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,
)


__magic_name__								={
    "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    (						A							):
     UpperCamelCase__										=      {}
     state_dict.pop('''pixel_mean''' ,							__snake_case							)
     state_dict.pop('''pixel_std''' ,							__snake_case							)

     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(__snake_case ,							__snake_case							)

          if re.match(__snake_case ,							__snake_case							):
               UpperCamelCase__										=      int(re.match(__snake_case ,							__snake_case							).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  __UpperCamelCase    (						A ,							A ,							A ,							A="ybelkada/segment-anything"							):
     UpperCamelCase__										=      hf_hub_download(__snake_case ,							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=1024 ,							num_hidden_layers=24 ,							num_attention_heads=16 ,							global_attn_indexes=[5, 11, 17, 23] ,							)

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

          UpperCamelCase__										=      SamConfig(
              vision_config=__snake_case ,							)

     UpperCamelCase__										=      torch.load(__snake_case ,							map_location='''cpu'''							)
     UpperCamelCase__										=      replace_keys(__snake_case							)

     UpperCamelCase__										=      SamImageProcessor()

     UpperCamelCase__										=      SamProcessor(image_processor=__snake_case							)
     UpperCamelCase__										=      SamModel(__snake_case							)

     hf_model.load_state_dict(__snake_case							)
     UpperCamelCase__										=      hf_model.to('''cuda'''							)

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

     UpperCamelCase__										=      [[[400, 650]]]
     UpperCamelCase__										=      [[1]]

     UpperCamelCase__										=      processor(images=np.array(__snake_case							) ,							return_tensors='''pt'''							).to('''cuda'''							)

     with torch.no_grad():
          UpperCamelCase__										=      hf_model(**__snake_case							)
     UpperCamelCase__										=      output.iou_scores.squeeze()

     if model_name == "sam_vit_h_4b8939":
          assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

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

          with torch.no_grad():
               UpperCamelCase__										=      hf_model(**__snake_case							)
          UpperCamelCase__										=      output.iou_scores.squeeze()

          assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

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

          UpperCamelCase__										=      processor(images=np.array(__snake_case							) ,							input_boxes=__snake_case ,							return_tensors='''pt'''							).to('''cuda'''							)

          with torch.no_grad():
               UpperCamelCase__										=      hf_model(**__snake_case							)
          UpperCamelCase__										=      output.iou_scores.squeeze()

          assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

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

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

          with torch.no_grad():
               UpperCamelCase__										=      hf_model(**__snake_case							)
          UpperCamelCase__										=      output.iou_scores.squeeze()

          assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
     __magic_name__								=argparse.ArgumentParser()
     __magic_name__								=["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''',
     )

     __magic_name__								=parser.parse_args()

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




def  __UpperCamelCase    (						A = 600851475143							):

     try:
          UpperCamelCase__										=      int(A							)
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''							)
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''							)
     UpperCamelCase__										=      1
     UpperCamelCase__										=      2
     while i * i <= n:
          while n % i == 0:
               UpperCamelCase__										=      i
               n //= i
          i += 1
     if n > 1:
          UpperCamelCase__										=      n
     return int(A							)


if __name__ == "__main__":
     print(f"""{solution() = }""")



