




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_   		=     {
    "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   (  lowercase_      )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						{}
    state_dict.pop('''pixel_mean'''					,     lowercase_      )
    state_dict.pop('''pixel_std'''					,     lowercase_      )

    __UpperCamelCase  		=						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:
                __UpperCamelCase  		=						key.replace(lowercase_					,     lowercase_      )

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

        __UpperCamelCase  		=						value

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

    return model_state_dict




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_="ybelkada/segment-anything"      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						hf_hub_download(lowercase_					,     F"checkpoints/{model_name}.pth"      )

    if "sam_vit_b" in model_name:
        __UpperCamelCase  		=						SamConfig()
    elif "sam_vit_l" in model_name:
        __UpperCamelCase  		=						SamVisionConfig(
            hidden_size=10_24					,     num_hidden_layers=24					,     num_attention_heads=16					,     global_attn_indexes=[5, 11, 17, 23]					,     )

        __UpperCamelCase  		=						SamConfig(
            vision_config=lowercase_					,     )
    elif "sam_vit_h" in model_name:
        __UpperCamelCase  		=						SamVisionConfig(
            hidden_size=12_80					,     num_hidden_layers=32					,     num_attention_heads=16					,     global_attn_indexes=[7, 15, 23, 31]					,     )

        __UpperCamelCase  		=						SamConfig(
            vision_config=lowercase_					,     )

    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    __UpperCamelCase  		=						replace_keys(lowercase_      )

    __UpperCamelCase  		=						SamImageProcessor()

    __UpperCamelCase  		=						SamProcessor(image_processor=lowercase_      )
    __UpperCamelCase  		=						SamModel(lowercase_      )

    hf_model.load_state_dict(lowercase_      )
    __UpperCamelCase  		=						hf_model.to('''cuda'''      )

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

    __UpperCamelCase  		=						[[[4_00, 6_50]]]
    __UpperCamelCase  		=						[[1]]

    __UpperCamelCase  		=						processor(images=np.array(lowercase_      )					,     return_tensors='''pt'''      ).to('''cuda'''      )

    with torch.no_grad():
        __UpperCamelCase  		=						hf_model(**lowercase_      )
    __UpperCamelCase  		=						output.iou_scores.squeeze()

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

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

        with torch.no_grad():
            __UpperCamelCase  		=						hf_model(**lowercase_      )
        __UpperCamelCase  		=						output.iou_scores.squeeze()

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

        __UpperCamelCase  		=						((75, 2_75, 17_25, 8_50),)

        __UpperCamelCase  		=						processor(images=np.array(lowercase_      )					,     input_boxes=lowercase_					,     return_tensors='''pt'''      ).to('''cuda'''      )

        with torch.no_grad():
            __UpperCamelCase  		=						hf_model(**lowercase_      )
        __UpperCamelCase  		=						output.iou_scores.squeeze()

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

        # Test with 2 points and 1 image.
        __UpperCamelCase  		=						[[[4_00, 6_50], [8_00, 6_50]]]
        __UpperCamelCase  		=						[[1, 1]]

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

        with torch.no_grad():
            __UpperCamelCase  		=						hf_model(**lowercase_      )
        __UpperCamelCase  		=						output.iou_scores.squeeze()

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


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 a_   		=     ["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_   		=     parser.parse_args()

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












import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
 print("Googling.....")
 a_   		=     "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
 a_   		=     requests.get(url, headers={"UserAgent": UserAgent().random})
 # res.raise_for_status()
 with open("project1a.html", "wb") as out_file:  # only for knowing the class
  for data in res.iter_content(1_0000):
   out_file.write(data)
 a_   		=     BeautifulSoup(res.text, "html.parser")
 a_   		=     list(soup.select(".eZt8xd"))[:5]

 print(len(links))
 for link in links:
  if link.text == "Maps":
   webbrowser.open(link.get("href"))
  else:
   webbrowser.open(f"""https://google.com{link.get('href')}""")






