



"""simple docstring"""

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,
)


lowercase__			   =  {
    "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       __magic_name__    (   _lowerCamelCase				: Any					):
							__a		:		List[str]									=       {}
							state_dict.pop("""pixel_mean"""  ,       _lowerCamelCase					)
							state_dict.pop("""pixel_std"""  ,       _lowerCamelCase					)

							__a		:		Any									=       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		:		str									=       key.replace(_lowerCamelCase  ,       _lowerCamelCase					)

														if re.match(_lowerCamelCase  ,       _lowerCamelCase					):
																					__a		:		List[Any]									=       int(re.match(_lowerCamelCase  ,       _lowerCamelCase					).group(2					)					)
																					if layer_nb == 0:
																												__a		:		int									=       key.replace("""layers.0"""  ,       """proj_in"""					)
																					elif layer_nb == 1:
																												__a		:		Union[str, Any]									=       key.replace("""layers.1"""  ,       """layers.0"""					)
																					elif layer_nb == 2:
																												__a		:		Optional[int]									=       key.replace("""layers.2"""  ,       """proj_out"""					)

														__a		:		Dict									=       value

							__a		:		Optional[int]									=       model_state_dict[
							    """prompt_encoder.shared_embedding.positional_embedding"""
							]

							return model_state_dict





def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int="ybelkada/segment-anything"					):
							__a		:		int									=       hf_hub_download(_lowerCamelCase  ,       F'''checkpoints/{model_name}.pth'''					)

							if "sam_vit_b" in model_name:
														__a		:		Optional[int]									=       SamConfig()
							elif "sam_vit_l" in model_name:
														__a		:		Union[str, Any]									=       SamVisionConfig(
														    hidden_size=1_0_2_4  ,       num_hidden_layers=2_4  ,       num_attention_heads=1_6  ,       global_attn_indexes=[5, 1_1, 1_7, 2_3]  ,       )

														__a		:		Optional[Any]									=       SamConfig(
														    vision_config=_lowerCamelCase  ,       )
							elif "sam_vit_h" in model_name:
														__a		:		Optional[int]									=       SamVisionConfig(
														    hidden_size=1_2_8_0  ,       num_hidden_layers=3_2  ,       num_attention_heads=1_6  ,       global_attn_indexes=[7, 1_5, 2_3, 3_1]  ,       )

														__a		:		Optional[int]									=       SamConfig(
														    vision_config=_lowerCamelCase  ,       )

							__a		:		str									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
							__a		:		Dict									=       replace_keys(_lowerCamelCase					)

							__a		:		Any									=       SamImageProcessor()

							__a		:		Union[str, Any]									=       SamProcessor(image_processor=_lowerCamelCase					)
							__a		:		int									=       SamModel(_lowerCamelCase					)

							hf_model.load_state_dict(_lowerCamelCase					)
							__a		:		List[Any]									=       hf_model.to("""cuda"""					)

							__a		:		Tuple									=       """https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
							__a		:		str									=       Image.open(requests.get(_lowerCamelCase  ,       stream=_lowerCamelCase					).raw					).convert("""RGB"""					)

							__a		:		str									=       [[[4_0_0, 6_5_0]]]
							__a		:		Any									=       [[1]]

							__a		:		Optional[int]									=       processor(images=np.array(_lowerCamelCase					)  ,       return_tensors="""pt"""					).to("""cuda"""					)

							with torch.no_grad():
														__a		:		Optional[Any]									=       hf_model(**_lowerCamelCase					)
							__a		:		Any									=       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		:		Tuple									=       processor(
														    images=np.array(_lowerCamelCase					)  ,       input_points=_lowerCamelCase  ,       input_labels=_lowerCamelCase  ,       return_tensors="""pt"""					).to("""cuda"""					)

														with torch.no_grad():
																					__a		:		int									=       hf_model(**_lowerCamelCase					)
														__a		:		Any									=       output.iou_scores.squeeze()

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

														__a		:		Union[str, Any]									=       ((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

														__a		:		Optional[int]									=       processor(images=np.array(_lowerCamelCase					)  ,       input_boxes=_lowerCamelCase  ,       return_tensors="""pt"""					).to("""cuda"""					)

														with torch.no_grad():
																					__a		:		Optional[int]									=       hf_model(**_lowerCamelCase					)
														__a		:		Optional[int]									=       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		:		int									=       [[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
														__a		:		str									=       [[1, 1]]

														__a		:		Dict									=       processor(
														    images=np.array(_lowerCamelCase					)  ,       input_points=_lowerCamelCase  ,       input_labels=_lowerCamelCase  ,       return_tensors="""pt"""					).to("""cuda"""					)

														with torch.no_grad():
																					__a		:		Optional[int]									=       hf_model(**_lowerCamelCase					)
														__a		:		int									=       output.iou_scores.squeeze()

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


if __name__ == "__main__":
	lowercase__			   =  argparse.ArgumentParser()
	lowercase__			   =  ["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",
	)

	lowercase__			   =  parser.parse_args()

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






"""simple docstring"""

import os





def       __magic_name__    (   _lowerCamelCase				: Dict					):
							__a		:		List[str]									=       len(grid[0]					)
							__a		:		int									=       len(_lowerCamelCase					)

							__a		:		Tuple									=       0
							__a		:		List[Any]									=       0
							__a		:		List[str]									=       0

							# Check vertically, horizontally, diagonally at the same time (only works
							# for nxn grid)
							for i in range(_lowerCamelCase					):
														for j in range(n_rows - 3					):
																					__a		:		List[Any]									=       grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
																					__a		:		Tuple									=       grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

																					# Left-to-right diagonal (\) product
																					if i < n_columns - 3:
																												__a		:		List[Any]									=       (
																												    grid[i][j]
																												    * grid[i + 1][j + 1]
																												    * grid[i + 2][j + 2]
																												    * grid[i + 3][j + 3]
																												)

																					# Right-to-left diagonal(/) product
																					if i > 2:
																												__a		:		List[Any]									=       (
																												    grid[i][j]
																												    * grid[i - 1][j + 1]
																												    * grid[i - 2][j + 2]
																												    * grid[i - 3][j + 3]
																												)

																					__a		:		str									=       max(
																					    _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																					if max_product > largest:
																												__a		:		Optional[Any]									=       max_product

							return largest





def       __magic_name__    (   ):
							__a		:		Tuple									=       []
							with open(os.path.dirname(_lowerCamelCase					) + """/grid.txt"""					) as file:
														for line in file:
																					grid.append(line.strip("""\n"""					).split(""" """					)					)

							__a		:		Tuple									=       [[int(_lowerCamelCase					) for i in grid[j]] for j in range(len(_lowerCamelCase					)					)]

							return largest_product(_lowerCamelCase					)


if __name__ == "__main__":
	print(solution())

