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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	Tuple="shi-labs/oneformer_demo"     )							-> Tuple:







  """simple docstring"""





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







class __snake_case    (						unittest.TestCase		):




   def __init__(   self      :    List[Any]	,     _lowercase      :    Optional[Any]	,     _lowercase      :    int=7	,     _lowercase      :    Any=3	,     _lowercase      :    int=30	,     _lowercase      :    List[Any]=4_00	,     _lowercase      :    Union[str, Any]=None	,     _lowercase      :    Dict=True	,     _lowercase      :    Tuple=True	,     _lowercase      :    int=[0.5, 0.5, 0.5]	,     _lowercase      :    List[str]=[0.5, 0.5, 0.5]	,     _lowercase      :    str=10	,     _lowercase      :    Union[str, Any]=False	,     _lowercase      :    int=2_55	,     _lowercase      :    List[str]="shi-labs/oneformer_demo"	,     _lowercase      :    Any="ade20k_panoptic.json"	,     _lowercase      :    Any=10	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				parent
     SCREAMING_SNAKE_CASE__      							=				batch_size
     SCREAMING_SNAKE_CASE__      							=				num_channels
     SCREAMING_SNAKE_CASE__      							=				min_resolution
     SCREAMING_SNAKE_CASE__      							=				max_resolution
     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				{"""shortest_edge""": 32, """longest_edge""": 13_33} if size is None else size
     SCREAMING_SNAKE_CASE__      							=				do_normalize
     SCREAMING_SNAKE_CASE__      							=				image_mean
     SCREAMING_SNAKE_CASE__      							=				image_std
     SCREAMING_SNAKE_CASE__      							=				class_info_file
     SCREAMING_SNAKE_CASE__      							=				prepare_metadata(_lowercase	,     _lowercase				)
     SCREAMING_SNAKE_CASE__      							=				num_text
     SCREAMING_SNAKE_CASE__      							=				repo_path

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




   def      __a						(   self      :    Optional[int]				):



     """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      __a						(   self      :    List[Any]	,     _lowercase      :    Any	,     _lowercase      :    Optional[Any]=False				):



     """simple docstring"""






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

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

     return expected_height, expected_width




   def      __a						(   self      :    Dict				):



     """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    (						lowerCamelCase_       ,		unittest.TestCase		):
   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      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				OneFormerImageProcessorTester(self				)




   @property
   def      __a						(   self      :    str				):



     """simple docstring"""






     return self.image_processing_tester.prepare_image_processor_dict()




   def      __a						(   self      :    str				):



     """simple docstring"""






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




   def      __a						(   self      :    Any				):



     """simple docstring"""






     pass




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processing_tester.get_expected_values(_lowercase				)

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

     # Test batched
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processing_tester.get_expected_values(_lowercase	,     batched=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				image_processor(
         _lowercase	,     ["""semantic"""] * len(_lowercase				)	,     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      :    int				):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processing_tester.get_expected_values(_lowercase				)

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

     # Test batched
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processing_tester.get_expected_values(_lowercase	,     batched=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				image_processor(
         _lowercase	,     ["""semantic"""] * len(_lowercase				)	,     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      :    str				):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processing_tester.get_expected_values(_lowercase				)

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

     # Test batched
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processing_tester.get_expected_values(_lowercase	,     batched=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				image_processor(
         _lowercase	,     ["""semantic"""] * len(_lowercase				)	,     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      :    Tuple	,     _lowercase      :    Optional[int]=False	,     _lowercase      :    Any=False	,     _lowercase      :    List[str]="np"				):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				image_processor(
         _lowercase	,     ["""semantic"""] * len(_lowercase				)	,     _lowercase	,     return_tensors="""pt"""	,     instance_id_to_semantic_id=_lowercase	,     pad_and_return_pixel_mask=_lowercase	,     )

     return inputs




   def      __a						(   self      :    str				):



     """simple docstring"""






     pass




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     def common(_lowercase      :    Optional[int]=False	,     _lowercase      :    Union[str, Any]=None				):
       SCREAMING_SNAKE_CASE__      							=				self.comm_get_image_processor_inputs(
           with_segmentation_maps=_lowercase	,     is_instance_map=_lowercase	,     segmentation_type=_lowercase				)

       SCREAMING_SNAKE_CASE__      							=				inputs["""mask_labels"""]
       SCREAMING_SNAKE_CASE__      							=				inputs["""class_labels"""]
       SCREAMING_SNAKE_CASE__      							=				inputs["""pixel_values"""]
       SCREAMING_SNAKE_CASE__      							=				inputs["""text_inputs"""]

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

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




   def      __a						(   self      :    Any				):



     """simple docstring"""






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

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




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_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"""	,     )
     SCREAMING_SNAKE_CASE__      							=				self.image_processing_tester.get_fake_oneformer_outputs()

     SCREAMING_SNAKE_CASE__      							=				fature_extractor.post_process_semantic_segmentation(_lowercase				)

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

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

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




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_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"""	,     )
     SCREAMING_SNAKE_CASE__      							=				self.image_processing_tester.get_fake_oneformer_outputs()
     SCREAMING_SNAKE_CASE__      							=				image_processor.post_process_instance_segmentation(_lowercase	,     threshold=0				)

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




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_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"""	,     )
     SCREAMING_SNAKE_CASE__      							=				self.image_processing_tester.get_fake_oneformer_outputs()
     SCREAMING_SNAKE_CASE__      							=				image_processor.post_process_panoptic_segmentation(_lowercase	,     threshold=0				)

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

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
   __lowerCamelCase      :			Optional[Any]							       =		{
       '''linear''': PIL.Image.Resampling.BILINEAR,
       '''bilinear''': PIL.Image.Resampling.BILINEAR,
       '''bicubic''': PIL.Image.Resampling.BICUBIC,
       '''lanczos''': PIL.Image.Resampling.LANCZOS,
       '''nearest''': PIL.Image.Resampling.NEAREST,
   }
else:
   __lowerCamelCase      :			int							       =		{
       '''linear''': PIL.Image.LINEAR,
       '''bilinear''': PIL.Image.BILINEAR,
       '''bicubic''': PIL.Image.BICUBIC,
       '''lanczos''': PIL.Image.LANCZOS,
       '''nearest''': PIL.Image.NEAREST,
   }



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				(images / 2 + 0.5).clamp(0			,					1     )
  SCREAMING_SNAKE_CASE__      							=				images.cpu().permute(0			,					2			,					3			,					1     ).float().numpy()
  SCREAMING_SNAKE_CASE__      							=				numpy_to_pil(__UpperCamelCase     )
  return images






def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> int:







  """simple docstring"""





  if images.ndim == 3:
    SCREAMING_SNAKE_CASE__      							=				images[None, ...]
  SCREAMING_SNAKE_CASE__      							=				(images * 2_55).round().astype("""uint8"""     )
  if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    SCREAMING_SNAKE_CASE__      							=				[Image.fromarray(image.squeeze()			,					mode="""L"""     ) for image in images]
  else:
    SCREAMING_SNAKE_CASE__      							=				[Image.fromarray(__UpperCamelCase     ) for image in images]

  return pil_images
