

"""simple docstring"""






from __future__ import annotations

_lowerCAmelCase       						=		[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]

def 		lowerCamelCase__		(       _lowerCamelCase			, _lowerCamelCase			, _lowerCamelCase			, _lowerCamelCase			, _lowerCamelCase			, ):

    '''simple docstring'''



    _lowerCAmelCase							: List[str]	=     [
        [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
    ]  # the reference grid
    _lowerCAmelCase							: int	=     1
    _lowerCAmelCase							: Any	=     [
        [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
    ]  # the action grid

    _lowerCAmelCase							: Tuple	=     init[0]
    _lowerCAmelCase							: List[str]	=     init[1]
    _lowerCAmelCase							: Optional[int]	=     0
    _lowerCAmelCase							: Union[str, Any]	=     g + heuristic[x][y]  # cost from starting cell to destination cell
    _lowerCAmelCase							: Optional[Any]	=     [[f, g, x, y]]

    _lowerCAmelCase							: Dict	=     False  # flag that is set when search is complete
    _lowerCAmelCase							: Optional[int]	=     False  # flag set if we can't find expand

    while not found and not resign:
        if len(_lowerCamelCase ) == 0:
            raise ValueError('Algorithm is unable to find solution' )
        else:  # to choose the least costliest action so as to move closer to the goal
            cell.sort()
            cell.reverse()
            _lowerCAmelCase							: List[str]	=     cell.pop()
            _lowerCAmelCase							: List[Any]	=     next_cell[2]
            _lowerCAmelCase							: str	=     next_cell[3]
            _lowerCAmelCase							: int	=     next_cell[1]

            if x == goal[0] and y == goal[1]:
                _lowerCAmelCase							: Optional[Any]	=     True
            else:
                for i in range(len(_lowerCamelCase ) ):  # to try out different valid actions
                    _lowerCAmelCase							: List[Any]	=     x + DIRECTIONS[i][0]
                    _lowerCAmelCase							: Union[str, Any]	=     y + DIRECTIONS[i][1]
                    if xa >= 0 and xa < len(_lowerCamelCase ) and ya >= 0 and ya < len(grid[0] ):
                        if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                            _lowerCAmelCase							: Dict	=     g + cost
                            _lowerCAmelCase							: Optional[Any]	=     ga + heuristic[xa][ya]
                            cell.append([fa, ga, xa, ya] )
                            _lowerCAmelCase							: Any	=     1
                            _lowerCAmelCase							: str	=     i
    _lowerCAmelCase							: List[str]	=     []
    _lowerCAmelCase							: Tuple	=     goal[0]
    _lowerCAmelCase							: int	=     goal[1]
    invpath.append([x, y] )  # we get the reverse path from here
    while x != init[0] or y != init[1]:
        _lowerCAmelCase							: str	=     x - DIRECTIONS[action[x][y]][0]
        _lowerCAmelCase							: Union[str, Any]	=     y - DIRECTIONS[action[x][y]][1]
        _lowerCAmelCase							: Optional[int]	=     xa
        _lowerCAmelCase							: Optional[int]	=     ya
        invpath.append([x, y] )

    _lowerCAmelCase							: Tuple	=     []
    for i in range(len(_lowerCamelCase ) ):
        path.append(invpath[len(_lowerCamelCase ) - 1 - i] )
    return path, action


if __name__ == "__main__":
 _lowerCAmelCase       						=		[
     [0, 1, 0, 0, 0, 0],
     [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
     [0, 1, 0, 0, 0, 0],
     [0, 1, 0, 0, 1, 0],
     [0, 0, 0, 0, 1, 0],
 ]

 _lowerCAmelCase       						=		[0, 0]
 # all coordinates are given in format [y,x]
 _lowerCAmelCase       						=		[len(grid) - 1, len(grid[0]) - 1]
 _lowerCAmelCase       						=		1

 # the cost map which pushes the path closer to the goal
 _lowerCAmelCase       						=		[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
 for i in range(len(grid)):
  for j in range(len(grid[0])):
   _lowerCAmelCase       						=		abs(i - goal[0]) + abs(j - goal[1])
   if grid[i][j] == 1:
    # added extra penalty in the heuristic map
    _lowerCAmelCase       						=		9_9

 _lowerCAmelCase       						=		search(grid, init, goal, cost, heuristic)

 print("""ACTION MAP""")
 for i in range(len(action)):
  print(action[i])

 for i in range(len(path)):
  print(path[i])












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,
)


SCREAMING_SNAKE_CASE_   :      str			=   {
    '''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  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     {}
		state_dict.pop('pixel_mean'						,   snake_case							)
		state_dict.pop('pixel_std'						,   snake_case							)

		__lowercase											=     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:
								__lowercase											=     key.replace(snake_case						,   snake_case							)

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

				__lowercase											=     value

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

		return model_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case="ybelkada/segment-anything"							)		->    int:
		__lowercase											=     hf_hub_download(snake_case						,   F"checkpoints/{model_name}.pth"							)

		if "sam_vit_b" in model_name:
				__lowercase											=     SamConfig()
		elif "sam_vit_l" in model_name:
				__lowercase											=     SamVisionConfig(
				    hidden_size=1_024						,   num_hidden_layers=24						,   num_attention_heads=16						,   global_attn_indexes=[5, 11, 17, 23]						,   )

				__lowercase											=     SamConfig(
				    vision_config=snake_case						,   )
		elif "sam_vit_h" in model_name:
				__lowercase											=     SamVisionConfig(
				    hidden_size=1_280						,   num_hidden_layers=32						,   num_attention_heads=16						,   global_attn_indexes=[7, 15, 23, 31]						,   )

				__lowercase											=     SamConfig(
				    vision_config=snake_case						,   )

		__lowercase											=     torch.load(snake_case						,   map_location='cpu'							)
		__lowercase											=     replace_keys(snake_case							)

		__lowercase											=     SamImageProcessor()

		__lowercase											=     SamProcessor(image_processor=snake_case							)
		__lowercase											=     SamModel(snake_case							)

		hf_model.load_state_dict(snake_case							)
		__lowercase											=     hf_model.to('cuda'							)

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

		__lowercase											=     [[[400, 650]]]
		__lowercase											=     [[1]]

		__lowercase											=     processor(images=np.array(snake_case							)						,   return_tensors='pt'							).to('cuda'							)

		with torch.no_grad():
				__lowercase											=     hf_model(**snake_case							)
		__lowercase											=     output.iou_scores.squeeze()

		if model_name == "sam_vit_h_4b8939":
				assert scores[-1].item() == 0.579_8902_5115_9668

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

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

				assert scores[-1].item() == 0.9712_6030_9219_3604

				__lowercase											=     ((75, 275, 1_725, 850),)

				__lowercase											=     processor(images=np.array(snake_case							)						,   input_boxes=snake_case						,   return_tensors='pt'							).to('cuda'							)

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

				assert scores[-1].item() == 0.8686_0156_0592_6514

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

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

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

				assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   argparse.ArgumentParser()
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   ['''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''',
		)

		SCREAMING_SNAKE_CASE_   :      List[Any]			=   parser.parse_args()

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




