






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,
)


a_		:						Optional[int]             =    {
    '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 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= {}
			state_dict.pop('pixel_mean'							,       _UpperCAmelCase)
			state_dict.pop('pixel_std'							,       _UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= 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:
												SCREAMING_SNAKE_CASE						= key.replace(_UpperCAmelCase							,       _UpperCAmelCase)

						if re.match(_UpperCAmelCase							,       _UpperCAmelCase):
									SCREAMING_SNAKE_CASE						= int(re.match(_UpperCAmelCase							,       _UpperCAmelCase).group(2))
									if layer_nb == 0:
												SCREAMING_SNAKE_CASE						= key.replace('layers.0'							,       'proj_in')
									elif layer_nb == 1:
												SCREAMING_SNAKE_CASE						= key.replace('layers.1'							,       'layers.0')
									elif layer_nb == 2:
												SCREAMING_SNAKE_CASE						= key.replace('layers.2'							,       'proj_out')

						SCREAMING_SNAKE_CASE						= value

			SCREAMING_SNAKE_CASE						= model_state_dict[
			    'prompt_encoder.shared_embedding.positional_embedding'
			]

			return model_state_dict






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase="ybelkada/segment-anything"):
			SCREAMING_SNAKE_CASE						= hf_hub_download(_UpperCAmelCase							,       F'''checkpoints/{model_name}.pth''')

			if "sam_vit_b" in model_name:
						SCREAMING_SNAKE_CASE						= SamConfig()
			elif "sam_vit_l" in model_name:
						SCREAMING_SNAKE_CASE						= SamVisionConfig(
						    hidden_size=1024							,       num_hidden_layers=24							,       num_attention_heads=16							,       global_attn_indexes=[5, 11, 17, 23]							,       )

						SCREAMING_SNAKE_CASE						= SamConfig(
						    vision_config=_UpperCAmelCase							,       )
			elif "sam_vit_h" in model_name:
						SCREAMING_SNAKE_CASE						= SamVisionConfig(
						    hidden_size=1280							,       num_hidden_layers=32							,       num_attention_heads=16							,       global_attn_indexes=[7, 15, 23, 31]							,       )

						SCREAMING_SNAKE_CASE						= SamConfig(
						    vision_config=_UpperCAmelCase							,       )

			SCREAMING_SNAKE_CASE						= torch.load(_UpperCAmelCase							,       map_location='cpu')
			SCREAMING_SNAKE_CASE						= replace_keys(_UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= SamImageProcessor()

			SCREAMING_SNAKE_CASE						= SamProcessor(image_processor=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= SamModel(_UpperCAmelCase)

			hf_model.load_state_dict(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= hf_model.to('cuda')

			SCREAMING_SNAKE_CASE						= 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
			SCREAMING_SNAKE_CASE						= Image.open(requests.get(_UpperCAmelCase							,       stream=_UpperCAmelCase).raw).convert('RGB')

			SCREAMING_SNAKE_CASE						= [[[400, 650]]]
			SCREAMING_SNAKE_CASE						= [[1]]

			SCREAMING_SNAKE_CASE						= processor(images=np.array(_UpperCAmelCase)							,       return_tensors='pt').to('cuda')

			with torch.no_grad():
						SCREAMING_SNAKE_CASE						= hf_model(**_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= output.iou_scores.squeeze()

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

						SCREAMING_SNAKE_CASE						= processor(
						    images=np.array(_UpperCAmelCase)							,       input_points=_UpperCAmelCase							,       input_labels=_UpperCAmelCase							,       return_tensors='pt').to('cuda')

						with torch.no_grad():
									SCREAMING_SNAKE_CASE						= hf_model(**_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= output.iou_scores.squeeze()

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

						SCREAMING_SNAKE_CASE						= ((75, 275, 1725, 850),)

						SCREAMING_SNAKE_CASE						= processor(images=np.array(_UpperCAmelCase)							,       input_boxes=_UpperCAmelCase							,       return_tensors='pt').to('cuda')

						with torch.no_grad():
									SCREAMING_SNAKE_CASE						= hf_model(**_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= output.iou_scores.squeeze()

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

						# Test with 2 points and 1 image.
						SCREAMING_SNAKE_CASE						= [[[400, 650], [800, 650]]]
						SCREAMING_SNAKE_CASE						= [[1, 1]]

						SCREAMING_SNAKE_CASE						= processor(
						    images=np.array(_UpperCAmelCase)							,       input_points=_UpperCAmelCase							,       input_labels=_UpperCAmelCase							,       return_tensors='pt').to('cuda')

						with torch.no_grad():
									SCREAMING_SNAKE_CASE						= hf_model(**_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= output.iou_scores.squeeze()

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


if __name__ == "__main__":
				a_		:						int             =    argparse.ArgumentParser()
				a_		:						Optional[int]             =    ['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',
				)

				a_		:						int             =    parser.parse_args()

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












from __future__ import annotations

import time

a_		:						Tuple             =    list[tuple[int, int]]

a_		:						Optional[int]             =    [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a_		:						Dict             =    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class 		_snake_case  :






			def __init__(       self		,					a		,					a		,					a		,					a		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= pos_x
						SCREAMING_SNAKE_CASE						= pos_y
						SCREAMING_SNAKE_CASE						= (pos_y, pos_x)
						SCREAMING_SNAKE_CASE						= goal_x
						SCREAMING_SNAKE_CASE						= goal_y
						SCREAMING_SNAKE_CASE						= parent



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Dict:
						SCREAMING_SNAKE_CASE						= Node(start[1]		,					start[0]		,					goal[1]		,					goal[0]		,					a)
						SCREAMING_SNAKE_CASE						= Node(goal[1]		,					goal[0]		,					goal[1]		,					goal[0]		,					a)

						SCREAMING_SNAKE_CASE						= [self.start]
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.node_queue:
									SCREAMING_SNAKE_CASE						= self.node_queue.pop(0)

									if current_node.pos == self.target.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_path(a)

									SCREAMING_SNAKE_CASE						= self.get_successors(a)

									for node in successors:
												self.node_queue.append(a)

						if not self.reached:
									return [self.start.pos]
						return None






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       list[Node]:
						SCREAMING_SNAKE_CASE						= []
						for action in delta:
									SCREAMING_SNAKE_CASE						= parent.pos_x + action[1]
									SCREAMING_SNAKE_CASE						= parent.pos_y + action[0]
									if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(a) - 1):
												continue

									if grid[pos_y][pos_x] != 0:
												continue

									successors.append(
									    Node(a		,					a		,					self.target.pos_y		,					self.target.pos_x		,					a))
						return successors







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= node
						SCREAMING_SNAKE_CASE						= []
						while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x))
									SCREAMING_SNAKE_CASE						= current_node.parent
						path.reverse()
						return path



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
									SCREAMING_SNAKE_CASE						= self.fwd_bfs.node_queue.pop(0)
									SCREAMING_SNAKE_CASE						= self.bwd_bfs.node_queue.pop(0)

									if current_bwd_node.pos == current_fwd_node.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_bidirectional_path(
												    a		,					a)

									SCREAMING_SNAKE_CASE						= current_bwd_node
									SCREAMING_SNAKE_CASE						= current_fwd_node

									SCREAMING_SNAKE_CASE						= {
									    self.fwd_bfs: self.fwd_bfs.get_successors(a),
									    self.bwd_bfs: self.bwd_bfs.get_successors(a),
									}

									for bfs in [self.fwd_bfs, self.bwd_bfs]:
												for node in successors[bfs]:
															bfs.node_queue.append(a)

						if not self.reached:
									return [self.fwd_bfs.start.pos]
						return None







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= self.fwd_bfs.retrace_path(a)
						SCREAMING_SNAKE_CASE						= self.bwd_bfs.retrace_path(a)
						bwd_path.pop()
						bwd_path.reverse()
						SCREAMING_SNAKE_CASE						= fwd_path + bwd_path
						return path


if __name__ == "__main__":
				# all coordinates are given in format [y,x]
				import doctest

				doctest.testmod()
				a_		:						Union[str, Any]             =    (0, 0)
				a_		:						Any             =    (len(grid) - 1, len(grid[0]) - 1)
				for elem in grid:
								print(elem)

				a_		:						Optional[Any]             =    time.time()
				a_		:						Union[str, Any]             =    BreadthFirstSearch(init, goal)
				a_		:						Optional[Any]             =    bfs.search()
				a_		:						List[str]             =    time.time() - start_bfs_time

				print('Unidirectional BFS computation time : ', bfs_time)

				a_		:						Optional[int]             =    time.time()
				a_		:						Union[str, Any]             =    BidirectionalBreadthFirstSearch(init, goal)
				a_		:						List[Any]             =    bd_bfs.search()
				a_		:						List[str]             =    time.time() - start_bd_bfs_time

				print('Bidirectional BFS computation time : ', bd_bfs_time)




