



'''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  UpperCAmelCase_       (		lowercase__       ,	lowercase__="shi-labs/oneformer_demo"       ):


					'''simple docstring'''







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







class 	UpperCAmelCase	(		unittest.TestCase):


	'''simple docstring'''




	def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_=7							,  lowerCAmelCase_=3							,  lowerCAmelCase_=3_0							,  lowerCAmelCase_=4_0_0							,  lowerCAmelCase_=None							,  lowerCAmelCase_=True							,  lowerCAmelCase_=True							,  lowerCAmelCase_=[0.5, 0.5, 0.5]							,  lowerCAmelCase_=[0.5, 0.5, 0.5]							,  lowerCAmelCase_=1_0							,  lowerCAmelCase_=False							,  lowerCAmelCase_=2_5_5							,  lowerCAmelCase_="shi-labs/oneformer_demo"							,  lowerCAmelCase_="ade20k_panoptic.json"							,  lowerCAmelCase_=1_0							,  ) ->      Union[str, Any]:



						"""simple docstring"""







						a_       		=parent
						a_       		=batch_size
						a_       		=num_channels
						a_       		=min_resolution
						a_       		=max_resolution
						a_       		=do_resize
						a_       		={"""shortest_edge""": 3_2, """longest_edge""": 1_3_3_3} if size is None else size
						a_       		=do_normalize
						a_       		=image_mean
						a_       		=image_std
						a_       		=class_info_file
						a_       		=prepare_metadata(lowerCamelCase_							,  lowerCamelCase_)
						a_       		=num_text
						a_       		=repo_path

						# for the post_process_functions
						a_       		=2
						a_       		=1_0
						a_       		=1_0
						a_       		=3
						a_       		=4
						a_       		=num_labels
						a_       		=do_reduce_labels
						a_       		=ignore_index




	def       lowercase_  (   self) ->      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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=False) ->      Any:



						"""simple docstring"""







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

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

						return expected_height, expected_width




	def       lowercase_  (   self) ->      List[Any]:



						"""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 	UpperCAmelCase	(		lowercase_					,	unittest.TestCase):


	'''simple docstring'''




	__magic_name__					: Tuple  =      OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	__magic_name__					: Dict  =      image_processing_class




	def       lowercase_  (   self) ->      str:



						"""simple docstring"""







						a_       		=OneFormerImageProcessorTester(self)




	@property
	def       lowercase_  (   self) ->      int:



						"""simple docstring"""







						return self.image_processing_tester.prepare_image_processor_dict()




	def       lowercase_  (   self) ->      Optional[Any]:



						"""simple docstring"""







						a_       		=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       lowercase_  (   self) ->      Dict:



						"""simple docstring"""







						pass




	def       lowercase_  (   self) ->      str:



						"""simple docstring"""







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

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

						a_       		=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
						a_       		=self.image_processing_tester.get_expected_values(lowerCamelCase_							,  batched=lowerCamelCase_)

						a_       		=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       lowercase_  (   self) ->      int:



						"""simple docstring"""







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

						a_       		=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
						a_       		=self.image_processing_tester.get_expected_values(lowerCamelCase_							,  batched=lowerCamelCase_)

						a_       		=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       lowercase_  (   self) ->      int:



						"""simple docstring"""







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

						a_       		=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
						a_       		=self.image_processing_tester.get_expected_values(lowerCamelCase_							,  batched=lowerCamelCase_)

						a_       		=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       lowercase_  (   self							,  lowerCAmelCase_=False							,  lowerCAmelCase_=False							,  lowerCAmelCase_="np") ->      Union[str, Any]:



						"""simple docstring"""







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

						a_       		=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       lowercase_  (   self) ->      str:



						"""simple docstring"""







						pass




	def       lowercase_  (   self) ->      Union[str, Any]:



						"""simple docstring"""







						def common(lowerCAmelCase_=False							,  lowerCAmelCase_=None):
											a_       		=self.comm_get_image_processor_inputs(
											    with_segmentation_maps=lowerCamelCase_							,  is_instance_map=lowerCamelCase_							,  segmentation_type=lowerCamelCase_)

											a_       		=inputs["""mask_labels"""]
											a_       		=inputs["""class_labels"""]
											a_       		=inputs["""pixel_values"""]
											a_       		=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       lowercase_  (   self) ->      Tuple:



						"""simple docstring"""







						a_       		=np.zeros((2_0, 5_0))
						a_       		=1
						a_       		=1
						a_       		=1

						a_       		=binary_mask_to_rle(lowerCamelCase_)
						self.assertEqual(len(lowerCamelCase_)							,  4)
						self.assertEqual(rle[0]							,  2_1)
						self.assertEqual(rle[1]							,  4_5)




	def       lowercase_  (   self) ->      Any:



						"""simple docstring"""







						a_       		=self.image_processing_class(
						    num_labels=self.image_processing_tester.num_classes							,  max_seq_length=7_7							,  task_seq_length=7_7							,  class_info_file="ade20k_panoptic.json"							,  num_text=self.image_processing_tester.num_text							,  repo_path="shi-labs/oneformer_demo"							,  )
						a_       		=self.image_processing_tester.get_fake_oneformer_outputs()

						a_       		=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,
						    )							,  )

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

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




	def       lowercase_  (   self) ->      List[str]:



						"""simple docstring"""







						a_       		=self.image_processing_class(
						    num_labels=self.image_processing_tester.num_classes							,  max_seq_length=7_7							,  task_seq_length=7_7							,  class_info_file="ade20k_panoptic.json"							,  num_text=self.image_processing_tester.num_text							,  repo_path="shi-labs/oneformer_demo"							,  )
						a_       		=self.image_processing_tester.get_fake_oneformer_outputs()
						a_       		=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       lowercase_  (   self) ->      Dict:



						"""simple docstring"""







						a_       		=self.image_processing_class(
						    num_labels=self.image_processing_tester.num_classes							,  max_seq_length=7_7							,  task_seq_length=7_7							,  class_info_file="ade20k_panoptic.json"							,  num_text=self.image_processing_tester.num_text							,  repo_path="shi-labs/oneformer_demo"							,  )
						a_       		=self.image_processing_tester.get_fake_oneformer_outputs()
						a_       		=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))







'''simple docstring'''


from collections.abc import Sequence






def  UpperCAmelCase_       (		lowercase__ = None       ):


     '''simple docstring'''







     if nums is None or not nums:
          raise ValueError("Input sequence should not be empty"       )

     a_       		=nums[0]
     for i in range(1       ,	len(lowercase__       )       ):
          a_       		=nums[i]
          a_       		=max(lowercase__       ,	ans + num       ,	lowercase__       )

     return ans


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # Try on a sample input from the user
  lowercase							       =			int(input('''Enter number of elements : ''').strip())
  lowercase							       =			list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n]
  print(max_subsequence_sum(array))


