






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 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							Optional[Any]="shi-labs/oneformer_demo"      )     -> List[str]:





				'''simple docstring'''


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


class 					_snake_case						(			unittest.TestCase ):






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

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






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

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

					return expected_height, expected_width






	def    lowerCAmelCase_							(		self       )						->    str:
					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 					_snake_case						(			_A       ,    unittest.TestCase ):
	_A          =   OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	_A          =   image_processing_class






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Union[str, Any]         =   OneFormerImageProcessorTester(self       )






	@property
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					return self.image_processing_tester.prepare_image_processor_dict()






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :str         =   self.image_processing_class(**self.image_processor_dict       )
					self.assertTrue(hasattr(UpperCamelCase					,"image_mean"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"image_std"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"do_normalize"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"do_resize"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"size"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"ignore_index"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"class_info_file"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"num_text"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"repo_path"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"metadata"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"do_reduce_labels"       )       )






	def    lowerCAmelCase_							(		self       )						->    str:
					pass






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

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

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

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

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

					snake_case__      :Optional[int]         =   image_processor(
					    UpperCamelCase					,["semantic"] * len(UpperCamelCase       )					,return_tensors="pt"       ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )					,)






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					# Initialize image_processor
					snake_case__      :Any         =   self.image_processing_class(**self.image_processor_dict       )
					# create random numpy tensors
					snake_case__      :Union[str, Any]         =   prepare_image_inputs(self.image_processing_tester					,equal_resolution=UpperCamelCase					,numpify=UpperCamelCase       )
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase					,np.ndarray       )

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

					snake_case__    ,						snake_case__      :str         =   self.image_processing_tester.get_expected_values(UpperCamelCase       )

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

					# Test batched
					snake_case__    ,						snake_case__      :int         =   self.image_processing_tester.get_expected_values(UpperCamelCase					,batched=UpperCamelCase       )

					snake_case__      :Tuple         =   image_processor(
					    UpperCamelCase					,["semantic"] * len(UpperCamelCase       )					,return_tensors="pt"       ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )					,)






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					# Initialize image_processor
					snake_case__      :Optional[int]         =   self.image_processing_class(**self.image_processor_dict       )
					# create random PyTorch tensors
					snake_case__      :Tuple         =   prepare_image_inputs(self.image_processing_tester					,equal_resolution=UpperCamelCase					,torchify=UpperCamelCase       )
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase					,torch.Tensor       )

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

					snake_case__    ,						snake_case__      :List[Any]         =   self.image_processing_tester.get_expected_values(UpperCamelCase       )

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

					# Test batched
					snake_case__    ,						snake_case__      :Any         =   self.image_processing_tester.get_expected_values(UpperCamelCase					,batched=UpperCamelCase       )

					snake_case__      :List[Any]         =   image_processor(
					    UpperCamelCase					,["semantic"] * len(UpperCamelCase       )					,return_tensors="pt"       ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )					,)






	def    lowerCAmelCase_							(		self					,UpperCamelCase=False					,UpperCamelCase=False					,UpperCamelCase="np"       )						->    List[Any]:
					snake_case__      :Any         =   self.image_processing_class(**self.image_processor_dict       )
					# prepare image and target
					snake_case__      :Dict         =   self.image_processing_tester.num_labels
					snake_case__      :Tuple         =   None
					snake_case__      :Optional[Any]         =   None
					snake_case__      :Dict         =   prepare_image_inputs(self.image_processing_tester					,equal_resolution=UpperCamelCase       )
					if with_segmentation_maps:
									snake_case__      :str         =   num_labels
									if is_instance_map:
													snake_case__      :List[str]         =   list(range(UpperCamelCase       )       ) * 2
													snake_case__      :Tuple         =   dict(enumerate(UpperCamelCase       )       )
									snake_case__      :int         =   [
									    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__      :int         =   [Image.fromarray(UpperCamelCase       ) for annotation in annotations]

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

					return inputs






	def    lowerCAmelCase_							(		self       )						->    int:
					pass






	def    lowerCAmelCase_							(		self       )						->    int:
					def common(UpperCamelCase=False					,UpperCamelCase=None       ):
									snake_case__      :Any         =   self.comm_get_image_processor_inputs(
									    with_segmentation_maps=UpperCamelCase					,is_instance_map=UpperCamelCase					,segmentation_type=UpperCamelCase       )

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

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

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






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :Optional[Any]         =   np.zeros((20, 50)       )
					snake_case__      :Optional[Any]         =   1
					snake_case__      :Optional[Any]         =   1
					snake_case__      :Any         =   1

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






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :List[str]         =   self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes					,max_seq_length=77					,task_seq_length=77					,class_info_file="ade20k_panoptic.json"					,num_text=self.image_processing_tester.num_text					,repo_path="shi-labs/oneformer_demo"					,)
					snake_case__      :Optional[int]         =   self.image_processing_tester.get_fake_oneformer_outputs()

					snake_case__      :List[Any]         =   fature_extractor.post_process_semantic_segmentation(UpperCamelCase       )

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

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

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






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :List[str]         =   self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes					,max_seq_length=77					,task_seq_length=77					,class_info_file="ade20k_panoptic.json"					,num_text=self.image_processing_tester.num_text					,repo_path="shi-labs/oneformer_demo"					,)
					snake_case__      :List[str]         =   self.image_processing_tester.get_fake_oneformer_outputs()
					snake_case__      :Dict         =   image_processor.post_process_instance_segmentation(UpperCamelCase					,threshold=0       )

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






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :str         =   self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes					,max_seq_length=77					,task_seq_length=77					,class_info_file="ade20k_panoptic.json"					,num_text=self.image_processing_tester.num_text					,repo_path="shi-labs/oneformer_demo"					,)
					snake_case__      :Tuple         =   self.image_processing_tester.get_fake_oneformer_outputs()
					snake_case__      :List[Any]         =   image_processor.post_process_panoptic_segmentation(UpperCamelCase					,threshold=0       )

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







import os
import sys
import unittest


__UpperCAmelCase							:   str							=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase							:   Tuple							=  os.path.join(git_repo_path, "src", "diffusers")


class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Tuple         =   find_backend("    if not is_torch_available():"       )
					self.assertEqual(UpperCamelCase					,"torch"       )

					# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
					# self.assertEqual(backend_with_underscore, "tensorflow_text")

					snake_case__      :Tuple         =   find_backend("    if not (is_torch_available() and is_transformers_available()):"       )
					self.assertEqual(UpperCamelCase					,"torch_and_transformers"       )

					# double_backend_with_underscore = find_backend(
					#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
					# )
					# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

					snake_case__      :str         =   find_backend(
					    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"       )
					self.assertEqual(UpperCamelCase					,"torch_and_transformers_and_onnx"       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :int         =   read_init()
					# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
					self.assertIn("torch"					,UpperCamelCase       )
					self.assertIn("torch_and_transformers"					,UpperCamelCase       )
					self.assertIn("flax_and_transformers"					,UpperCamelCase       )
					self.assertIn("torch_and_transformers_and_onnx"					,UpperCamelCase       )

					# Likewise, we can't assert on the exact content of a key
					self.assertIn("UNet2DModel"					,objects["torch"]       )
					self.assertIn("FlaxUNet2DConditionModel"					,objects["flax"]       )
					self.assertIn("StableDiffusionPipeline"					,objects["torch_and_transformers"]       )
					self.assertIn("FlaxStableDiffusionPipeline"					,objects["flax_and_transformers"]       )
					self.assertIn("LMSDiscreteScheduler"					,objects["torch_and_scipy"]       )
					self.assertIn("OnnxStableDiffusionPipeline"					,objects["torch_and_transformers_and_onnx"]       )






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Union[str, Any]         =   create_dummy_object("CONSTANT"					,"'torch'"       )
					self.assertEqual(UpperCamelCase					,"\nCONSTANT = None\n"       )

					snake_case__      :Optional[Any]         =   create_dummy_object("function"					,"'torch'"       )
					self.assertEqual(
					    UpperCamelCase					,"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"       )

					snake_case__      :str         =   "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
					snake_case__      :List[str]         =   create_dummy_object("FakeClass"					,"'torch'"       )
					self.assertEqual(UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Tuple         =   "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
					snake_case__      :int         =   create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}       )
					self.assertEqual(dummy_files["torch"]					,UpperCamelCase       )