



'''simple docstring'''



import unittest

import numpy as np

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 PIL import Image

 from transformers import ViTImageProcessor


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=13						,   lowerCamelCase=3						,   lowerCamelCase=224						,   lowerCamelCase=30						,   lowerCamelCase=400						,   lowerCamelCase=True						,   lowerCamelCase=None						,   lowerCamelCase=True						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   ):
       _snake_case     		=				size if size is not None else {"height": 18, "width": 18}
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				num_channels
       _snake_case     		=				image_size
       _snake_case     		=				min_resolution
       _snake_case     		=				max_resolution
       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean
       _snake_case     		=				image_std






     def 	UpperCamelCase(					self	):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }







@require_torch
@require_vision
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Dict      =   ViTImageProcessor if is_vision_available() else None

     def 	UpperCamelCase(					self	):
       _snake_case     		=				EfficientFormerImageProcessorTester(self	)

     @property
     def 	UpperCamelCase(					self	):
       return self.image_proc_tester.prepare_image_processor_dict()

     def 	UpperCamelCase(					self	):
       _snake_case     		=				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"	)	)

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       # Initialize image_processor
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       _snake_case     		=				prepare_image_inputs(self.image_proc_tester						,   equal_resolution=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   Image.Image	)

       # Test not batched input
       _snake_case     		=				image_processor(image_inputs[0]						,   return_tensors="pt"	).pixel_values
       self.assertEqual(
           encoded_images.shape						,   (
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )						,   )

       # Test batched
       _snake_case     		=				image_processor(lowerCamelCase						,   return_tensors="pt"	).pixel_values
       self.assertEqual(
           encoded_images.shape						,   (
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )						,   )

     def 	UpperCamelCase(					self	):
       # Initialize image_processor
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       _snake_case     		=				prepare_image_inputs(self.image_proc_tester						,   equal_resolution=lowerCamelCase						,   numpify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   np.ndarray	)

       # Test not batched input
       _snake_case     		=				image_processor(image_inputs[0]						,   return_tensors="pt"	).pixel_values
       self.assertEqual(
           encoded_images.shape						,   (
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )						,   )

       # Test batched
       _snake_case     		=				image_processor(lowerCamelCase						,   return_tensors="pt"	).pixel_values
       self.assertEqual(
           encoded_images.shape						,   (
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )						,   )






     def 	UpperCamelCase(					self	):
       # Initialize image_processor
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       _snake_case     		=				prepare_image_inputs(self.image_proc_tester						,   equal_resolution=lowerCamelCase						,   torchify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   torch.Tensor	)

       # Test not batched input
       _snake_case     		=				image_processor(image_inputs[0]						,   return_tensors="pt"	).pixel_values
       self.assertEqual(
           encoded_images.shape						,   (
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )						,   )

       # Test batched
       _snake_case     		=				image_processor(lowerCamelCase						,   return_tensors="pt"	).pixel_values
       self.assertEqual(
           encoded_images.shape						,   (
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )						,   )









'''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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__="shi-labs/oneformer_demo"       ):







  '''simple docstring'''



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


class       __SCREAMING_SNAKE_CASE	(						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						,   ):
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				num_channels
       _snake_case     		=				min_resolution
       _snake_case     		=				max_resolution
       _snake_case     		=				do_resize
       _snake_case     		=				{"shortest_edge": 32, "longest_edge": 1_333} if size is None else size
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean
       _snake_case     		=				image_std
       _snake_case     		=				class_info_file
       _snake_case     		=				prepare_metadata(lowerCamelCase						,   lowerCamelCase	)
       _snake_case     		=				num_text
       _snake_case     		=				repo_path

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

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

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

       return expected_height, expected_width






     def 	UpperCamelCase(					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       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Union[str, Any]      =   OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
     # only for test_image_processing_common.test_image_proc_to_json_string
     UpperCAmelCase__       :						Tuple      =   image_processing_class

     def 	UpperCamelCase(					self	):
       _snake_case     		=				OneFormerImageProcessorTester(self	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				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 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       # Initialize image_processor
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       _snake_case     		=				prepare_image_inputs(self.image_processing_tester						,   equal_resolution=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   Image.Image	)

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

       _snake_case		,		_snake_case     		=				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
       _snake_case		,		_snake_case     		=				self.image_processing_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				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 	UpperCamelCase(					self	):
       # Initialize image_processor
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       _snake_case     		=				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
       _snake_case     		=				image_processor(image_inputs[0]						,   ["semantic"]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				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
       _snake_case		,		_snake_case     		=				self.image_processing_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				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 	UpperCamelCase(					self	):
       # Initialize image_processor
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       _snake_case     		=				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
       _snake_case     		=				image_processor(image_inputs[0]						,   ["semantic"]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				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
       _snake_case		,		_snake_case     		=				self.image_processing_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

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

       _snake_case     		=				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 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       def common(lowerCamelCase=False						,   lowerCamelCase=None	):
         _snake_case     		=				self.comm_get_image_processor_inputs(
             with_segmentation_maps=lowerCamelCase						,   is_instance_map=lowerCamelCase						,   segmentation_type=lowerCamelCase	)

         _snake_case     		=				inputs["mask_labels"]
         _snake_case     		=				inputs["class_labels"]
         _snake_case     		=				inputs["pixel_values"]
         _snake_case     		=				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 	UpperCamelCase(					self	):
       _snake_case     		=				np.zeros((20, 50)	)
       _snake_case     		=				1
       _snake_case     		=				1
       _snake_case     		=				1

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

     def 	UpperCamelCase(					self	):
       _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"						,   )
       _snake_case     		=				self.image_processing_tester.get_fake_oneformer_outputs()

       _snake_case     		=				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,
           )						,   )

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

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

     def 	UpperCamelCase(					self	):
       _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"						,   )
       _snake_case     		=				self.image_processing_tester.get_fake_oneformer_outputs()
       _snake_case     		=				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 	UpperCamelCase(					self	):
       _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"						,   )
       _snake_case     		=				self.image_processing_tester.get_fake_oneformer_outputs()
       _snake_case     		=				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)	)


