




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

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


class 			_a  (	unittest.TestCase ):
       def __init__(	self						:	Tuple   ,				lowercase						:	Tuple   ,				lowercase						:	int=7   ,				lowercase						:	List[Any]=3   ,				lowercase						:	Dict=30   ,				lowercase						:	Optional[Any]=400   ,				lowercase						:	Any=None   ,				lowercase						:	str=True   ,				lowercase						:	List[Any]=True   ,				lowercase						:	List[str]=[0.5, 0.5, 0.5]   ,				lowercase						:	Tuple=[0.5, 0.5, 0.5]   ,				lowercase						:	List[Any]=10   ,				lowercase						:	Dict=False   ,				lowercase						:	int=255   ,				lowercase						:	List[str]="shi-labs/oneformer_demo"   ,				lowercase						:	Tuple="ade20k_panoptic.json"   ,				lowercase						:	Optional[int]=10   ,				):



           '''simple docstring'''

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

           # for the post_process_functions
           UpperCAmelCase			       =		2
           UpperCAmelCase			       =		10
           UpperCAmelCase			       =		10
           UpperCAmelCase			       =		3
           UpperCAmelCase			       =		4
           UpperCAmelCase			       =		num_labels
           UpperCAmelCase			       =		do_reduce_labels
           UpperCAmelCase			       =		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						:	Optional[Any]   ,				lowercase						:	List[Any]=False				):



           '''simple docstring'''

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

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

           return expected_height, expected_width







       def     A      (	self						:	int				):



           '''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 			_a  (	UpperCAmelCase__							,							unittest.TestCase ):
       __a				:     List[Any]   				=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
       # only for test_image_processing_common.test_image_proc_to_json_string
       __a				:     Dict   				=					image_processing_class
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		OneFormerImageProcessorTester(self				)
       @property
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           return self.image_processing_tester.prepare_image_processor_dict()
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,				'''image_mean'''				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,				'''image_std'''				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,				'''do_normalize'''				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,				'''do_resize'''				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,				'''size'''				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,				'''ignore_index'''				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,				'''class_info_file'''				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,				'''num_text'''				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,				'''repo_path'''				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,				'''metadata'''				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,				'''do_reduce_labels'''				)				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Any				):



           '''simple docstring'''

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

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

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

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

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

           UpperCAmelCase			       =		image_processor(
               SCREAMING_SNAKE_CASE__   ,				['''semantic'''] * len(SCREAMING_SNAKE_CASE__				)   ,				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						:	Optional[int]				):



           '''simple docstring'''

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

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

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

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

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

           UpperCAmelCase			       =		image_processor(
               SCREAMING_SNAKE_CASE__   ,				['''semantic'''] * len(SCREAMING_SNAKE_CASE__				)   ,				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						:	List[str]				):



           '''simple docstring'''

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

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

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

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

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

           UpperCAmelCase			       =		image_processor(
               SCREAMING_SNAKE_CASE__   ,				['''semantic'''] * len(SCREAMING_SNAKE_CASE__				)   ,				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   ,				lowercase						:	List[str]=False   ,				lowercase						:	List[str]=False   ,				lowercase						:	Dict="np"				):



           '''simple docstring'''

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

           UpperCAmelCase			       =		image_processor(
               SCREAMING_SNAKE_CASE__   ,				['''semantic'''] * len(SCREAMING_SNAKE_CASE__				)   ,				SCREAMING_SNAKE_CASE__   ,				return_tensors='''pt'''   ,				instance_id_to_semantic_id=SCREAMING_SNAKE_CASE__   ,				pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE__   ,				)

           return inputs
       def     A      (	self						:	str				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           def common(lowercase						:	Optional[int]=False   ,				lowercase						:	List[str]=None				):
               UpperCAmelCase			       =		self.comm_get_image_processor_inputs(
                   with_segmentation_maps=SCREAMING_SNAKE_CASE__   ,				is_instance_map=SCREAMING_SNAKE_CASE__   ,				segmentation_type=SCREAMING_SNAKE_CASE__				)

               UpperCAmelCase			       =		inputs['''mask_labels''']
               UpperCAmelCase			       =		inputs['''class_labels''']
               UpperCAmelCase			       =		inputs['''pixel_values''']
               UpperCAmelCase			       =		inputs['''text_inputs''']

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

           common()
           common(is_instance_map=SCREAMING_SNAKE_CASE__				)
           common(is_instance_map=SCREAMING_SNAKE_CASE__   ,				segmentation_type='''pil'''				)
           common(is_instance_map=SCREAMING_SNAKE_CASE__   ,				segmentation_type='''pil'''				)
       def     A      (	self						:	str				):



           '''simple docstring'''

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

           UpperCAmelCase			       =		binary_mask_to_rle(SCREAMING_SNAKE_CASE__				)
           self.assertEqual(len(SCREAMING_SNAKE_CASE__				)   ,				4				)
           self.assertEqual(rle[0]   ,				21				)
           self.assertEqual(rle[1]   ,				45				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		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'''   ,				)
           UpperCAmelCase			       =		self.image_processing_tester.get_fake_oneformer_outputs()

           UpperCAmelCase			       =		fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE__				)

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

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

           self.assertEqual(segmentation[0].shape   ,				target_sizes[0]				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		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'''   ,				)
           UpperCAmelCase			       =		self.image_processing_tester.get_fake_oneformer_outputs()
           UpperCAmelCase			       =		image_processor.post_process_instance_segmentation(SCREAMING_SNAKE_CASE__   ,				threshold=0				)

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







       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		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'''   ,				)
           UpperCAmelCase			       =		self.image_processing_tester.get_fake_oneformer_outputs()
           UpperCAmelCase			       =		image_processor.post_process_panoptic_segmentation(SCREAMING_SNAKE_CASE__   ,				threshold=0				)

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





'''simple docstring'''

import re

from filelock import FileLock


try:
 import nltk

 _A			:      int					      =				True
except (ImportError, ModuleNotFoundError):
 _A			:      Optional[Any]					      =				False

if NLTK_AVAILABLE:
 with FileLock('''.lock''') as lock:
  nltk.download('''punkt''', quiet=True)







def      UpperCamelCase_						(					snake_case_       :       str      )					->     str:






 '''simple docstring'''

 re.sub("""<n>"""						,		""""""						,		snake_case_      )  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case_      )      )






