


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







class        snake_case_   (unittest.TestCase      ):
					def __init__(      self   :Optional[Any]			,__snake_case   :str			,__snake_case   :Dict=7			,__snake_case   :Any=3			,__snake_case   :List[Any]=30			,__snake_case   :Optional[int]=4_00			,__snake_case   :Dict=None			,__snake_case   :Optional[int]=True			,__snake_case   :Optional[Any]=True			,__snake_case   :List[str]=[0.5, 0.5, 0.5]			,__snake_case   :int=[0.5, 0.5, 0.5]			,__snake_case   :int=10			,__snake_case   :List[Any]=False			,__snake_case   :Any=2_55			,__snake_case   :Any="shi-labs/oneformer_demo"			,__snake_case   :List[Any]="ade20k_panoptic.json"			,__snake_case   :int=10			,)					->    str:
												a__             =     parent
												a__             =     batch_size
												a__             =     num_channels
												a__             =     min_resolution
												a__             =     max_resolution
												a__             =     do_resize
												a__             =     {'shortest_edge': 32, 'longest_edge': 13_33} if size is None else size
												a__             =     do_normalize
												a__             =     image_mean
												a__             =     image_std
												a__             =     class_info_file
												a__             =     prepare_metadata(__snake_case			,__snake_case      )
												a__             =     num_text
												a__             =     repo_path

												# for the post_process_functions
												a__             =     2
												a__             =     10
												a__             =     10
												a__             =     3
												a__             =     4
												a__             =     num_labels
												a__             =     do_reduce_labels
												a__             =     ignore_index
					def  lowerCamelCase__(      self   :Optional[int]      )					->    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  lowerCamelCase__(      self   :List[str]			,__snake_case   :Optional[Any]			,__snake_case   :Tuple=False      )					->    Dict:
												if not batched:
																			a__             =     image_inputs[0]
																			if isinstance(__snake_case			,Image.Image      ):
																										a__      ,					a__             =     image.size
																			else:
																										a__      ,					a__             =     image.shape[1], image.shape[2]
																			if w < h:
																										a__             =     int(self.size['shortest_edge'] * h / w      )
																										a__             =     self.size['shortest_edge']
																			elif w > h:
																										a__             =     self.size['shortest_edge']
																										a__             =     int(self.size['shortest_edge'] * w / h      )
																			else:
																										a__             =     self.size['shortest_edge']
																										a__             =     self.size['shortest_edge']

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

												return expected_height, expected_width



					def  lowerCamelCase__(      self   :Optional[int]      )					->    Tuple:
												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_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    List[str]          =			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  lowerCamelCase__(      self   :Union[str, Any]      )					->    List[Any]:
												a__             =     OneFormerImageProcessorTester(self      )
					@property
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[int]:
												return self.image_processing_tester.prepare_image_processor_dict()
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    List[str]:
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(__snake_case			,'image_mean'      )      )
												self.assertTrue(hasattr(__snake_case			,'image_std'      )      )
												self.assertTrue(hasattr(__snake_case			,'do_normalize'      )      )
												self.assertTrue(hasattr(__snake_case			,'do_resize'      )      )
												self.assertTrue(hasattr(__snake_case			,'size'      )      )
												self.assertTrue(hasattr(__snake_case			,'ignore_index'      )      )
												self.assertTrue(hasattr(__snake_case			,'class_info_file'      )      )
												self.assertTrue(hasattr(__snake_case			,'num_text'      )      )
												self.assertTrue(hasattr(__snake_case			,'repo_path'      )      )
												self.assertTrue(hasattr(__snake_case			,'metadata'      )      )
												self.assertTrue(hasattr(__snake_case			,'do_reduce_labels'      )      )
					def  lowerCamelCase__(      self   :str      )					->    int:
												pass
					def  lowerCamelCase__(      self   :List[str]      )					->    Tuple:
												# Initialize image_processor
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												a__             =     prepare_image_inputs(self.image_processing_tester			,equal_resolution=__snake_case      )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case			,Image.Image      )

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

												a__      ,					a__             =     self.image_processing_tester.get_expected_values(__snake_case      )

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

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

												a__             =     image_processor(
												    __snake_case			,['semantic'] * len(__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  lowerCamelCase__(      self   :List[Any]      )					->    Optional[Any]:
												# Initialize image_processor
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												a__             =     prepare_image_inputs(self.image_processing_tester			,equal_resolution=__snake_case			,numpify=__snake_case      )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case			,np.ndarray      )

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

												a__      ,					a__             =     self.image_processing_tester.get_expected_values(__snake_case      )

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

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

												a__             =     image_processor(
												    __snake_case			,['semantic'] * len(__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  lowerCamelCase__(      self   :Optional[Any]      )					->    Tuple:
												# Initialize image_processor
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												a__             =     prepare_image_inputs(self.image_processing_tester			,equal_resolution=__snake_case			,torchify=__snake_case      )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case			,torch.Tensor      )

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

												a__      ,					a__             =     self.image_processing_tester.get_expected_values(__snake_case      )

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

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

												a__             =     image_processor(
												    __snake_case			,['semantic'] * len(__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  lowerCamelCase__(      self   :List[Any]			,__snake_case   :Union[str, Any]=False			,__snake_case   :Any=False			,__snake_case   :Union[str, Any]="np"      )					->    Union[str, Any]:
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												# prepare image and target
												a__             =     self.image_processing_tester.num_labels
												a__             =     None
												a__             =     None
												a__             =     prepare_image_inputs(self.image_processing_tester			,equal_resolution=__snake_case      )
												if with_segmentation_maps:
																			a__             =     num_labels
																			if is_instance_map:
																										a__             =     list(range(__snake_case      )      ) * 2
																										a__             =     dict(enumerate(__snake_case      )      )
																			a__             =     [
																			    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__             =     [Image.fromarray(__snake_case      ) for annotation in annotations]

												a__             =     image_processor(
												    __snake_case			,['semantic'] * len(__snake_case      )			,__snake_case			,return_tensors='pt'			,instance_id_to_semantic_id=__snake_case			,pad_and_return_pixel_mask=__snake_case			,)

												return inputs
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    List[Any]:
												pass
					def  lowerCamelCase__(      self   :int      )					->    Union[str, Any]:
												def common(__snake_case   :Any=False			,__snake_case   :Any=None      ):
																			a__             =     self.comm_get_image_processor_inputs(
																			    with_segmentation_maps=__snake_case			,is_instance_map=__snake_case			,segmentation_type=__snake_case      )

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

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

												common()
												common(is_instance_map=__snake_case      )
												common(is_instance_map=__snake_case			,segmentation_type='pil'      )
												common(is_instance_map=__snake_case			,segmentation_type='pil'      )
					def  lowerCamelCase__(      self   :int      )					->    Optional[int]:
												a__             =     np.zeros((20, 50)      )
												a__             =     1
												a__             =     1
												a__             =     1

												a__             =     binary_mask_to_rle(__snake_case      )
												self.assertEqual(len(__snake_case      )			,4      )
												self.assertEqual(rle[0]			,21      )
												self.assertEqual(rle[1]			,45      )
					def  lowerCamelCase__(      self   :Dict      )					->    Optional[Any]:
												a__             =     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__             =     self.image_processing_tester.get_fake_oneformer_outputs()

												a__             =     fature_extractor.post_process_semantic_segmentation(__snake_case      )

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

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

												self.assertEqual(segmentation[0].shape			,target_sizes[0]      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Any:
												a__             =     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__             =     self.image_processing_tester.get_fake_oneformer_outputs()
												a__             =     image_processor.post_process_instance_segmentation(__snake_case			,threshold=0      )

												self.assertTrue(len(__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']      )			,__snake_case      )
																			self.assertEqual(
																			    el['segmentation'].shape			,(self.image_processing_tester.height, self.image_processing_tester.width)      )



					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												a__             =     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__             =     self.image_processing_tester.get_fake_oneformer_outputs()
												a__             =     image_processor.post_process_panoptic_segmentation(__snake_case			,threshold=0      )

												self.assertTrue(len(__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']      )			,__snake_case      )
																			self.assertEqual(
																			    el['segmentation'].shape			,(self.image_processing_tester.height, self.image_processing_tester.width)      )








import argparse


snake_case							:       int     			=      '''docs/source/_static/js/custom.js'''






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]   ):
							with open(__lowerCAmelCase					,    encoding='utf-8'					,    newline='\n'   ) as f:
														a__             =     f.readlines()
							a__             =     0

							# First let's put the right version
							while not lines[index].startswith('const stableVersion ='   ):
														index += 1
							a__             =     F'const stableVersion = "v{version}"\n'

							# Then update the dictionary
							while not lines[index].startswith('const versionMapping = {'   ):
														index += 1

							# We go until the end
							while not lines[index].startswith('}'   ):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'    "v{version}": "v{version}",\n'

							with open(__lowerCAmelCase					,    'w'					,    encoding='utf-8'					,    newline='\n'   ) as f:
														f.writelines(__lowerCAmelCase   )


if __name__ == "__main__":
						snake_case							:       Optional[Any]     			=      argparse.ArgumentParser()
						parser.add_argument('''--version''', help='''Release version.''')
						snake_case							:       Optional[int]     			=      parser.parse_args()
						update_custom_js(args.version)




