=  list(range(SCREAMING_SNAKE_CASE						)						) * 2
                UpperCamelCase__			:   Optional[Any]       				=  dict(enumerate(SCREAMING_SNAKE_CASE						)						)
           UpperCamelCase__			:   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":
                UpperCamelCase__			:   List[str]       				=  [Image.fromarray(SCREAMING_SNAKE_CASE						) for annotation in annotations]

      UpperCamelCase__			:   Optional[int]       				=  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 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      def common(SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   str=None						):
           UpperCamelCase__			:   Any       				=  self.comm_get_image_processor_inputs(
               with_segmentation_maps=SCREAMING_SNAKE_CASE							,   is_instance_map=SCREAMING_SNAKE_CASE							,   segmentation_type=SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   str       				=  inputs["mask_labels"]
           UpperCamelCase__			:   Optional[Any]       				=  inputs["class_labels"]
           UpperCamelCase__			:   List[str]       				=  inputs["pixel_values"]
           UpperCamelCase__			:   int       				=  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 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  np.zeros((20, 50)						)
      UpperCamelCase__			:   int       				=  1
      UpperCamelCase__			:   Dict       				=  1
      UpperCamelCase__			:   Dict       				=  1

      UpperCamelCase__			:   int       				=  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 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   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"							,   )
      UpperCamelCase__			:   int       				=  self.image_processing_tester.get_fake_oneformer_outputs()

      UpperCamelCase__			:   Union[str, Any]       				=  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,
          )							,   )

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

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







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   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"							,   )
      UpperCamelCase__			:   List[str]       				=  self.image_processing_tester.get_fake_oneformer_outputs()
      UpperCamelCase__			:   Optional[int]       				=  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 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   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"							,   )
      UpperCamelCase__			:   Any       				=  self.image_processing_tester.get_fake_oneformer_outputs()
      UpperCamelCase__			:   Tuple       				=  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)						)