

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

class 			a					(		unittest.TestCase			):


						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  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      parent
										_UpperCAmelCase    :						Union[str, Any]							=      batch_size
										_UpperCAmelCase    :						List[str]							=      num_channels
										_UpperCAmelCase    :						Dict							=      min_resolution
										_UpperCAmelCase    :						str							=      max_resolution
										_UpperCAmelCase    :						Tuple							=      do_resize
										_UpperCAmelCase    :						Union[str, Any]							=      {"shortest_edge": 32, "longest_edge": 1333} if size is None else size
										_UpperCAmelCase    :						List[str]							=      do_normalize
										_UpperCAmelCase    :						Optional[int]							=      image_mean
										_UpperCAmelCase    :						Optional[int]							=      image_std
										_UpperCAmelCase    :						Dict							=      class_info_file
										_UpperCAmelCase    :						Tuple							=      prepare_metadata(A_  ,   A_		)
										_UpperCAmelCase    :						Optional[int]							=      num_text
										_UpperCAmelCase    :						Tuple							=      repo_path

										# for the post_process_functions
										_UpperCAmelCase    :						int							=      2
										_UpperCAmelCase    :						Tuple							=      10
										_UpperCAmelCase    :						List[str]							=      10
										_UpperCAmelCase    :						List[Any]							=      3
										_UpperCAmelCase    :						List[str]							=      4
										_UpperCAmelCase    :						Optional[Any]							=      num_labels
										_UpperCAmelCase    :						List[str]							=      do_reduce_labels
										_UpperCAmelCase    :						Union[str, Any]							=      ignore_index


						def     _UpperCAmelCase			(    self		):







										'''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     _UpperCAmelCase			(    self  ,   A_  ,   A_=False		):







										'''simple docstring'''


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

										else:
														_UpperCAmelCase    :						Any							=      []
														for image in image_inputs:
																		_UpperCAmelCase    :						int							=      self.get_expected_values([image]		)
																		expected_values.append((expected_height, expected_width)		)
														_UpperCAmelCase    :						List[str]							=      max(A_  ,   key=lambda A_		: item[0]		)[0]
														_UpperCAmelCase    :						Union[str, Any]							=      max(A_  ,   key=lambda A_		: item[1]		)[1]

										return expected_height, expected_width





						def     _UpperCAmelCase			(    self		):







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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      OneFormerImageProcessorTester(self		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.image_processing_tester.prepare_image_processor_dict()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.image_processing_class(**self.image_processor_dict		)
										# create random PIL images
										_UpperCAmelCase    :						Optional[int]							=      prepare_image_inputs(self.image_processing_tester  ,   equal_resolution=A_		)
										for image in image_inputs:
														self.assertIsInstance(A_  ,   Image.Image		)

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

										_UpperCAmelCase    :						Any							=      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
										_UpperCAmelCase    :						Tuple							=      self.image_processing_tester.get_expected_values(A_  ,   batched=A_		)

										_UpperCAmelCase    :						Optional[int]							=      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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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

										_UpperCAmelCase    :						List[Any]							=      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
										_UpperCAmelCase    :						Optional[int]							=      self.image_processing_tester.get_expected_values(A_  ,   batched=A_		)

										_UpperCAmelCase    :						Optional[int]							=      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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.image_processing_class(**self.image_processor_dict		)
										# create random PyTorch tensors
										_UpperCAmelCase    :						Union[str, Any]							=      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
										_UpperCAmelCase    :						List[str]							=      image_processor(image_inputs[0]  ,   ["semantic"]  ,   return_tensors="pt"		).pixel_values

										_UpperCAmelCase    :						str							=      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
										_UpperCAmelCase    :						Any							=      self.image_processing_tester.get_expected_values(A_  ,   batched=A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      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     _UpperCAmelCase			(    self  ,   A_=False  ,   A_=False  ,   A_="np"		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.image_processing_class(**self.image_processor_dict		)
										# prepare image and target
										_UpperCAmelCase    :						List[str]							=      self.image_processing_tester.num_labels
										_UpperCAmelCase    :						Union[str, Any]							=      None
										_UpperCAmelCase    :						Optional[int]							=      None
										_UpperCAmelCase    :						int							=      prepare_image_inputs(self.image_processing_tester  ,   equal_resolution=A_		)
										if with_segmentation_maps:
														_UpperCAmelCase    :						Optional[int]							=      num_labels
														if is_instance_map:
																		_UpperCAmelCase    :						Optional[int]							=      list(range(A_		)		) * 2
																		_UpperCAmelCase    :						Tuple							=      dict(enumerate(A_		)		)
														_UpperCAmelCase    :						List[Any]							=      [
														    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    :						List[Any]							=      [Image.fromarray(A_		) for annotation in annotations]

										_UpperCAmelCase    :						Union[str, Any]							=      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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										def common(A_=False  ,   A_=None		):
														_UpperCAmelCase    :						Dict							=      self.comm_get_image_processor_inputs(
														    with_segmentation_maps=A_  ,   is_instance_map=A_  ,   segmentation_type=A_		)

														_UpperCAmelCase    :						List[Any]							=      inputs["mask_labels"]
														_UpperCAmelCase    :						List[str]							=      inputs["class_labels"]
														_UpperCAmelCase    :						List[Any]							=      inputs["pixel_values"]
														_UpperCAmelCase    :						Tuple							=      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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      np.zeros((20, 50)		)
										_UpperCAmelCase    :						int							=      1
										_UpperCAmelCase    :						str							=      1
										_UpperCAmelCase    :						Union[str, Any]							=      1

										_UpperCAmelCase    :						List[str]							=      binary_mask_to_rle(A_		)
										self.assertEqual(len(A_		)  ,   4		)
										self.assertEqual(rle[0]  ,   21		)
										self.assertEqual(rle[1]  ,   45		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      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    :						Optional[int]							=      self.image_processing_tester.get_fake_oneformer_outputs()

										_UpperCAmelCase    :						str							=      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,
										    )  ,   )

										_UpperCAmelCase    :						List[str]							=      [(1, 4) for i in range(self.image_processing_tester.batch_size		)]
										_UpperCAmelCase    :						Union[str, Any]							=      fature_extractor.post_process_semantic_segmentation(A_  ,   target_sizes=A_		)

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						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"  ,   )
										_UpperCAmelCase    :						Tuple							=      self.image_processing_tester.get_fake_oneformer_outputs()
										_UpperCAmelCase    :						Union[str, Any]							=      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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      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    :						Any							=      self.image_processing_tester.get_fake_oneformer_outputs()
										_UpperCAmelCase    :						Optional[Any]							=      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)		)





from typing import Any

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      data
										_UpperCAmelCase    :						Any							=      None







class 			a					:


						def __init__(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      None


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.head
										while temp is not None:
														print(temp.data  ,   end=" "		)
														_UpperCAmelCase    :						str							=      temp.next
										print()


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      Node(A_		)
										_UpperCAmelCase    :						Tuple							=      self.head
										_UpperCAmelCase    :						Tuple							=      new_node





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if node_data_a == node_data_a:
														return
										else:
														_UpperCAmelCase    :						int							=      self.head
														while node_a is not None and node_a.data != node_data_a:
																		_UpperCAmelCase    :						Tuple							=      node_a.next

														_UpperCAmelCase    :						Dict							=      self.head
														while node_a is not None and node_a.data != node_data_a:
																		_UpperCAmelCase    :						List[Any]							=      node_a.next

														if node_a is None or node_a is None:
																		return

														_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      node_a.data, node_a.data


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = LinkedList()
					for i in range(5, 0, -1):
										ll.push(i)

					ll.print_list()

					ll.swap_nodes(1, 4)
					print('After swapping')
					ll.print_list()


