




"""simple docstring"""







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 PIL import Image

						from transformers import DetaImageProcessor




class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def __init__(     self					:	Any    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	int=7    ,   lowercase__					:	Union[str, Any]=3    ,   lowercase__					:	Tuple=3_0    ,   lowercase__					:	Tuple=4_0_0    ,   lowercase__					:	List[Any]=True    ,   lowercase__					:	Union[str, Any]=None    ,   lowercase__					:	Tuple=True    ,   lowercase__					:	List[str]=[0.5, 0.5, 0.5]    ,   lowercase__					:	Dict=[0.5, 0.5, 0.5]    ,   lowercase__					:	Tuple=True    ,   lowercase__					:	Union[str, Any]=1 / 2_5_5    ,   lowercase__					:	str=True    ,   ):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						__lowercase				:		str												=				size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						__lowercase				:		Any												=				parent
						__lowercase				:		Optional[int]												=				batch_size
						__lowercase				:		List[Any]												=				num_channels
						__lowercase				:		List[Any]												=				min_resolution
						__lowercase				:		List[Any]												=				max_resolution
						__lowercase				:		Any												=				do_resize
						__lowercase				:		Any												=				size
						__lowercase				:		Union[str, Any]												=				do_normalize
						__lowercase				:		Optional[Any]												=				image_mean
						__lowercase				:		Dict												=				image_std
						__lowercase				:		Optional[Any]												=				do_rescale
						__lowercase				:		Tuple												=				rescale_factor
						__lowercase				:		Optional[Any]												=				do_pad



				def       snake_case    (     self					:	Optional[Any]						):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}



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

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

						return expected_height, expected_width





@require_torch
@require_vision
class       lowerCAmelCase__       (			lowerCAmelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     List[str]       =      DetaImageProcessor if is_vision_available() else None



				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		Optional[Any]												=				DetaImageProcessingTester(self						)



				@property
				def       snake_case    (     self					:	str						):
						return self.image_processor_tester.prepare_image_processor_dict()



				def       snake_case    (     self					:	Optional[Any]						):
						__lowercase				:		Tuple												=				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__    ,   "do_rescale"						)						)
						self.assertTrue(hasattr(lowercase__    ,   "do_pad"						)						)
						self.assertTrue(hasattr(lowercase__    ,   "size"						)						)



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		int												=				self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size    ,   {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}						)
						self.assertEqual(image_processor.do_pad    ,   lowercase__						)



				def       snake_case    (     self					:	Tuple						):
						pass



				def       snake_case    (     self					:	int						):
						# Initialize image_processing
						__lowercase				:		Union[str, Any]												=				self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						__lowercase				:		List[str]												=				prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=lowercase__						)
						for image in image_inputs:
								self.assertIsInstance(lowercase__    ,   Image.Image						)

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

						__lowercase						,__lowercase				:		Tuple												=				self.image_processor_tester.get_expected_values(lowercase__						)

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

						# Test batched
						__lowercase						,__lowercase				:		List[Any]												=				self.image_processor_tester.get_expected_values(lowercase__    ,   batched=lowercase__						)

						__lowercase				:		Dict												=				image_processing(lowercase__    ,   return_tensors="pt"						).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,   (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )    ,   )



				def       snake_case    (     self					:	List[str]						):
						# Initialize image_processing
						__lowercase				:		Any												=				self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						__lowercase				:		Optional[int]												=				prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=lowercase__    ,   numpify=lowercase__						)
						for image in image_inputs:
								self.assertIsInstance(lowercase__    ,   np.ndarray						)

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

						__lowercase						,__lowercase				:		Union[str, Any]												=				self.image_processor_tester.get_expected_values(lowercase__						)

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

						# Test batched
						__lowercase				:		List[Any]												=				image_processing(lowercase__    ,   return_tensors="pt"						).pixel_values

						__lowercase						,__lowercase				:		List[Any]												=				self.image_processor_tester.get_expected_values(lowercase__    ,   batched=lowercase__						)

						self.assertEqual(
						    encoded_images.shape    ,   (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )    ,   )



				def       snake_case    (     self					:	List[str]						):
						# Initialize image_processing
						__lowercase				:		Dict												=				self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						__lowercase				:		Any												=				prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=lowercase__    ,   torchify=lowercase__						)
						for image in image_inputs:
								self.assertIsInstance(lowercase__    ,   torch.Tensor						)

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

						__lowercase						,__lowercase				:		List[str]												=				self.image_processor_tester.get_expected_values(lowercase__						)

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

						# Test batched
						__lowercase				:		List[str]												=				image_processing(lowercase__    ,   return_tensors="pt"						).pixel_values

						__lowercase						,__lowercase				:		int												=				self.image_processor_tester.get_expected_values(lowercase__    ,   batched=lowercase__						)

						self.assertEqual(
						    encoded_images.shape    ,   (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )    ,   )



				@slow
				def       snake_case    (     self					:	List[str]						):
						# prepare image and target
						__lowercase				:		int												=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"    ,   "r"						) as f:
								__lowercase				:		List[Any]												=				json.loads(f.read()						)

						__lowercase				:		Optional[int]												=				{"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						__lowercase				:		Any												=				DetaImageProcessor()
						__lowercase				:		Union[str, Any]												=				image_processing(images=lowercase__    ,   annotations=lowercase__    ,   return_tensors="pt"						)

						# verify pixel values
						__lowercase				:		Any												=				torch.Size([1, 3, 8_0_0, 1_0_6_6]						)
						self.assertEqual(encoding["pixel_values"].shape    ,   lowercase__						)

						__lowercase				:		Union[str, Any]												=				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,   lowercase__    ,   atol=1e-4						)						)

						# verify area
						__lowercase				:		Dict												=				torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,   lowercase__						)						)
						# verify boxes
						__lowercase				:		List[str]												=				torch.Size([6, 4]						)
						self.assertEqual(encoding["labels"][0]["boxes"].shape    ,   lowercase__						)
						__lowercase				:		Tuple												=				torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,   lowercase__    ,   atol=1e-3						)						)
						# verify image_id
						__lowercase				:		str												=				torch.tensor([3_9_7_6_9]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,   lowercase__						)						)
						# verify is_crowd
						__lowercase				:		Union[str, Any]												=				torch.tensor([0, 0, 0, 0, 0, 0]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,   lowercase__						)						)
						# verify class_labels
						__lowercase				:		Optional[int]												=				torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,   lowercase__						)						)
						# verify orig_size
						__lowercase				:		List[Any]												=				torch.tensor([4_8_0, 6_4_0]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,   lowercase__						)						)
						# verify size
						__lowercase				:		int												=				torch.tensor([8_0_0, 1_0_6_6]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,   lowercase__						)						)



				@slow
				def       snake_case    (     self					:	List[Any]						):
						# prepare image, target and masks_path
						__lowercase				:		str												=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"    ,   "r"						) as f:
								__lowercase				:		Union[str, Any]												=				json.loads(f.read()						)

						__lowercase				:		Optional[int]												=				{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

						__lowercase				:		str												=				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

						# encode them
						__lowercase				:		List[Any]												=				DetaImageProcessor(format="coco_panoptic"						)
						__lowercase				:		Dict												=				image_processing(images=lowercase__    ,   annotations=lowercase__    ,   masks_path=lowercase__    ,   return_tensors="pt"						)

						# verify pixel values
						__lowercase				:		Any												=				torch.Size([1, 3, 8_0_0, 1_0_6_6]						)
						self.assertEqual(encoding["pixel_values"].shape    ,   lowercase__						)

						__lowercase				:		Tuple												=				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,   lowercase__    ,   atol=1e-4						)						)

						# verify area
						__lowercase				:		int												=				torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,   lowercase__						)						)
						# verify boxes
						__lowercase				:		Tuple												=				torch.Size([6, 4]						)
						self.assertEqual(encoding["labels"][0]["boxes"].shape    ,   lowercase__						)
						__lowercase				:		Dict												=				torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,   lowercase__    ,   atol=1e-3						)						)
						# verify image_id
						__lowercase				:		List[Any]												=				torch.tensor([3_9_7_6_9]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,   lowercase__						)						)
						# verify is_crowd
						__lowercase				:		str												=				torch.tensor([0, 0, 0, 0, 0, 0]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,   lowercase__						)						)
						# verify class_labels
						__lowercase				:		Dict												=				torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,   lowercase__						)						)
						# verify masks
						__lowercase				:		List[str]												=				8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()    ,   lowercase__						)
						# verify orig_size
						__lowercase				:		List[str]												=				torch.tensor([4_8_0, 6_4_0]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,   lowercase__						)						)
						# verify size
						__lowercase				:		Optional[int]												=				torch.tensor([8_0_0, 1_0_6_6]						)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,   lowercase__						)						)






"""simple docstring"""







from __future__ import annotations

import os
from collections.abc import Mapping

__A   :  str					=      tuple[int, int]




class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	set[int]    ,   lowercase__					:	Mapping[EdgeT, int]						):
						__lowercase				:		set[int]												=				vertices
						__lowercase				:		dict[EdgeT, int]												=				{
						    (min(lowercase__						), max(lowercase__						)): weight for edge, weight in edges.items()
						}



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	EdgeT    ,   lowercase__					:	int						):
						self.vertices.add(edge[0]						)
						self.vertices.add(edge[1]						)
						__lowercase				:		Union[str, Any]												=				weight



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Graph												=				Graph({min(self.vertices						)}    ,   {}						)
						__lowercase				:		EdgeT
						__lowercase				:		int
						__lowercase				:		EdgeT
						__lowercase				:		int

						while len(subgraph.vertices						) < len(self.vertices						):
								__lowercase				:		int												=				max(self.edges.values()						) + 1
								for edge, weight in self.edges.items():
										if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
												if weight < min_weight:
														__lowercase				:		Optional[Any]												=				edge
														__lowercase				:		List[str]												=				weight

								subgraph.add_edge(lowercase__    ,   lowercase__						)

						return subgraph





def 						snake_case__       (					_lowerCamelCase = "p107_network.txt"				)			->int:






		"""simple docstring"""


		__lowercase				:		str												=				os.path.abspath(os.path.dirname(_lowerCamelCase				)				)
		__lowercase				:		str												=				os.path.join(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		dict[EdgeT, int]												=				{}
		__lowercase				:		list[str]
		__lowercase				:		int
		__lowercase				:		int

		with open(_lowerCamelCase				) as f:
				__lowercase				:		List[str]												=				f.read().strip().split("\n"				)

		__lowercase				:		Any												=				[line.split(","				) for line in data]

		for edgea in range(1,  len(_lowerCamelCase				)				):
				for edgea in range(_lowerCamelCase				):
						if adjaceny_matrix[edgea][edgea] != "-":
								__lowercase				:		Dict												=				int(adjaceny_matrix[edgea][edgea]				)

		__lowercase				:		Graph												=				Graph(set(range(len(_lowerCamelCase				)				)				),  _lowerCamelCase				)

		__lowercase				:		Graph												=				graph.prims_algorithm()

		__lowercase				:		int												=				sum(graph.edges.values()				)
		__lowercase				:		int												=				sum(subgraph.edges.values()				)

		return initial_total - optimal_total


if __name__ == "__main__":
						print(F"""{solution() = }""")
