


"""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,
)


UpperCamelCase						:				List[Any]             =      {
    "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  __snake_case       (   UpperCamelCase__	)      ->		List[Any]:







		"""simple docstring"""





		A					       = {}
		state_dict.pop('pixel_mean' ,			UpperCamelCase__	)
		state_dict.pop('pixel_std' ,			UpperCamelCase__	)

		A					       = 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					       = key.replace(UpperCamelCase__ ,			UpperCamelCase__	)

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

				A					       = value

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

		return model_state_dict







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__="ybelkada/segment-anything"	)      ->		Tuple:







		"""simple docstring"""





		A					       = hf_hub_download(UpperCamelCase__ ,			f'checkpoints/{model_name}.pth'	)

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

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

				A					       = SamConfig(
				    vision_config=UpperCamelCase__ ,			)

		A					       = torch.load(UpperCamelCase__ ,			map_location='cpu'	)
		A					       = replace_keys(UpperCamelCase__	)

		A					       = SamImageProcessor()

		A					       = SamProcessor(image_processor=UpperCamelCase__	)
		A					       = SamModel(UpperCamelCase__	)

		hf_model.load_state_dict(UpperCamelCase__	)
		A					       = hf_model.to('cuda'	)

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

		A					       = [[[400, 650]]]
		A					       = [[1]]

		A					       = processor(images=np.array(UpperCamelCase__	) ,			return_tensors='pt'	).to('cuda'	)

		with torch.no_grad():
				A					       = hf_model(**UpperCamelCase__	)
		A					       = output.iou_scores.squeeze()

		if model_name == "sam_vit_h_4b8939":
				assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

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

				with torch.no_grad():
						A					       = hf_model(**UpperCamelCase__	)
				A					       = output.iou_scores.squeeze()

				assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

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

				A					       = processor(images=np.array(UpperCamelCase__	) ,			input_boxes=UpperCamelCase__ ,			return_tensors='pt'	).to('cuda'	)

				with torch.no_grad():
						A					       = hf_model(**UpperCamelCase__	)
				A					       = output.iou_scores.squeeze()

				assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

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

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

				with torch.no_grad():
						A					       = hf_model(**UpperCamelCase__	)
				A					       = output.iou_scores.squeeze()

				assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
	UpperCamelCase						:				List[Any]             =      argparse.ArgumentParser()
	UpperCamelCase						:				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",
	)

	UpperCamelCase						:				Optional[int]             =      parser.parse_args()

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









"""simple docstring"""


from __future__ import annotations







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		if not nums:
				return 0
		A					       = nums[0]
		A					       = 0
		for num in nums[1:]:
				A       , A					       = (
				    max_excluding + num,
				    max(UpperCamelCase__ ,			UpperCamelCase__	),
				)
		return max(UpperCamelCase__ ,			UpperCamelCase__	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





