





import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

					if is_vision_available():
										from transformers import OneFormerImageProcessor
										from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
										from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
					from PIL import Image




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_="shi-labs/oneformer_demo"  )     ->		Tuple:






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



class 							_UpperCamelCase					(	unittest.TestCase     ):

							def __init__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Any=7					,   _SCREAMING_SNAKE_CASE: Optional[int]=3					,   _SCREAMING_SNAKE_CASE: List[Any]=30					,   _SCREAMING_SNAKE_CASE: Any=400					,   _SCREAMING_SNAKE_CASE: Optional[int]=None					,   _SCREAMING_SNAKE_CASE: List[Any]=True					,   _SCREAMING_SNAKE_CASE: List[Any]=True					,   _SCREAMING_SNAKE_CASE: Tuple=[0.5, 0.5, 0.5]					,   _SCREAMING_SNAKE_CASE: Any=[0.5, 0.5, 0.5]					,   _SCREAMING_SNAKE_CASE: Optional[int]=10					,   _SCREAMING_SNAKE_CASE: Dict=False					,   _SCREAMING_SNAKE_CASE: Tuple=255					,   _SCREAMING_SNAKE_CASE: Tuple="shi-labs/oneformer_demo"					,   _SCREAMING_SNAKE_CASE: List[Any]="ade20k_panoptic.json"					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=10					,   )       -> Optional[Any]:




														"""simple docstring"""


														UpperCamelCase_          =  parent
														UpperCamelCase_          =  batch_size
														UpperCamelCase_          =  num_channels
														UpperCamelCase_          =  min_resolution
														UpperCamelCase_          =  max_resolution
														UpperCamelCase_          =  do_resize
														UpperCamelCase_          =  {"shortest_edge": 32, "longest_edge": 1333} if size is None else size
														UpperCamelCase_          =  do_normalize
														UpperCamelCase_          =  image_mean
														UpperCamelCase_          =  image_std
														UpperCamelCase_          =  class_info_file
														UpperCamelCase_          =  prepare_metadata(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
														UpperCamelCase_          =  num_text
														UpperCamelCase_          =  repo_path

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

							def lowercase (		self: List[Any]		)       -> str:




														"""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: Optional[int]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: int=False		)       -> Union[str, Any]:




														"""simple docstring"""


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

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

														return expected_height, expected_width



							def lowercase (		self: List[str]		)       -> Dict:




														"""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 							_UpperCamelCase					(	lowercase_  ,						unittest.TestCase     ):
							_UpperCamelCase				:					Union[str, Any]         =   OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
							# only for test_image_processing_common.test_image_proc_to_json_string
							_UpperCamelCase				:					Optional[int]         =   image_processing_class

							def lowercase (		self: int		)       -> Optional[int]:




														"""simple docstring"""


														UpperCamelCase_          =  OneFormerImageProcessorTester(self		)

							@property
							def lowercase (		self: Tuple		)       -> Union[str, Any]:




														"""simple docstring"""


														return self.image_processing_tester.prepare_image_processor_dict()

							def lowercase (		self: str		)       -> Union[str, Any]:




														"""simple docstring"""


														UpperCamelCase_          =  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: Optional[int]		)       -> str:




														"""simple docstring"""


														pass

							def lowercase (		self: Union[str, Any]		)       -> Tuple:




														"""simple docstring"""


														UpperCamelCase_          =  self.image_processing_class(**self.image_processor_dict		)
														# create random PIL images
														UpperCamelCase_          =  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_          =  image_processor(image_inputs[0]					,   ["semantic"]					,   return_tensors="pt"		).pixel_values

														UpperCamelCase_       , UpperCamelCase_          =  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_          =  self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE					,   batched=_SCREAMING_SNAKE_CASE		)

														UpperCamelCase_          =  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: Union[str, Any]		)       -> List[Any]:




														"""simple docstring"""


														UpperCamelCase_          =  self.image_processing_class(**self.image_processor_dict		)
														# create random numpy tensors
														UpperCamelCase_          =  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_          =  image_processor(image_inputs[0]					,   ["semantic"]					,   return_tensors="pt"		).pixel_values

														UpperCamelCase_       , UpperCamelCase_          =  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_          =  self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE					,   batched=_SCREAMING_SNAKE_CASE		)

														UpperCamelCase_          =  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: Optional[int]		)       -> str:




														"""simple docstring"""


														UpperCamelCase_          =  self.image_processing_class(**self.image_processor_dict		)
														# create random PyTorch tensors
														UpperCamelCase_          =  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_          =  image_processor(image_inputs[0]					,   ["semantic"]					,   return_tensors="pt"		).pixel_values

														UpperCamelCase_       , UpperCamelCase_          =  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_          =  self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE					,   batched=_SCREAMING_SNAKE_CASE		)

														UpperCamelCase_          =  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: List[str]					,   _SCREAMING_SNAKE_CASE: str=False					,   _SCREAMING_SNAKE_CASE: Optional[int]=False					,   _SCREAMING_SNAKE_CASE: Union[str, Any]="np"		)       -> str:




														"""simple docstring"""


														UpperCamelCase_          =  self.image_processing_class(**self.image_processor_dict		)
														# prepare image and target
														UpperCamelCase_          =  self.image_processing_tester.num_labels
														UpperCamelCase_          =  None
														UpperCamelCase_          =  None
														UpperCamelCase_          =  prepare_image_inputs(self.image_processing_tester					,   equal_resolution=_SCREAMING_SNAKE_CASE		)
														if with_segmentation_maps:
																					UpperCamelCase_          =  num_labels
																					if is_instance_map:
																												UpperCamelCase_          =  list(range(_SCREAMING_SNAKE_CASE		)		) * 2
																												UpperCamelCase_          =  dict(enumerate(_SCREAMING_SNAKE_CASE		)		)
																					UpperCamelCase_          =  [
																					    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_          =  [Image.fromarray(_SCREAMING_SNAKE_CASE		) for annotation in annotations]

														UpperCamelCase_          =  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: Optional[Any]		)       -> int:




														"""simple docstring"""


														pass

							def lowercase (		self: Union[str, Any]		)       -> List[str]:




														"""simple docstring"""


														def common(_SCREAMING_SNAKE_CASE: Union[str, Any]=False					,   _SCREAMING_SNAKE_CASE: Tuple=None		):
																					UpperCamelCase_          =  self.comm_get_image_processor_inputs(
																					    with_segmentation_maps=_SCREAMING_SNAKE_CASE					,   is_instance_map=_SCREAMING_SNAKE_CASE					,   segmentation_type=_SCREAMING_SNAKE_CASE		)

																					UpperCamelCase_          =  inputs["mask_labels"]
																					UpperCamelCase_          =  inputs["class_labels"]
																					UpperCamelCase_          =  inputs["pixel_values"]
																					UpperCamelCase_          =  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: Dict		)       -> Union[str, Any]:




														"""simple docstring"""


														UpperCamelCase_          =  np.zeros((20, 50)		)
														UpperCamelCase_          =  1
														UpperCamelCase_          =  1
														UpperCamelCase_          =  1

														UpperCamelCase_          =  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: Union[str, Any]		)       -> List[str]:




														"""simple docstring"""


														UpperCamelCase_          =  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_          =  self.image_processing_tester.get_fake_oneformer_outputs()

														UpperCamelCase_          =  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_          =  [(1, 4) for i in range(self.image_processing_tester.batch_size		)]
														UpperCamelCase_          =  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		)       -> Optional[Any]:




														"""simple docstring"""


														UpperCamelCase_          =  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_          =  self.image_processing_tester.get_fake_oneformer_outputs()
														UpperCamelCase_          =  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: Any		)       -> Dict:




														"""simple docstring"""


														UpperCamelCase_          =  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_          =  self.image_processing_tester.get_fake_oneformer_outputs()
														UpperCamelCase_          =  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)		)








_UpperCAmelCase        =				{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

