



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,
)


__snake_case     :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_	(      lowerCAmelCase__	:	Union[str, Any]				)       ->							Tuple:

							'''simple docstring'''






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

							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(lowerCAmelCase__			,	lowerCAmelCase__				)

														if re.match(lowerCAmelCase__			,	lowerCAmelCase__				):
																					A      						=    int(re.match(lowerCAmelCase__			,	lowerCAmelCase__				).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  lowerCamelCase_	(      lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	Dict="ybelkada/segment-anything"				)       ->							Dict:

							'''simple docstring'''






							A      						=    hf_hub_download(lowerCAmelCase__			,	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=lowerCAmelCase__			,	)
							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=lowerCAmelCase__			,	)

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

							A      						=    SamImageProcessor()

							A      						=    SamProcessor(image_processor=lowerCAmelCase__				)
							A      						=    SamModel(lowerCAmelCase__				)

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

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

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

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

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

							if model_name == "sam_vit_h_4b8939":
														assert scores[-1].item() == 0.579890251159668

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

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

														assert scores[-1].item() == 0.9712603092193604

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

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

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

														assert scores[-1].item() == 0.8686015605926514

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

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

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

														assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
				__snake_case     :int			       =argparse.ArgumentParser()
				__snake_case     :str			       =['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',
				)

				__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)




import re






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							if len(re.findall('[ATCG]'			,	lowerCAmelCase__				)				) != len(lowerCAmelCase__				):
														raise ValueError('Invalid Strand'				)

							return dna.translate(dna.maketrans('ATCG'			,	'TAGC'				)				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()