




'''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 					UpperCamelCase_(     snake_case							:	Any							,    snake_case							:	Union[str, Any]="shi-labs/oneformer_demo"					):

							'''simple docstring'''






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




class 			_snake_case		(     unittest.TestCase							):




			def __init__( self    ,							a__    ,							a__=7    ,							a__=3    ,							a__=30    ,							a__=400    ,							a__=None    ,							a__=True    ,							a__=True    ,							a__=[0.5, 0.5, 0.5]    ,							a__=[0.5, 0.5, 0.5]    ,							a__=10    ,							a__=False    ,							a__=255    ,							a__="shi-labs/oneformer_demo"    ,							a__="ade20k_panoptic.json"    ,							a__=10    ,							)    ->    Tuple:

										'''simple docstring'''




										snake_case_      =				parent
										snake_case_      =				batch_size
										snake_case_      =				num_channels
										snake_case_      =				min_resolution
										snake_case_      =				max_resolution
										snake_case_      =				do_resize
										snake_case_      =				{'shortest_edge': 32, 'longest_edge': 1_333} if size is None else size
										snake_case_      =				do_normalize
										snake_case_      =				image_mean
										snake_case_      =				image_std
										snake_case_      =				class_info_file
										snake_case_      =				prepare_metadata(a__    ,							a__							)
										snake_case_      =				num_text
										snake_case_      =				repo_path

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




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''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        lowerCAmelCase__    ( self    ,							a__    ,							a__=False							)    ->    int:

										'''simple docstring'''




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

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

										return expected_height, expected_width




			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''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 			_snake_case		(     snake_case_						,						unittest.TestCase							):
			lowerCAmelCase_					:				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
			lowerCAmelCase_					:				List[Any]							=   image_processing_class




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

										'''simple docstring'''




										snake_case_      =				OneFormerImageProcessorTester(self							)




			@property
			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										return self.image_processing_tester.prepare_image_processor_dict()




			def        lowerCAmelCase__    ( self							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(a__    ,							"image_mean"							)							)
										self.assertTrue(hasattr(a__    ,							"image_std"							)							)
										self.assertTrue(hasattr(a__    ,							"do_normalize"							)							)
										self.assertTrue(hasattr(a__    ,							"do_resize"							)							)
										self.assertTrue(hasattr(a__    ,							"size"							)							)
										self.assertTrue(hasattr(a__    ,							"ignore_index"							)							)
										self.assertTrue(hasattr(a__    ,							"class_info_file"							)							)
										self.assertTrue(hasattr(a__    ,							"num_text"							)							)
										self.assertTrue(hasattr(a__    ,							"repo_path"							)							)
										self.assertTrue(hasattr(a__    ,							"metadata"							)							)
										self.assertTrue(hasattr(a__    ,							"do_reduce_labels"							)							)




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

										'''simple docstring'''




										pass




			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										snake_case_      =				self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										snake_case_      =				prepare_image_inputs(self.image_processing_tester    ,							equal_resolution=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__    ,							Image.Image							)

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

										snake_case_      =				self.image_processing_tester.get_expected_values(a__							)

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

										# Test batched
										snake_case_      =				self.image_processing_tester.get_expected_values(a__    ,							batched=a__							)

										snake_case_      =				image_processor(
										    a__    ,							["semantic"] * len(a__							)    ,							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							)    ->    int:

										'''simple docstring'''




										snake_case_      =				self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										snake_case_      =				prepare_image_inputs(self.image_processing_tester    ,							equal_resolution=a__    ,							numpify=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__    ,							np.ndarray							)

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

										snake_case_      =				self.image_processing_tester.get_expected_values(a__							)

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

										# Test batched
										snake_case_      =				self.image_processing_tester.get_expected_values(a__    ,							batched=a__							)

										snake_case_      =				image_processor(
										    a__    ,							["semantic"] * len(a__							)    ,							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							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										snake_case_      =				prepare_image_inputs(self.image_processing_tester    ,							equal_resolution=a__    ,							torchify=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__    ,							torch.Tensor							)

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

										snake_case_      =				self.image_processing_tester.get_expected_values(a__							)

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

										# Test batched
										snake_case_      =				self.image_processing_tester.get_expected_values(a__    ,							batched=a__							)

										snake_case_      =				image_processor(
										    a__    ,							["semantic"] * len(a__							)    ,							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    ,							a__=False    ,							a__=False    ,							a__="np"							)    ->    Union[str, Any]:

										'''simple docstring'''




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

										snake_case_      =				image_processor(
										    a__    ,							["semantic"] * len(a__							)    ,							a__    ,							return_tensors="pt"    ,							instance_id_to_semantic_id=a__    ,							pad_and_return_pixel_mask=a__    ,							)

										return inputs




			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										pass




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										def common(a__=False    ,							a__=None							):
																	snake_case_      =				self.comm_get_image_processor_inputs(
																	    with_segmentation_maps=a__    ,							is_instance_map=a__    ,							segmentation_type=a__							)

																	snake_case_      =				inputs['mask_labels']
																	snake_case_      =				inputs['class_labels']
																	snake_case_      =				inputs['pixel_values']
																	snake_case_      =				inputs['text_inputs']

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

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




			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




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

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




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

										'''simple docstring'''




										snake_case_      =				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_      =				self.image_processing_tester.get_fake_oneformer_outputs()

										snake_case_      =				fature_extractor.post_process_semantic_segmentation(a__							)

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

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

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




			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										snake_case_      =				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_      =				self.image_processing_tester.get_fake_oneformer_outputs()
										snake_case_      =				image_processor.post_process_instance_segmentation(a__    ,							threshold=0							)

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




			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				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_      =				self.image_processing_tester.get_fake_oneformer_outputs()
										snake_case_      =				image_processor.post_process_panoptic_segmentation(a__    ,							threshold=0							)

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









'''simple docstring'''





from __future__ import annotations

import numpy as np


def 					UpperCamelCase_(     snake_case							:	np.ndarray					):

							'''simple docstring'''






							snake_case_ ,				snake_case_      =				np.shape(snake_case					)
							if rows != columns:
														snake_case_      =				(
														    "'table' has to be of square shaped array but got a "
														    f'{rows}x{columns} array:\n{table}'
														)
														raise ValueError(snake_case					)

							snake_case_      =				np.zeros((rows, columns)					)
							snake_case_      =				np.zeros((rows, columns)					)
							for i in range(snake_case					):
														for j in range(snake_case					):
																					snake_case_      =				sum(lower[i][k] * upper[k][j] for k in range(snake_case					)					)
																					if upper[j][j] == 0:
																												raise ArithmeticError("No LU decomposition exists"					)
																					snake_case_      =				(table[i][j] - total) / upper[j][j]
														snake_case_      =				1
														for j in range(snake_case							,    snake_case					):
																					snake_case_      =				sum(lower[i][k] * upper[k][j] for k in range(snake_case					)					)
																					snake_case_      =				table[i][j] - total
							return lower, upper


if __name__ == "__main__":
		import doctest

		doctest.testmod()




