import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


_UpperCAmelCase          =						{
    """iou_prediction_head.layers.0""": """iou_prediction_head.proj_in""",
    """iou_prediction_head.layers.1""": """iou_prediction_head.layers.0""",
    """iou_prediction_head.layers.2""": """iou_prediction_head.proj_out""",
    """mask_decoder.output_upscaling.0""": """mask_decoder.upscale_conv1""",
    """mask_decoder.output_upscaling.1""": """mask_decoder.upscale_layer_norm""",
    """mask_decoder.output_upscaling.3""": """mask_decoder.upscale_conv2""",
    """mask_downscaling.0""": """mask_embed.conv1""",
    """mask_downscaling.1""": """mask_embed.layer_norm1""",
    """mask_downscaling.3""": """mask_embed.conv2""",
    """mask_downscaling.4""": """mask_embed.layer_norm2""",
    """mask_downscaling.6""": """mask_embed.conv3""",
    """point_embeddings""": """point_embed""",
    """pe_layer.positional_encoding_gaussian_matrix""": """shared_embedding.positional_embedding""",
    """image_encoder""": """vision_encoder""",
    """neck.0""": """neck.conv1""",
    """neck.1""": """neck.layer_norm1""",
    """neck.2""": """neck.conv2""",
    """neck.3""": """neck.layer_norm2""",
    """patch_embed.proj""": """patch_embed.projection""",
    """.norm""": """.layer_norm""",
    """blocks""": """layers""",
}




def 							UpperCamelCase		(			__lowercase		:					Optional[int]      ):
		'''simple docstring'''




		A_						:							List[str]      = {}
		state_dict.pop('pixel_mean'		,__snake_case      )
		state_dict.pop('pixel_std'		,__snake_case      )

		A_						:							Optional[int]      = r'.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'

		for key, value in state_dict.items():
				for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
						if key_to_modify in key:
								A_						:							List[Any]      = key.replace(__snake_case		,__snake_case      )

				if re.match(__snake_case		,__snake_case      ):
						A_						:							str      = int(re.match(__snake_case		,__snake_case      ).group(2      )      )
						if layer_nb == 0:
								A_						:							List[Any]      = key.replace('layers.0'		,'proj_in'      )
						elif layer_nb == 1:
								A_						:							Dict      = key.replace('layers.1'		,'layers.0'      )
						elif layer_nb == 2:
								A_						:							str      = key.replace('layers.2'		,'proj_out'      )

				A_						:							Dict      = value

		A_						:							str      = model_state_dict[
		    'prompt_encoder.shared_embedding.positional_embedding'
		]

		return model_state_dict




def 							UpperCamelCase		(			__lowercase		:					str		,__lowercase		:					Any		,__lowercase		:					int		,__lowercase		:					Optional[Any]="ybelkada/segment-anything"      ):
		'''simple docstring'''




		A_						:							Optional[int]      = hf_hub_download(__snake_case		,f'''checkpoints/{model_name}.pth'''      )

		if "sam_vit_b" in model_name:
				A_						:							Optional[int]      = SamConfig()
		elif "sam_vit_l" in model_name:
				A_						:							List[Any]      = SamVisionConfig(
				    hidden_size=10_24		,num_hidden_layers=24		,num_attention_heads=16		,global_attn_indexes=[5, 11, 17, 23]		,)

				A_						:							Optional[int]      = SamConfig(
				    vision_config=__snake_case		,)
		elif "sam_vit_h" in model_name:
				A_						:							Tuple      = SamVisionConfig(
				    hidden_size=12_80		,num_hidden_layers=32		,num_attention_heads=16		,global_attn_indexes=[7, 15, 23, 31]		,)

				A_						:							List[Any]      = SamConfig(
				    vision_config=__snake_case		,)

		A_						:							List[Any]      = torch.load(__snake_case		,map_location='cpu'      )
		A_						:							List[Any]      = replace_keys(__snake_case      )

		A_						:							List[Any]      = SamImageProcessor()

		A_						:							Optional[int]      = SamProcessor(image_processor=__snake_case      )
		A_						:							Tuple      = SamModel(__snake_case      )

		hf_model.load_state_dict(__snake_case      )
		A_						:							Union[str, Any]      = hf_model.to('cuda'      )

		A_						:							List[Any]      = 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
		A_						:							int      = Image.open(requests.get(__snake_case		,stream=__snake_case      ).raw      ).convert('RGB'      )

		A_						:							int      = [[[4_00, 6_50]]]
		A_						:							Optional[Any]      = [[1]]

		A_						:							Optional[int]      = processor(images=np.array(__snake_case      )		,return_tensors='pt'      ).to('cuda'      )

		with torch.no_grad():
				A_						:							Any      = hf_model(**__snake_case      )
		A_						:							List[str]      = output.iou_scores.squeeze()

		if model_name == "sam_vit_h_4b8939":
				assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

				A_						:							List[Any]      = processor(
				    images=np.array(__snake_case      )		,input_points=__snake_case		,input_labels=__snake_case		,return_tensors='pt'      ).to('cuda'      )

				with torch.no_grad():
						A_						:							int      = hf_model(**__snake_case      )
				A_						:							List[str]      = output.iou_scores.squeeze()

				assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

				A_						:							int      = ((75, 2_75, 17_25, 8_50),)

				A_						:							Optional[int]      = processor(images=np.array(__snake_case      )		,input_boxes=__snake_case		,return_tensors='pt'      ).to('cuda'      )

				with torch.no_grad():
						A_						:							Tuple      = hf_model(**__snake_case      )
				A_						:							Any      = output.iou_scores.squeeze()

				assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

				# Test with 2 points and 1 image.
				A_						:							List[str]      = [[[4_00, 6_50], [8_00, 6_50]]]
				A_						:							Any      = [[1, 1]]

				A_						:							Dict      = processor(
				    images=np.array(__snake_case      )		,input_points=__snake_case		,input_labels=__snake_case		,return_tensors='pt'      ).to('cuda'      )

				with torch.no_grad():
						A_						:							str      = hf_model(**__snake_case      )
				A_						:							int      = output.iou_scores.squeeze()

				assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
	_UpperCAmelCase          =						argparse.ArgumentParser()
	_UpperCAmelCase          =						["""sam_vit_b_01ec64""", """sam_vit_h_4b8939""", """sam_vit_l_0b3195"""]
	parser.add_argument(
	    """--model_name""",
	    default="""sam_vit_h_4b8939""",
	    choices=choices,
	    type=str,
	    help="""Path to hf config.json of model to convert""",
	)
	parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	    help="""Whether to push the model and processor to the hub after converting""",
	)
	parser.add_argument(
	    """--model_hub_id""",
	    default="""ybelkada/segment-anything""",
	    choices=choices,
	    type=str,
	    help="""Path to hf config.json of model to convert""",
	)

	_UpperCAmelCase          =						parser.parse_args()

	convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)







import sys
from collections import defaultdict
class     lowerCAmelCase_     :







							def __init__(   self      :     Optional[int]		):
												_UpperCamelCase			     =     []







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     str		):
												return self.node_position[vertex]







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     List[str] ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     pos







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     List[str] ,			_A      :     int ,			_A      :     Optional[Any] ,			_A      :     Union[str, Any]		):
												if start > size // 2 - 1:
																	return
												else:
																	if 2 * start + 2 >= size:
																						_UpperCamelCase			     =     2 * start + 1
																	else:
																						if heap[2 * start + 1] < heap[2 * start + 2]:
																											_UpperCamelCase			     =     2 * start + 1
																						else:
																											_UpperCamelCase			     =     2 * start + 2
																	if heap[smallest_child] < heap[start]:
																						_UpperCamelCase     ,    _UpperCamelCase			     =     heap[smallest_child], positions[smallest_child]
																						_UpperCamelCase     ,    _UpperCamelCase			     =     (
																						    heap[start],
																						    positions[start],
																						)
																						_UpperCamelCase     ,    _UpperCamelCase			     =     temp, tempa

																						_UpperCamelCase			     =     self.get_position(positions[smallest_child]		)
																						self.set_position(
																						    positions[smallest_child] ,			self.get_position(positions[start]		)		)
																						self.set_position(positions[start] ,			_A		)

																						self.top_to_bottom(_A ,			_A ,			_A ,			_A		)







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Tuple ,			_A      :     Optional[Any] ,			_A      :     int ,			_A      :     Optional[int]		):
												_UpperCamelCase			     =     position[index]

												while index != 0:
																	_UpperCamelCase			     =     int((index - 2) / 2		) if index % 2 == 0 else int((index - 1) / 2		)

																	if val < heap[parent]:
																						_UpperCamelCase			     =     heap[parent]
																						_UpperCamelCase			     =     position[parent]
																						self.set_position(position[parent] ,			_A		)
																	else:
																						_UpperCamelCase			     =     val
																						_UpperCamelCase			     =     temp
																						self.set_position(_A ,			_A		)
																						break
																	_UpperCamelCase			     =     parent
												else:
																	_UpperCamelCase			     =     val
																	_UpperCamelCase			     =     temp
																	self.set_position(_A ,			0		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     Tuple ,			_A      :     int		):
												_UpperCamelCase			     =     len(_A		) // 2 - 1
												for i in range(_A ,			-1 ,			-1		):
																	self.top_to_bottom(_A ,			_A ,			len(_A		) ,			_A		)







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     int ,			_A      :     List[str]		):
												_UpperCamelCase			     =     positions[0]
												_UpperCamelCase			     =     sys.maxsize
												self.top_to_bottom(_A ,			0 ,			len(_A		) ,			_A		)
												return temp




def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     Heap()

					_UpperCamelCase			     =     [0] * len(__snake_case						)
					_UpperCamelCase			     =     [-1] * len(__snake_case						)  # Neighboring Tree Vertex of selected vertex
					# Minimum Distance of explored vertex with neighboring vertex of partial tree
					# formed in graph
					_UpperCamelCase			     =     []  # Heap of Distance of vertices from their neighboring vertex
					_UpperCamelCase			     =     []

					for vertex in range(len(__snake_case						)						):
										distance_tv.append(sys.maxsize						)
										positions.append(__snake_case						)
										heap.node_position.append(__snake_case						)

					_UpperCamelCase			     =     []
					_UpperCamelCase			     =     1
					_UpperCamelCase			     =     sys.maxsize
					for neighbor, distance in adjacency_list[0]:
										_UpperCamelCase			     =     0
										_UpperCamelCase			     =     distance
					heap.heapify(__snake_case  ,     __snake_case						)

					for _ in range(1  ,     len(__snake_case						)						):
										_UpperCamelCase			     =     heap.delete_minimum(__snake_case  ,     __snake_case						)
										if visited[vertex] == 0:
															tree_edges.append((nbr_tv[vertex], vertex)						)
															_UpperCamelCase			     =     1
															for neighbor, distance in adjacency_list[vertex]:
																				if (
																				    visited[neighbor] == 0
																				    and distance < distance_tv[heap.get_position(__snake_case						)]
																				):
																									_UpperCamelCase			     =     distance
																									heap.bottom_to_top(
																									    __snake_case  ,     heap.get_position(__snake_case						)  ,     __snake_case  ,     __snake_case						)
																									_UpperCamelCase			     =     vertex
					return tree_edges


if __name__ == "__main__":  # pragma: no cover
					# < --------- Prims Algorithm --------- >
					_lowerCAmelCase       =		int(input("Enter number of edges: ").strip())
					_lowerCAmelCase       =		defaultdict(list)
					for _ in range(edges_number):
										_lowerCAmelCase       =		[int(x) for x in input().strip().split()]
										adjacency_list[edge[0]].append([edge[1], edge[2]])
										adjacency_list[edge[1]].append([edge[0], edge[2]])
					print(prisms_algorithm(adjacency_list))
