

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_	:			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 						UpperCamelCase (lowercase_:			List[str] )					->				str:
	A__							:   Union[str, Any]					       =		{}
	state_dict.pop("""pixel_mean"""   ,				lowercase_ )
	state_dict.pop("""pixel_std"""   ,				lowercase_ )

	A__							:   List[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__							:   Union[str, Any]					       =		key.replace(lowercase_   ,				lowercase_ )

		if re.match(lowercase_   ,				lowercase_ ):
			A__							:   List[Any]					       =		int(re.match(lowercase_   ,				lowercase_ ).group(2 ) )
			if layer_nb == 0:
				A__							:   List[str]					       =		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__							:   Any					       =		value

	A__							:   Union[str, Any]					       =		model_state_dict[
	    """prompt_encoder.shared_embedding.positional_embedding"""
	]

	return model_state_dict

def 						UpperCamelCase (lowercase_:			Union[str, Any]   ,				lowercase_:			int   ,				lowercase_:			Any   ,				lowercase_:			Optional[int]="ybelkada/segment-anything" )					->				Dict:
	A__							:   List[Any]					       =		hf_hub_download(lowercase_   ,				f"""checkpoints/{model_name}.pth""" )

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

		A__							:   Union[str, Any]					       =		SamConfig(
		    vision_config=lowercase_   ,				)
	elif "sam_vit_h" in model_name:
		A__							:   Union[str, Any]					       =		SamVisionConfig(
		    hidden_size=1280   ,				num_hidden_layers=32   ,				num_attention_heads=16   ,				global_attn_indexes=[7, 15, 23, 31]   ,				)

		A__							:   Any					       =		SamConfig(
		    vision_config=lowercase_   ,				)

	A__							:   Dict					       =		torch.load(lowercase_   ,				map_location="""cpu""" )
	A__							:   Dict					       =		replace_keys(lowercase_ )

	A__							:   Any					       =		SamImageProcessor()

	A__							:   Any					       =		SamProcessor(image_processor=lowercase_ )
	A__							:   int					       =		SamModel(lowercase_ )

	hf_model.load_state_dict(lowercase_ )
	A__							:   Optional[int]					       =		hf_model.to("""cuda""" )

	A__							:   Union[str, Any]					       =		"""https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
	A__							:   int					       =		Image.open(requests.get(lowercase_   ,				stream=lowercase_ ).raw ).convert("""RGB""" )

	A__							:   Union[str, Any]					       =		[[[400, 650]]]
	A__							:   int					       =		[[1]]

	A__							:   Dict					       =		processor(images=np.array(lowercase_ )   ,				return_tensors="""pt""" ).to("""cuda""" )

	with torch.no_grad():
		A__							:   int					       =		hf_model(**lowercase_ )
	A__							:   Dict					       =		output.iou_scores.squeeze()

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

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

		with torch.no_grad():
			A__							:   Union[str, Any]					       =		hf_model(**lowercase_ )
		A__							:   int					       =		output.iou_scores.squeeze()

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

		A__							:   Tuple					       =		((75, 275, 1725, 850),)

		A__							:   Any					       =		processor(images=np.array(lowercase_ )   ,				input_boxes=lowercase_   ,				return_tensors="""pt""" ).to("""cuda""" )

		with torch.no_grad():
			A__							:   int					       =		hf_model(**lowercase_ )
		A__							:   List[Any]					       =		output.iou_scores.squeeze()

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

		# Test with 2 points and 1 image.
		A__							:   Dict					       =		[[[400, 650], [800, 650]]]
		A__							:   str					       =		[[1, 1]]

		A__							:   Tuple					       =		processor(
		    images=np.array(lowercase_ )   ,				input_points=lowercase_   ,				input_labels=lowercase_   ,				return_tensors="""pt""" ).to("""cuda""" )

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

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


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

		A_	:			Dict  	=      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

def 						UpperCamelCase (lowercase_:			float   ,				lowercase_:			float   ,				lowercase_:			float )					->				dict[str, float]:
	if (voltage, current, resistance).count(0 ) != 1:
		raise ValueError("""One and only one argument must be 0""" )
	if resistance < 0:
		raise ValueError("""Resistance cannot be negative""" )
	if voltage == 0:
		return {"voltage": float(current * resistance )}
	elif current == 0:
		return {"current": voltage / resistance}
	elif resistance == 0:
		return {"resistance": voltage / current}
	else:
		raise ValueError("""Exactly one argument must be 0""" )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



