


'''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 				lowerCAmelCase__	(    lowerCamelCase					:      Dict  ,lowerCamelCase					:      int="shi-labs/oneformer_demo"       ):

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


class 			__lowerCamelCase			(	unittest.TestCase	):
				"""simple docstring"""


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

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


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

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

											return expected_height, expected_width


				def        A   (  self      :							Optional[int]):
											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 			__lowerCamelCase			(	a__    , unittest.TestCase	):
				"""simple docstring"""
				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        A   (  self      :							List[Any]):
											_A					:     Dict     = OneFormerImageProcessorTester(self)


				@property
				def        A   (  self      :							int):
											return self.image_processing_tester.prepare_image_processor_dict()


				def        A   (  self      :							str):
											_A					:     Union[str, Any]     = 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        A   (  self      :							Union[str, Any]):
											pass


				def        A   (  self      :							List[Any]):
											# Initialize image_processor
											_A					:     Any     = self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											_A					:     List[str]     = 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
											_A					:     Dict     = image_processor(image_inputs[0]				,							['semantic']				,							return_tensors='pt').pixel_values

											_A					:     Union[str, Any]     = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE)

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

											# Test batched
											_A					:     int     = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE				,							batched=SCREAMING_SNAKE_CASE)

											_A					:     Optional[int]     = 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        A   (  self      :							str):
											# Initialize image_processor
											_A					:     List[str]     = self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											_A					:     Optional[Any]     = prepare_image_inputs(self.image_processing_tester				,							equal_resolution=SCREAMING_SNAKE_CASE				,							numpify=SCREAMING_SNAKE_CASE)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE				,							np.ndarray)

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

											_A					:     Optional[Any]     = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE)

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

											# Test batched
											_A					:     Optional[int]     = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE				,							batched=SCREAMING_SNAKE_CASE)

											_A					:     Tuple     = 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        A   (  self      :							Union[str, Any]):
											# Initialize image_processor
											_A					:     str     = self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											_A					:     List[str]     = 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
											_A					:     Any     = image_processor(image_inputs[0]				,							['semantic']				,							return_tensors='pt').pixel_values

											_A					:     str     = 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
											_A					:     str     = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE				,							batched=SCREAMING_SNAKE_CASE)

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

											_A					:     Any     = 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        A   (  self      :							Dict):
											pass


				def        A   (  self      :							int):
											def common(SCREAMING_SNAKE_CASE      :							Tuple=False				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None):
																		_A					:     List[Any]     = self.comm_get_image_processor_inputs(
																		    with_segmentation_maps=SCREAMING_SNAKE_CASE				,							is_instance_map=SCREAMING_SNAKE_CASE				,							segmentation_type=SCREAMING_SNAKE_CASE)

																		_A					:     Dict     = inputs["mask_labels"]
																		_A					:     Tuple     = inputs["class_labels"]
																		_A					:     List[str]     = inputs["pixel_values"]
																		_A					:     Optional[Any]     = 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        A   (  self      :							Optional[Any]):
											_A					:     List[Any]     = np.zeros((20, 50))
											_A					:     Any     = 1
											_A					:     Any     = 1
											_A					:     List[Any]     = 1

											_A					:     int     = binary_mask_to_rle(SCREAMING_SNAKE_CASE)
											self.assertEqual(len(SCREAMING_SNAKE_CASE)				,							4)
											self.assertEqual(rle[0]				,							21)
											self.assertEqual(rle[1]				,							45)


				def        A   (  self      :							Union[str, Any]):
											_A					:     int     = self.image_processing_class(
											    num_labels=self.image_processing_tester.num_classes				,							max_seq_length=77				,							task_seq_length=77				,							class_info_file='ade20k_panoptic.json'				,							num_text=self.image_processing_tester.num_text				,							repo_path='shi-labs/oneformer_demo'				,							)
											_A					:     Optional[Any]     = self.image_processing_tester.get_fake_oneformer_outputs()

											_A					:     Optional[Any]     = fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE)

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

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

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


				def        A   (  self      :							Optional[Any]):
											_A					:     Optional[int]     = self.image_processing_class(
											    num_labels=self.image_processing_tester.num_classes				,							max_seq_length=77				,							task_seq_length=77				,							class_info_file='ade20k_panoptic.json'				,							num_text=self.image_processing_tester.num_text				,							repo_path='shi-labs/oneformer_demo'				,							)
											_A					:     Any     = self.image_processing_tester.get_fake_oneformer_outputs()
											_A					:     int     = image_processor.post_process_instance_segmentation(SCREAMING_SNAKE_CASE				,							threshold=0)

											self.assertTrue(len(SCREAMING_SNAKE_CASE) == self.image_processing_tester.batch_size)
											for el in segmentation:
																		self.assertTrue('segmentation' in el)
																		self.assertTrue('segments_info' in el)
																		self.assertEqual(type(el['segments_info'])				,							SCREAMING_SNAKE_CASE)
																		self.assertEqual(
																		    el['segmentation'].shape				,							(self.image_processing_tester.height, self.image_processing_tester.width))


				def        A   (  self      :							Tuple):
											_A					:     Union[str, 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'				,							)
											_A					:     int     = self.image_processing_tester.get_fake_oneformer_outputs()
											_A					:     int     = 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))










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int = 10       ):
       if not isinstance(lowerCamelCase  ,lowerCamelCase       ) or n < 0:
              raise ValueError('Invalid input'       )
       _A					:     Optional[Any]     = 10**n
       _A					:     List[str]     = 28433 * (pow(2  ,7830457  ,lowerCamelCase       )) + 1
       return str(number % modulus       )


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print(f"""{solution(10) = }""")






