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

class 					_lowercase    (     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       ,						):
       lowerCAmelCase_:							str       =	parent
       lowerCAmelCase_:							Any       =	batch_size
       lowerCAmelCase_:							str       =	num_channels
       lowerCAmelCase_:							Dict       =	min_resolution
       lowerCAmelCase_:							Tuple       =	max_resolution
       lowerCAmelCase_:							Tuple       =	do_resize
       lowerCAmelCase_:							Optional[Any]       =	{"shortest_edge": 32, "longest_edge": 1_333} if size is None else size
       lowerCAmelCase_:							Optional[int]       =	do_normalize
       lowerCAmelCase_:							List[str]       =	image_mean
       lowerCAmelCase_:							Any       =	image_std
       lowerCAmelCase_:							Optional[Any]       =	class_info_file
       lowerCAmelCase_:							List[str]       =	prepare_metadata(lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	num_text
       lowerCAmelCase_:							Tuple       =	repo_path

       # for the post_process_functions
       lowerCAmelCase_:							List[str]       =	2
       lowerCAmelCase_:							Any       =	10
       lowerCAmelCase_:							str       =	10
       lowerCAmelCase_:							Any       =	3
       lowerCAmelCase_:							List[Any]       =	4
       lowerCAmelCase_:							List[Any]       =	num_labels
       lowerCAmelCase_:							int       =	do_reduce_labels
       lowerCAmelCase_:							List[Any]       =	ignore_index




    def      _a    (    self ):
       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       ,						lowerCamelCase__       ,						lowerCamelCase__=False ):
       if not batched:
          lowerCAmelCase_:							List[Any]       =	image_inputs[0]
          if isinstance(lowerCamelCase__       ,						Image.Image ):
             lowerCAmelCase_  ,	lowerCAmelCase_:							Dict       =	image.size
          else:
             lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	image.shape[1], image.shape[2]
          if w < h:
             lowerCAmelCase_:							List[Any]       =	int(self.size["shortest_edge"] * h / w )
             lowerCAmelCase_:							List[Any]       =	self.size["shortest_edge"]
          elif w > h:
             lowerCAmelCase_:							Union[str, Any]       =	self.size["shortest_edge"]
             lowerCAmelCase_:							List[str]       =	int(self.size["shortest_edge"] * w / h )
          else:
             lowerCAmelCase_:							str       =	self.size["shortest_edge"]
             lowerCAmelCase_:							int       =	self.size["shortest_edge"]

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

       return expected_height, expected_width




    def      _a    (    self ):
       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 					_lowercase    (     UpperCAmelCase__ ,  unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Dict          =    OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
    # only for test_image_processing_common.test_image_proc_to_json_string
    SCREAMING_SNAKE_CASE:				Optional[int]          =    image_processing_class




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	OneFormerImageProcessorTester(self )




    @property
    def      _a    (    self ):
       return self.image_processing_tester.prepare_image_processor_dict()




    def      _a    (    self ):
       lowerCAmelCase_:							Tuple       =	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      _a    (    self ):
       pass




    def      _a    (    self ):
       # Initialize image_processor
       lowerCAmelCase_:							Tuple       =	self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       lowerCAmelCase_:							List[str]       =	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_:							Optional[int]       =	image_processor(image_inputs[0]       ,						["semantic"]       ,						return_tensors="pt" ).pixel_values

       lowerCAmelCase_  ,	lowerCAmelCase_:							List[Any]       =	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_:							Tuple       =	self.image_processing_tester.get_expected_values(lowerCamelCase__       ,						batched=lowerCamelCase__ )

       lowerCAmelCase_:							int       =	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      _a    (    self ):
       # Initialize image_processor
       lowerCAmelCase_:							Tuple       =	self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       lowerCAmelCase_:							List[str]       =	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_:							List[Any]       =	image_processor(image_inputs[0]       ,						["semantic"]       ,						return_tensors="pt" ).pixel_values

       lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	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_:							Tuple       =	self.image_processing_tester.get_expected_values(lowerCamelCase__       ,						batched=lowerCamelCase__ )

       lowerCAmelCase_:							Any       =	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      _a    (    self ):
       # Initialize image_processor
       lowerCAmelCase_:							Dict       =	self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       lowerCAmelCase_:							Optional[int]       =	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_:							int       =	image_processor(image_inputs[0]       ,						["semantic"]       ,						return_tensors="pt" ).pixel_values

       lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	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_:							List[str]       =	self.image_processing_tester.get_expected_values(lowerCamelCase__       ,						batched=lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	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      _a    (    self       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False       ,						lowerCamelCase__="np" ):
       lowerCAmelCase_:							Optional[int]       =	self.image_processing_class(**self.image_processor_dict )
       # prepare image and target
       lowerCAmelCase_:							List[Any]       =	self.image_processing_tester.num_labels
       lowerCAmelCase_:							int       =	None
       lowerCAmelCase_:							Dict       =	None
       lowerCAmelCase_:							List[str]       =	prepare_image_inputs(self.image_processing_tester       ,						equal_resolution=lowerCamelCase__ )
       if with_segmentation_maps:
          lowerCAmelCase_:							str       =	num_labels
          if is_instance_map:
             lowerCAmelCase_:							int       =	list(range(lowerCamelCase__ ) ) * 2
             lowerCAmelCase_:							Dict       =	dict(enumerate(lowerCamelCase__ ) )
          lowerCAmelCase_:							Tuple       =	[
              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_:							Any       =	[Image.fromarray(lowerCamelCase__ ) for annotation in annotations]

       lowerCAmelCase_:							List[str]       =	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      _a    (    self ):
       pass




    def      _a    (    self ):
       def common(lowerCamelCase__=False       ,						lowerCamelCase__=None ):
          lowerCAmelCase_:							Dict       =	self.comm_get_image_processor_inputs(
              with_segmentation_maps=lowerCamelCase__       ,						is_instance_map=lowerCamelCase__       ,						segmentation_type=lowerCamelCase__ )

          lowerCAmelCase_:							Any       =	inputs["mask_labels"]
          lowerCAmelCase_:							Tuple       =	inputs["class_labels"]
          lowerCAmelCase_:							Any       =	inputs["pixel_values"]
          lowerCAmelCase_:							int       =	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      _a    (    self ):
       lowerCAmelCase_:							Optional[Any]       =	np.zeros((20, 50) )
       lowerCAmelCase_:							Optional[Any]       =	1
       lowerCAmelCase_:							int       =	1
       lowerCAmelCase_:							Union[str, Any]       =	1

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




    def      _a    (    self ):
       lowerCAmelCase_:							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"       ,						)
       lowerCAmelCase_:							int       =	self.image_processing_tester.get_fake_oneformer_outputs()

       lowerCAmelCase_:							List[str]       =	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_:							Any       =	[(1, 4) for i in range(self.image_processing_tester.batch_size )]
       lowerCAmelCase_:							Dict       =	fature_extractor.post_process_semantic_segmentation(lowerCamelCase__       ,						target_sizes=lowerCamelCase__ )

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




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[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"       ,						)
       lowerCAmelCase_:							Any       =	self.image_processing_tester.get_fake_oneformer_outputs()
       lowerCAmelCase_:							int       =	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      _a    (    self ):
       lowerCAmelCase_:							str       =	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_:							Any       =	self.image_processing_tester.get_fake_oneformer_outputs()
       lowerCAmelCase_:							List[Any]       =	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) )
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							Union[str, Any]       =	[1]

   lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	0, 0, 0
   lowerCAmelCase_:							Union[str, Any]       =	ugly_nums[ia] * 2
   lowerCAmelCase_:							str       =	ugly_nums[ia] * 3
   lowerCAmelCase_:							Dict       =	ugly_nums[ia] * 5

   for _ in range(1							,				lowercase					):
      lowerCAmelCase_:							Any       =	min(lowercase							,				lowercase							,				lowercase					)
      ugly_nums.append(lowercase					)
      if next_num == next_a:
         ia += 1
         lowerCAmelCase_:							str       =	ugly_nums[ia] * 2
      if next_num == next_a:
         ia += 1
         lowerCAmelCase_:							Optional[int]       =	ugly_nums[ia] * 3
      if next_num == next_a:
         ia += 1
         lowerCAmelCase_:							int       =	ugly_nums[ia] * 5
   return ugly_nums[-1]


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

 testmod(verbose=True)
 print(f'''{ugly_numbers(2_0_0) = }''')