


"""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 _A						(						lowercase       ,    lowercase="shi-labs/oneformer_demo"		):







    """simple docstring"""
    with open(hf_hub_download(lowercase       ,    lowercase       ,    repo_type='''dataset'''		)       ,    '''r'''		) as f:
        a 		=json.load(lowercase		)
    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(lowercase		)		)
    a 		=thing_ids
    a 		=class_names
    return metadata






class       __A				(       unittest.TestCase			):






 """simple docstring"""




 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 ,    )					->     List[Any]:
     a 		=parent
     a 		=batch_size
     a 		=num_channels
     a 		=min_resolution
     a 		=max_resolution
     a 		=do_resize
     a 		={'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
     a 		=do_normalize
     a 		=image_mean
     a 		=image_std
     a 		=class_info_file
     a 		=prepare_metadata(__A ,    __A						)
     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       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 ,    __A ,    __A=False						)					->     List[Any]:
     if not batched:
         a 		=image_inputs[0]
         if isinstance(__A ,    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(__A ,    key=lambda __A						: item[0]						)[0]
         a 		=max(__A ,    key=lambda __A						: 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       __A				(       _SCREAMING_SNAKE_CASE,    unittest.TestCase			):






 """simple docstring"""




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


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     a 		=OneFormerImageProcessorTester(self						)


 @property
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     return self.image_processing_tester.prepare_image_processor_dict()


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     a 		=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       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     pass


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     # 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=__A						)
     for image in image_inputs:
         self.assertIsInstance(__A ,    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(__A						)

     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(__A ,    batched=__A						)

     a 		=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       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     # 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=__A ,    numpify=__A						)
     for image in image_inputs:
         self.assertIsInstance(__A ,    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(__A						)

     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(__A ,    batched=__A						)

     a 		=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       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     # 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=__A ,    torchify=__A						)
     for image in image_inputs:
         self.assertIsInstance(__A ,    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(__A						)

     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(__A ,    batched=__A						)

     a 		=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       SCREAMING_SNAKE_CASE							(		self ,    __A=False ,    __A=False ,    __A="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=__A						)
     if with_segmentation_maps:
         a 		=num_labels
         if is_instance_map:
             a 		=list(range(__A						)						) * 2
             a 		=dict(enumerate(__A						)						)
         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(__A						) for annotation in annotations]

     a 		=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       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     pass


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     def common(__A=False ,    __A=None						):
         a 		=self.comm_get_image_processor_inputs(
             with_segmentation_maps=__A ,    is_instance_map=__A ,    segmentation_type=__A						)

         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(__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       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     a 		=np.zeros((20, 50)						)
     a 		=1
     a 		=1
     a 		=1

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


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     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(__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,
         ) ,    )

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

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


 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     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(__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       SCREAMING_SNAKE_CASE							(		self						)					->     List[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(__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)						)





import numpy as np




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return 1 / (1 + np.exp(-vector   ))




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return vector * sigmoid(1.7_02 * vector   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






