





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       : Union[str, 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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Union[str, Any]:
    lowerCamelCase__					:   int      				=	{}
    state_dict.pop('pixel_mean'		,			_UpperCAmelCase				)
    state_dict.pop('pixel_std'		,			_UpperCAmelCase				)

    lowerCamelCase__					:   int      				=	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:
                lowerCamelCase__					:   int      				=	key.replace(_UpperCAmelCase		,			_UpperCAmelCase				)

        if re.match(_UpperCAmelCase		,			_UpperCAmelCase				):
            lowerCamelCase__					:   Optional[int]      				=	int(re.match(_UpperCAmelCase		,			_UpperCAmelCase				).group(2				)				)
            if layer_nb == 0:
                lowerCamelCase__					:   Any      				=	key.replace('layers.0'		,			'proj_in'				)
            elif layer_nb == 1:
                lowerCamelCase__					:   Any      				=	key.replace('layers.1'		,			'layers.0'				)
            elif layer_nb == 2:
                lowerCamelCase__					:   Dict      				=	key.replace('layers.2'		,			'proj_out'				)

        lowerCamelCase__					:   List[Any]      				=	value

    lowerCamelCase__					:   Tuple      				=	model_state_dict[
        'prompt_encoder.shared_embedding.positional_embedding'
    ]

    return model_state_dict


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

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

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

        lowerCamelCase__					:   Optional[Any]      				=	SamConfig(
            vision_config=_UpperCAmelCase		,			)

    lowerCamelCase__					:   List[Any]      				=	torch.load(_UpperCAmelCase		,			map_location='cpu'				)
    lowerCamelCase__					:   str      				=	replace_keys(_UpperCAmelCase				)

    lowerCamelCase__					:   List[str]      				=	SamImageProcessor()

    lowerCamelCase__					:   Dict      				=	SamProcessor(image_processor=_UpperCAmelCase				)
    lowerCamelCase__					:   str      				=	SamModel(_UpperCAmelCase				)

    hf_model.load_state_dict(_UpperCAmelCase				)
    lowerCamelCase__					:   str      				=	hf_model.to('cuda'				)

    lowerCamelCase__					:   str      				=	'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
    lowerCamelCase__					:   Optional[int]      				=	Image.open(requests.get(_UpperCAmelCase		,			stream=_UpperCAmelCase				).raw				).convert('RGB'				)

    lowerCamelCase__					:   Any      				=	[[[400, 650]]]
    lowerCamelCase__					:   Any      				=	[[1]]

    lowerCamelCase__					:   str      				=	processor(images=np.array(_UpperCAmelCase				)		,			return_tensors='pt'				).to('cuda'				)

    with torch.no_grad():
        lowerCamelCase__					:   int      				=	hf_model(**_UpperCAmelCase				)
    lowerCamelCase__					:   Optional[Any]      				=	output.iou_scores.squeeze()

    if model_name == "sam_vit_h_4b8939":
        assert scores[-1].item() == 0.579_890_251_159_668

        lowerCamelCase__					:   Tuple      				=	processor(
            images=np.array(_UpperCAmelCase				)		,			input_points=_UpperCAmelCase		,			input_labels=_UpperCAmelCase		,			return_tensors='pt'				).to('cuda'				)

        with torch.no_grad():
            lowerCamelCase__					:   Union[str, Any]      				=	hf_model(**_UpperCAmelCase				)
        lowerCamelCase__					:   str      				=	output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9_712_603_092_193_604

        lowerCamelCase__					:   List[str]      				=	((75, 275, 1725, 850),)

        lowerCamelCase__					:   Any      				=	processor(images=np.array(_UpperCAmelCase				)		,			input_boxes=_UpperCAmelCase		,			return_tensors='pt'				).to('cuda'				)

        with torch.no_grad():
            lowerCamelCase__					:   Tuple      				=	hf_model(**_UpperCAmelCase				)
        lowerCamelCase__					:   List[str]      				=	output.iou_scores.squeeze()

        assert scores[-1].item() == 0.8_686_015_605_926_514

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

        lowerCamelCase__					:   int      				=	processor(
            images=np.array(_UpperCAmelCase				)		,			input_points=_UpperCAmelCase		,			input_labels=_UpperCAmelCase		,			return_tensors='pt'				).to('cuda'				)

        with torch.no_grad():
            lowerCamelCase__					:   Union[str, Any]      				=	hf_model(**_UpperCAmelCase				)
        lowerCamelCase__					:   Optional[Any]      				=	output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
    _UpperCAmelCase       : Dict			     =       argparse.ArgumentParser()
    _UpperCAmelCase       : Dict			     =       ["""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       : int			     =       parser.parse_args()

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












import socket


def  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    lowerCamelCase__					:   int      				=	socket.socket(socket.AF_INET		,			socket.SOCK_STREAM				)
    lowerCamelCase__					:   Optional[int]      				=	socket.gethostname()
    lowerCamelCase__					:   Optional[int]      				=	1_2312

    sock.connect((host, port)				)
    sock.send(b'Hello server!'				)

    with open('Received_file'		,			'wb'				) as out_file:
        print('File opened'				)
        print('Receiving data...'				)
        while True:
            lowerCamelCase__					:   str      				=	sock.recv(1024				)
            if not data:
                break
            out_file.write(_UpperCAmelCase				)

    print('Successfully received the file'				)
    sock.close()
    print('Connection closed'				)


if __name__ == "__main__":
    main()





