

import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

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

if is_vision_available():
						from PIL import Image







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



class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







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

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



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

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

									return expected_height, expected_width




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[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    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[int]      							=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
			# only for test_image_processing_common.test_image_proc_to_json_string
			_UpperCAmelCase       :							int      							=					image_processing_class



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	OneFormerImageProcessorTester(self)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									return self.image_processing_tester.prepare_image_processor_dict()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									pass



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									# Initialize image_processor
									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	prepare_image_inputs(self.image_processing_tester       ,						equal_resolution=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						Image.Image)

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

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	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
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Tuple  		=	self.image_processing_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									# Initialize image_processor
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									SCREAMING_SNAKE_CASE_:			Dict  		=	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
									SCREAMING_SNAKE_CASE_:			int  		=	image_processor(image_inputs[0]       ,						["semantic"]       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Tuple  		=	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
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	self.image_processing_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Dict  		=	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
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			str  		=	self.image_processing_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									pass



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									def common(lowerCAmelCase__  :					Any=False       ,						lowerCAmelCase__  :					Any=None):
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.comm_get_image_processor_inputs(
															    with_segmentation_maps=lowerCAmelCase__       ,						is_instance_map=lowerCAmelCase__       ,						segmentation_type=lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			List[Any]  		=	inputs["mask_labels"]
															SCREAMING_SNAKE_CASE_:			Any  		=	inputs["class_labels"]
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	inputs["pixel_values"]
															SCREAMING_SNAKE_CASE_:			Tuple  		=	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 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	np.zeros((20, 50))
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	1
									SCREAMING_SNAKE_CASE_:			int  		=	1
									SCREAMING_SNAKE_CASE_:			List[str]  		=	1

									SCREAMING_SNAKE_CASE_:			Any  		=	binary_mask_to_rle(lowerCAmelCase__)
									self.assertEqual(len(lowerCAmelCase__)       ,						4)
									self.assertEqual(rle[0]       ,						21)
									self.assertEqual(rle[1]       ,						45)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Dict  		=	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"       ,						)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.image_processing_tester.get_fake_oneformer_outputs()

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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,
									    )       ,						)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	[(1, 4) for i in range(self.image_processing_tester.batch_size)]
									SCREAMING_SNAKE_CASE_:			Dict  		=	fature_extractor.post_process_semantic_segmentation(lowerCAmelCase__       ,						target_sizes=lowerCAmelCase__)

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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"       ,						)
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.image_processing_tester.get_fake_oneformer_outputs()
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			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"       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.image_processing_tester.get_fake_oneformer_outputs()
									SCREAMING_SNAKE_CASE_:			Any  		=	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))









import torch

from diffusers import StableDiffusionPipeline


lowerCAmelCase							:  Any            =						"""path-to-your-trained-model"""
lowerCAmelCase							:  int            =						StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

lowerCAmelCase							:  Union[str, Any]            =						"""A photo of sks dog in a bucket"""
lowerCAmelCase							:  Any            =						pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






