



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 		lowerCAmelCase__     (       UpperCamelCase_					:							Any						)->	int:
    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 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Any			,							UpperCamelCase_					:							Union[str, Any]="ybelkada/segment-anything"						)->	Optional[int]:
    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=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__					=       SamConfig(
            vision_config=UpperCamelCase_			,							)
    elif "sam_vit_h" in model_name:
        A__					=       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__					=       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__					=       [[[4_0_0, 6_5_0]]]
    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() == 0.579890251159668

        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() == 0.9712603092193604

        A__					=       ((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

        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() == 0.8686015605926514

        # Test with 2 points and 1 image.
        A__					=       [[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
        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() == 0.9936047792434692


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)





def 		lowerCAmelCase__     (       UpperCamelCase_					:							int						)->	int:
    A__					=       1
    for i in range(1			,							num + 1						):
        fact *= i
    return fact



def 		lowerCAmelCase__     (       UpperCamelCase_					:							int						)->	int:
    A__					=       0
    while number > 0:
        A__					=       number % 1_0
        sum_of_digits += last_digit
        A__					=       number // 1_0  # Removing the last_digit from the given number
    return sum_of_digits



def 		lowerCAmelCase__     (       UpperCamelCase_					:							int = 1_0_0						)->	int:
    A__					=       factorial(UpperCamelCase_						)
    A__					=       split_and_add(UpperCamelCase_						)
    return result


if __name__ == "__main__":
   print(solution(int(input("Enter the Number: ").strip())))
