'''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 (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						Dict="shi-labs/oneformer_demo"				)	->							int:
      '''simple docstring'''


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





class 							lowercase						(						unittest.TestCase ):


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Optional[int]=7   ,		__lowerCAmelCase				:	List[str]=3   ,		__lowerCAmelCase				:	int=30   ,		__lowerCAmelCase				:	List[Any]=400   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	Optional[int]=True   ,		__lowerCAmelCase				:	List[str]=True   ,		__lowerCAmelCase				:	Union[str, Any]=[0.5, 0.5, 0.5]   ,		__lowerCAmelCase				:	Any=[0.5, 0.5, 0.5]   ,		__lowerCAmelCase				:	Optional[int]=10   ,		__lowerCAmelCase				:	Optional[int]=False   ,		__lowerCAmelCase				:	List[Any]=255   ,		__lowerCAmelCase				:	Any="shi-labs/oneformer_demo"   ,		__lowerCAmelCase				:	Optional[Any]="ade20k_panoptic.json"   ,		__lowerCAmelCase				:	Union[str, Any]=10   ,		)      ->					int:
         lowercase_     =      parent
         lowercase_     =      batch_size
         lowercase_     =      num_channels
         lowercase_     =      min_resolution
         lowercase_     =      max_resolution
         lowercase_     =      do_resize
         lowercase_     =      {"shortest_edge": 32, "longest_edge": 1333} if size is None else size
         lowercase_     =      do_normalize
         lowercase_     =      image_mean
         lowercase_     =      image_std
         lowercase_     =      class_info_file
         lowercase_     =      prepare_metadata(__lowerCAmelCase   ,		__lowerCAmelCase)
         lowercase_     =      num_text
         lowercase_     =      repo_path

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


   def __UpperCAmelCase       (   self				:	Dict)      ->					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				:	Optional[Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Tuple=False)      ->					Tuple:
         if not batched:
               lowercase_     =      image_inputs[0]
               if isinstance(__lowerCAmelCase   ,		Image.Image):
                     lowercase_ ,							lowercase_     =      image.size
               else:
                     lowercase_ ,							lowercase_     =      image.shape[1], image.shape[2]
               if w < h:
                     lowercase_     =      int(self.size["shortest_edge"] * h / w)
                     lowercase_     =      self.size["shortest_edge"]
               elif w > h:
                     lowercase_     =      self.size["shortest_edge"]
                     lowercase_     =      int(self.size["shortest_edge"] * w / h)
               else:
                     lowercase_     =      self.size["shortest_edge"]
                     lowercase_     =      self.size["shortest_edge"]

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

         return expected_height, expected_width


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					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 							lowercase						(						__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
   # only for test_image_processing_common.test_image_proc_to_json_string
   lowerCamelCase_    					=image_processing_class


   def __UpperCAmelCase       (   self				:	Dict)      ->					Dict:
         lowercase_     =      OneFormerImageProcessorTester(self)


   @property
   def __UpperCAmelCase       (   self				:	Dict)      ->					int:
         return self.image_processing_tester.prepare_image_processor_dict()


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					List[str]:
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"image_mean"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"image_std"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_normalize"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_resize"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"size"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"ignore_index"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"class_info_file"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"num_text"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"repo_path"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"metadata"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_reduce_labels"))


   def __UpperCAmelCase       (   self				:	Any)      ->					int:
         pass


   def __UpperCAmelCase       (   self				:	int)      ->					str:
         # Initialize image_processor
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase_     =      prepare_image_inputs(self.image_processing_tester   ,		equal_resolution=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase   ,		Image.Image)

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

         lowercase_ ,							lowercase_     =      self.image_processing_tester.get_expected_values(__lowerCAmelCase)

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

         # Test batched
         lowercase_ ,							lowercase_     =      self.image_processing_tester.get_expected_values(__lowerCAmelCase   ,		batched=__lowerCAmelCase)

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

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

         lowercase_ ,							lowercase_     =      self.image_processing_tester.get_expected_values(__lowerCAmelCase)

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

         # Test batched
         lowercase_ ,							lowercase_     =      self.image_processing_tester.get_expected_values(__lowerCAmelCase   ,		batched=__lowerCAmelCase)

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

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

         lowercase_ ,							lowercase_     =      self.image_processing_tester.get_expected_values(__lowerCAmelCase)

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

         # Test batched
         lowercase_ ,							lowercase_     =      self.image_processing_tester.get_expected_values(__lowerCAmelCase   ,		batched=__lowerCAmelCase)

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

         lowercase_     =      image_processor(
             __lowerCAmelCase   ,		["semantic"] * len(__lowerCAmelCase)   ,		__lowerCAmelCase   ,		return_tensors="pt"   ,		instance_id_to_semantic_id=__lowerCAmelCase   ,		pad_and_return_pixel_mask=__lowerCAmelCase   ,		)

         return inputs


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


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Union[str, Any]:
         def common(__lowerCAmelCase				:	int=False   ,		__lowerCAmelCase				:	List[Any]=None):
               lowercase_     =      self.comm_get_image_processor_inputs(
                   with_segmentation_maps=__lowerCAmelCase   ,		is_instance_map=__lowerCAmelCase   ,		segmentation_type=__lowerCAmelCase)

               lowercase_     =      inputs["mask_labels"]
               lowercase_     =      inputs["class_labels"]
               lowercase_     =      inputs["pixel_values"]
               lowercase_     =      inputs["text_inputs"]

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

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


   def __UpperCAmelCase       (   self				:	Dict)      ->					Dict:
         lowercase_     =      np.zeros((20, 50))
         lowercase_     =      1
         lowercase_     =      1
         lowercase_     =      1

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


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Dict:
         lowercase_     =      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"   ,		)
         lowercase_     =      self.image_processing_tester.get_fake_oneformer_outputs()

         lowercase_     =      fature_extractor.post_process_semantic_segmentation(__lowerCAmelCase)

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

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

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


   def __UpperCAmelCase       (   self				:	List[str])      ->					Tuple:
         lowercase_     =      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"   ,		)
         lowercase_     =      self.image_processing_tester.get_fake_oneformer_outputs()
         lowercase_     =      image_processor.post_process_instance_segmentation(__lowerCAmelCase   ,		threshold=0)

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


   def __UpperCAmelCase       (   self				:	List[Any])      ->					List[str]:
         lowercase_     =      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"   ,		)
         lowercase_     =      self.image_processing_tester.get_fake_oneformer_outputs()
         lowercase_     =      image_processor.post_process_panoptic_segmentation(__lowerCAmelCase   ,		threshold=0)

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







'''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 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	int)      ->					List[str]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()


   def __UpperCAmelCase       (   self				:	Dict)      ->					Any:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "bird"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

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

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2


   def __UpperCAmelCase       (   self				:	Tuple)      ->					List[str]:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "Chef in the kitchen"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

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

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2






