






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






class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							def __init__(			self      :						Any			,    snake_case      :						Any			,    snake_case      :						List[str]=7			,    snake_case      :						Optional[int]=3			,    snake_case      :						Optional[int]=30			,    snake_case      :						List[Any]=400			,    snake_case      :						List[Any]=None			,    snake_case      :						Union[str, Any]=True			,    snake_case      :						Union[str, Any]=True			,    snake_case      :						int=[0.5, 0.5, 0.5]			,    snake_case      :						Optional[Any]=[0.5, 0.5, 0.5]			,    snake_case      :						List[str]=10			,    snake_case      :						Optional[Any]=False			,    snake_case      :						List[str]=255			,    snake_case      :						Any="shi-labs/oneformer_demo"			,    snake_case      :						List[Any]="ade20k_panoptic.json"			,    snake_case      :						Optional[Any]=10			,    ):
													'''simple docstring'''





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

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


							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''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      :						Optional[Any]			,    snake_case      :						Union[str, Any]			,    snake_case      :						str=False							):
													'''simple docstring'''





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

													else:
																			A__						:  List[Any]		  =  []
																			for image in image_inputs:
																									A__  ,  A__						:  Optional[int]		  =  self.get_expected_values([image]							)
																									expected_values.append((expected_height, expected_width)							)
																			A__						:  Tuple		  =  max(snake_case			,    key=lambda snake_case							: item[0]							)[0]
																			A__						:  Tuple		  =  max(snake_case			,    key=lambda snake_case							: item[1]							)[1]

													return expected_height, expected_width


							def 						_UpperCamelCase       (			self      :						str							):
													'''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 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		unittest.TestCase		):
							snake_case_     =							OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
							# only for test_image_processing_common.test_image_proc_to_json_string
							snake_case_     =							image_processing_class


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  OneFormerImageProcessorTester(self							)


							@property
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													return self.image_processing_tester.prepare_image_processor_dict()


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  self.image_processing_class(**self.image_processor_dict							)
													self.assertTrue(hasattr(snake_case			,    """image_mean"""							)							)
													self.assertTrue(hasattr(snake_case			,    """image_std"""							)							)
													self.assertTrue(hasattr(snake_case			,    """do_normalize"""							)							)
													self.assertTrue(hasattr(snake_case			,    """do_resize"""							)							)
													self.assertTrue(hasattr(snake_case			,    """size"""							)							)
													self.assertTrue(hasattr(snake_case			,    """ignore_index"""							)							)
													self.assertTrue(hasattr(snake_case			,    """class_info_file"""							)							)
													self.assertTrue(hasattr(snake_case			,    """num_text"""							)							)
													self.assertTrue(hasattr(snake_case			,    """repo_path"""							)							)
													self.assertTrue(hasattr(snake_case			,    """metadata"""							)							)
													self.assertTrue(hasattr(snake_case			,    """do_reduce_labels"""							)							)


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													pass


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  self.image_processing_class(**self.image_processor_dict							)
													# create random PIL images
													A__						:  Dict		  =  prepare_image_inputs(self.image_processing_tester			,    equal_resolution=snake_case							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case			,    Image.Image							)

													# Test not batched input
													A__						:  Union[str, Any]		  =  image_processor(image_inputs[0]			,    ["""semantic"""]			,    return_tensors="""pt"""							).pixel_values

													A__  ,  A__						:  Union[str, Any]		  =  self.image_processing_tester.get_expected_values(snake_case							)

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

													# Test batched
													A__  ,  A__						:  List[Any]		  =  self.image_processing_tester.get_expected_values(snake_case			,    batched=snake_case							)

													A__						:  Optional[int]		  =  image_processor(
													    snake_case			,    ["""semantic"""] * len(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 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





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

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

													A__  ,  A__						:  Optional[int]		  =  self.image_processing_tester.get_expected_values(snake_case							)

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

													# Test batched
													A__  ,  A__						:  Union[str, Any]		  =  self.image_processing_tester.get_expected_values(snake_case			,    batched=snake_case							)

													A__						:  Tuple		  =  image_processor(
													    snake_case			,    ["""semantic"""] * len(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 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





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

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

													A__  ,  A__						:  Dict		  =  self.image_processing_tester.get_expected_values(snake_case							)

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

													# Test batched
													A__  ,  A__						:  Union[str, Any]		  =  self.image_processing_tester.get_expected_values(snake_case			,    batched=snake_case							)

													A__						:  Tuple		  =  image_processor(
													    snake_case			,    ["""semantic"""] * len(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 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Optional[Any]=False			,    snake_case      :						List[str]=False			,    snake_case      :						int="np"							):
													'''simple docstring'''





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

													A__						:  str		  =  image_processor(
													    snake_case			,    ["""semantic"""] * len(snake_case							)			,    snake_case			,    return_tensors="""pt"""			,    instance_id_to_semantic_id=snake_case			,    pad_and_return_pixel_mask=snake_case			,    )

													return inputs


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													pass


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													def common(snake_case      :						Any=False			,    snake_case      :						Dict=None							):
																			A__						:  int		  =  self.comm_get_image_processor_inputs(
																			    with_segmentation_maps=snake_case			,    is_instance_map=snake_case			,    segmentation_type=snake_case							)

																			A__						:  Any		  =  inputs["""mask_labels"""]
																			A__						:  Any		  =  inputs["""class_labels"""]
																			A__						:  Dict		  =  inputs["""pixel_values"""]
																			A__						:  Dict		  =  inputs["""text_inputs"""]

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

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


							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													A__						:  List[str]		  =  np.zeros((20, 50)							)
													A__						:  Optional[Any]		  =  1
													A__						:  List[str]		  =  1
													A__						:  List[Any]		  =  1

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


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													A__						:  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"""			,    )
													A__						:  int		  =  self.image_processing_tester.get_fake_oneformer_outputs()

													A__						:  Tuple		  =  fature_extractor.post_process_semantic_segmentation(snake_case							)

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

													A__						:  Union[str, Any]		  =  [(1, 4) for i in range(self.image_processing_tester.batch_size							)]
													A__						:  Optional[Any]		  =  fature_extractor.post_process_semantic_segmentation(snake_case			,    target_sizes=snake_case							)

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


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													A__						:  str		  =  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"""			,    )
													A__						:  Union[str, Any]		  =  self.image_processing_tester.get_fake_oneformer_outputs()
													A__						:  Dict		  =  image_processor.post_process_instance_segmentation(snake_case			,    threshold=0							)

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


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  Tuple		  =  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"""			,    )
													A__						:  List[Any]		  =  self.image_processing_tester.get_fake_oneformer_outputs()
													A__						:  List[str]		  =  image_processor.post_process_panoptic_segmentation(snake_case			,    threshold=0							)

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














"""simple docstring"""



from __future__ import annotations

A_    =							10




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[int]      )		->list[int]:
						A__						:  Any		  =  1
						A__						:  Optional[int]		  =  max(UpperCAmelCase__      )
						while placement <= max_digit:
												# declare and initialize empty buckets
												A__						:  list[list]		  =  [[] for _ in range(UpperCAmelCase__      )]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		A__						:  List[Any]		  =  int((i / placement) % RADIX      )
																		buckets[tmp].append(UpperCAmelCase__      )
												# put each buckets' contents into list_of_ints
												A__						:  Union[str, Any]		  =  0
												for b in range(UpperCAmelCase__      ):
																		for i in buckets[b]:
																								A__						:  str		  =  i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()






