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						(__lowercase ,      __lowercase="shi-labs/oneformer_demo"						)	->	List[str]:

						'''simple docstring'''



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



class        lowercase_   (					unittest.TestCase		):



							def __init__(						self				,    lowercase_				,    lowercase_=7				,    lowercase_=3				,    lowercase_=30				,    lowercase_=400				,    lowercase_=None				,    lowercase_=True				,    lowercase_=True				,    lowercase_=[0.5, 0.5, 0.5]				,    lowercase_=[0.5, 0.5, 0.5]				,    lowercase_=10				,    lowercase_=False				,    lowercase_=255				,    lowercase_="shi-labs/oneformer_demo"				,    lowercase_="ade20k_panoptic.json"				,    lowercase_=10				,    ):
													_snake_case						:					Dict    =   parent
													_snake_case						:					Any    =   batch_size
													_snake_case						:					List[Any]    =   num_channels
													_snake_case						:					Optional[int]    =   min_resolution
													_snake_case						:					List[str]    =   max_resolution
													_snake_case						:					Optional[Any]    =   do_resize
													_snake_case						:					str    =   {"shortest_edge": 32, "longest_edge": 1_333} if size is None else size
													_snake_case						:					Tuple    =   do_normalize
													_snake_case						:					List[Any]    =   image_mean
													_snake_case						:					Any    =   image_std
													_snake_case						:					Tuple    =   class_info_file
													_snake_case						:					Optional[int]    =   prepare_metadata(lowercase_				,    lowercase_    )
													_snake_case						:					Optional[int]    =   num_text
													_snake_case						:					int    =   repo_path

													# for the post_process_functions
													_snake_case						:					int    =   2
													_snake_case						:					Any    =   10
													_snake_case						:					Any    =   10
													_snake_case						:					Union[str, Any]    =   3
													_snake_case						:					List[str]    =   4
													_snake_case						:					Union[str, Any]    =   num_labels
													_snake_case						:					Any    =   do_reduce_labels
													_snake_case						:					Union[str, Any]    =   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				,    lowercase_				,    lowercase_=False    ):
													if not batched:
																			_snake_case						:					Dict    =   image_inputs[0]
																			if isinstance(lowercase_				,    Image.Image    ):
																									_snake_case						:					str    =   image.size
																			else:
																									_snake_case						:					Optional[int]    =   image.shape[1], image.shape[2]
																			if w < h:
																									_snake_case						:					Optional[Any]    =   int(self.size["shortest_edge"] * h / w    )
																									_snake_case						:					str    =   self.size["shortest_edge"]
																			elif w > h:
																									_snake_case						:					List[str]    =   self.size["shortest_edge"]
																									_snake_case						:					List[Any]    =   int(self.size["shortest_edge"] * w / h    )
																			else:
																									_snake_case						:					Tuple    =   self.size["shortest_edge"]
																									_snake_case						:					List[Any]    =   self.size["shortest_edge"]

													else:
																			_snake_case						:					Union[str, Any]    =   []
																			for image in image_inputs:
																									_snake_case						:					List[Any]    =   self.get_expected_values([image]    )
																									expected_values.append((expected_height, expected_width)    )
																			_snake_case						:					Dict    =   max(lowercase_				,    key=lambda lowercase_    : item[0]    )[0]
																			_snake_case						:					List[Any]    =   max(lowercase_				,    key=lambda lowercase_    : 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        lowercase_   (					__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
							# only for test_image_processing_common.test_image_proc_to_json_string
							_lowerCamelCase						=		image_processing_class



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



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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(lowercase_				,    "image_mean"    )    )
													self.assertTrue(hasattr(lowercase_				,    "image_std"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_normalize"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_resize"    )    )
													self.assertTrue(hasattr(lowercase_				,    "size"    )    )
													self.assertTrue(hasattr(lowercase_				,    "ignore_index"    )    )
													self.assertTrue(hasattr(lowercase_				,    "class_info_file"    )    )
													self.assertTrue(hasattr(lowercase_				,    "num_text"    )    )
													self.assertTrue(hasattr(lowercase_				,    "repo_path"    )    )
													self.assertTrue(hasattr(lowercase_				,    "metadata"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_reduce_labels"    )    )



							def 	UpperCamelCase	(						self    ):
													pass



							def 	UpperCamelCase	(						self    ):
													# Initialize image_processor
													_snake_case						:					Optional[int]    =   self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													_snake_case						:					List[str]    =   prepare_image_inputs(self.image_processing_tester				,    equal_resolution=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    Image.Image    )

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

													_snake_case						:					str    =   self.image_processing_tester.get_expected_values(lowercase_    )

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

													# Test batched
													_snake_case						:					Optional[int]    =   self.image_processing_tester.get_expected_values(lowercase_				,    batched=lowercase_    )

													_snake_case						:					Any    =   image_processor(
													    lowercase_				,    ["semantic"] * len(lowercase_    )				,    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						:					int    =   self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													_snake_case						:					Union[str, Any]    =   prepare_image_inputs(self.image_processing_tester				,    equal_resolution=lowercase_				,    numpify=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    np.ndarray    )

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

													_snake_case						:					List[str]    =   self.image_processing_tester.get_expected_values(lowercase_    )

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

													# Test batched
													_snake_case						:					int    =   self.image_processing_tester.get_expected_values(lowercase_				,    batched=lowercase_    )

													_snake_case						:					Optional[Any]    =   image_processor(
													    lowercase_				,    ["semantic"] * len(lowercase_    )				,    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						:					str    =   self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													_snake_case						:					Any    =   prepare_image_inputs(self.image_processing_tester				,    equal_resolution=lowercase_				,    torchify=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    torch.Tensor    )

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

													_snake_case						:					List[str]    =   self.image_processing_tester.get_expected_values(lowercase_    )

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

													# Test batched
													_snake_case						:					List[str]    =   self.image_processing_tester.get_expected_values(lowercase_				,    batched=lowercase_    )

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

													_snake_case						:					int    =   image_processor(
													    lowercase_				,    ["semantic"] * len(lowercase_    )				,    lowercase_				,    return_tensors="pt"				,    instance_id_to_semantic_id=lowercase_				,    pad_and_return_pixel_mask=lowercase_				,    )

													return inputs



							def 	UpperCamelCase	(						self    ):
													pass



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

																			_snake_case						:					List[str]    =   inputs["mask_labels"]
																			_snake_case						:					str    =   inputs["class_labels"]
																			_snake_case						:					Union[str, Any]    =   inputs["pixel_values"]
																			_snake_case						:					List[str]    =   inputs["text_inputs"]

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

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   np.zeros((20, 50)    )
													_snake_case						:					Tuple    =   1
													_snake_case						:					List[Any]    =   1
													_snake_case						:					Union[str, Any]    =   1

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[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"				,    )
													_snake_case						:					Tuple    =   self.image_processing_tester.get_fake_oneformer_outputs()

													_snake_case						:					Optional[int]    =   fature_extractor.post_process_semantic_segmentation(lowercase_    )

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

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

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					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"				,    )
													_snake_case						:					Tuple    =   self.image_processing_tester.get_fake_oneformer_outputs()
													_snake_case						:					Optional[int]    =   image_processor.post_process_instance_segmentation(lowercase_				,    threshold=0    )

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[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"				,    )
													_snake_case						:					List[Any]    =   self.image_processing_tester.get_fake_oneformer_outputs()
													_snake_case						:					Any    =   image_processor.post_process_panoptic_segmentation(lowercase_				,    threshold=0    )

													self.assertTrue(len(lowercase_    ) == 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"]    )				,    lowercase_    )
																			self.assertEqual(
																			    el["segmentation"].shape				,    (self.image_processing_tester.height, self.image_processing_tester.width)    )
from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass



@dataclass
class        lowercase_   :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42




class        lowercase_   :



							def __init__(						self				,    lowercase_    ):
													_snake_case						:					list[list[Edge]]    =   [[] for _ in range(lowercase_    )]
													_snake_case						:					Union[str, Any]    =   size



							def __getitem__(						self				,    lowercase_    ):
													return iter(self._graph[vertex]    )



							@property
							def 	UpperCamelCase	(						self    ):
													return self._size



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													if weight not in (0, 1):
																			raise ValueError("Edge weight must be either 0 or 1."    )

													if to_vertex < 0 or to_vertex >= self.size:
																			raise ValueError("Vertex indexes must be in [0; size)."    )

													self._graph[from_vertex].append(Edge(lowercase_				,    lowercase_    )    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Optional[int]    =   deque([start_vertex]    )
													_snake_case						:					list[int | None]    =   [None] * self.size
													_snake_case						:					Tuple    =   0

													while queue:
																			_snake_case						:					List[Any]    =   queue.popleft()
																			_snake_case						:					Tuple    =   distances[current_vertex]
																			if current_distance is None:
																									continue

																			for edge in self[current_vertex]:
																									_snake_case						:					Dict    =   current_distance + edge.weight
																									_snake_case						:					str    =   distances[edge.destination_vertex]
																									if (
																									    isinstance(lowercase_				,    lowercase_    )
																									    and new_distance >= dest_vertex_distance
																									):
																															continue
																									_snake_case						:					List[Any]    =   new_distance
																									if edge.weight == 0:
																															queue.appendleft(edge.destination_vertex    )
																									else:
																															queue.append(edge.destination_vertex    )

													if distances[finish_vertex] is None:
																			raise ValueError("No path from start_vertex to finish_vertex."    )

													return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()