




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_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      List[str]="shi-labs/oneformer_demo"      )     ->       Union[str, Any]:





				"""simple docstring"""


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







class     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







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



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

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


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



							'''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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=False							)    ->      int:



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

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

							return expected_height, expected_width






			def       lowercase_    (      self							)    ->      Union[str, 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     __lowerCAmelCase (       __magic_name__			,				unittest.TestCase ):

			"""simple docstring"""







			snake_case_   		=       OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
			# only for test_image_processing_common.test_image_proc_to_json_string
			snake_case_   		=       image_processing_class


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    OneFormerImageProcessorTester(self							)


			@property
			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							return self.image_processing_tester.prepare_image_processor_dict()


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



							'''simple docstring'''
							__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							pass


			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							# Initialize image_processor
							__lowerCamelCase		       =    self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							__lowerCamelCase		       =    prepare_image_inputs(self.image_processing_tester			,						equal_resolution=lowerCamelCase__							)
							for image in image_inputs:
											self.assertIsInstance(lowerCamelCase__			,						Image.Image							)

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

							__lowerCamelCase       ,						__lowerCamelCase		       =    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
							__lowerCamelCase       ,						__lowerCamelCase		       =    self.image_processing_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__							)

							__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							# Initialize image_processor
							__lowerCamelCase		       =    self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							__lowerCamelCase		       =    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
							__lowerCamelCase		       =    image_processor(image_inputs[0]			,						['semantic']			,						return_tensors='pt'							).pixel_values

							__lowerCamelCase       ,						__lowerCamelCase		       =    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
							__lowerCamelCase       ,						__lowerCamelCase		       =    self.image_processing_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__							)

							__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							# Initialize image_processor
							__lowerCamelCase		       =    self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							__lowerCamelCase		       =    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
							__lowerCamelCase		       =    image_processor(image_inputs[0]			,						['semantic']			,						return_tensors='pt'							).pixel_values

							__lowerCamelCase       ,						__lowerCamelCase		       =    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
							__lowerCamelCase       ,						__lowerCamelCase		       =    self.image_processing_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__							)

							__lowerCamelCase		       =    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       lowercase_    (      self			,						lowerCamelCase__=False			,						lowerCamelCase__=False			,						lowerCamelCase__="np"							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.image_processing_class(**self.image_processor_dict							)
							# prepare image and target
							__lowerCamelCase		       =    self.image_processing_tester.num_labels
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    prepare_image_inputs(self.image_processing_tester			,						equal_resolution=lowerCamelCase__							)
							if with_segmentation_maps:
											__lowerCamelCase		       =    num_labels
											if is_instance_map:
															__lowerCamelCase		       =    list(range(lowerCamelCase__							)							) * 2
															__lowerCamelCase		       =    dict(enumerate(lowerCamelCase__							)							)
											__lowerCamelCase		       =    [
											    np.random.randint(0			,						high * 2			,						(img.size[1], img.size[0])							).astype(np.uinta							) for img in image_inputs
											]
											if segmentation_type == "pil":
															__lowerCamelCase		       =    [Image.fromarray(lowerCamelCase__							) for annotation in annotations]

							__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							pass


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							def common(lowerCamelCase__=False			,						lowerCamelCase__=None							):
											__lowerCamelCase		       =    self.comm_get_image_processor_inputs(
											    with_segmentation_maps=lowerCamelCase__			,						is_instance_map=lowerCamelCase__			,						segmentation_type=lowerCamelCase__							)

											__lowerCamelCase		       =    inputs['mask_labels']
											__lowerCamelCase		       =    inputs['class_labels']
											__lowerCamelCase		       =    inputs['pixel_values']
											__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    np.zeros((20, 50)							)
							__lowerCamelCase		       =    1
							__lowerCamelCase		       =    1
							__lowerCamelCase		       =    1

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


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    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'			,						)
							__lowerCamelCase		       =    self.image_processing_tester.get_fake_oneformer_outputs()

							__lowerCamelCase		       =    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,
							    )			,						)

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

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


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



							'''simple docstring'''
							__lowerCamelCase		       =    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'			,						)
							__lowerCamelCase		       =    self.image_processing_tester.get_fake_oneformer_outputs()
							__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    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'			,						)
							__lowerCamelCase		       =    self.image_processing_tester.get_fake_oneformer_outputs()
							__lowerCamelCase		       =    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)							)






"""simple docstring"""




__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # Text guided image variation with an image mask
        '''prompt''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''mask_image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # image variation with an image mask
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(
    [
        '''example_image''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''example_image''', '''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''audio_length_in_s''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])
__UpperCamelCase						=      frozenset(['''input_tokens'''])

__UpperCamelCase						=      frozenset(['''input_tokens'''])


