





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      lowerCAmelCase_ (       __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Union[str, Any]			  ={}
  state_dict.pop("pixel_mean"	,    __a		)
  state_dict.pop("pixel_std"	,    __a		)

  lowerCamelCase__:      str			  =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(__a	,    __a		)

    if re.match(__a	,    __a		):
      lowerCamelCase__:      Optional[Any]			  =int(re.match(__a	,    __a		).group(2		)		)
      if layer_nb == 0:
        lowerCamelCase__:      List[str]			  =key.replace("layers.0"	,    "proj_in"		)
      elif layer_nb == 1:
        lowerCamelCase__:      List[str]			  =key.replace("layers.1"	,    "layers.0"		)
      elif layer_nb == 2:
        lowerCamelCase__:      List[str]			  =key.replace("layers.2"	,    "proj_out"		)

    lowerCamelCase__:      str			  =value

  lowerCamelCase__:      Dict			  =model_state_dict[
      "prompt_encoder.shared_embedding.positional_embedding"
  ]

  return model_state_dict





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a="ybelkada/segment-anything"		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      str			  =hf_hub_download(__a	,    F"""checkpoints/{model_name}.pth"""		)

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

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

    lowerCamelCase__:      Dict			  =SamConfig(
        vision_config=__a	,    )

  lowerCamelCase__:      int			  =torch.load(__a	,    map_location="cpu"		)
  lowerCamelCase__:      int			  =replace_keys(__a		)

  lowerCamelCase__:      Dict			  =SamImageProcessor()

  lowerCamelCase__:      Union[str, Any]			  =SamProcessor(image_processor=__a		)
  lowerCamelCase__:      Tuple			  =SamModel(__a		)

  hf_model.load_state_dict(__a		)
  lowerCamelCase__:      Any			  =hf_model.to("cuda"		)

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

  lowerCamelCase__:      List[str]			  =[[[400, 650]]]
  lowerCamelCase__:      Union[str, Any]			  =[[1]]

  lowerCamelCase__:      Tuple			  =processor(images=np.array(__a		)	,    return_tensors="pt"		).to("cuda"		)

  with torch.no_grad():
    lowerCamelCase__:      Any			  =hf_model(**__a		)
  lowerCamelCase__:      Tuple			  =output.iou_scores.squeeze()

  if model_name == "sam_vit_h_4b8939":
    assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

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

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

    assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

    lowerCamelCase__:      Tuple			  =((75, 275, 1725, 850),)

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

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

    assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

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

    lowerCamelCase__:      Any			  =processor(
        images=np.array(__a		)	,    input_points=__a	,    input_labels=__a	,    return_tensors="pt"		).to("cuda"		)

    with torch.no_grad():
      lowerCamelCase__:      str			  =hf_model(**__a		)
    lowerCamelCase__:      str			  =output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


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 itertools
import math





def      lowerCAmelCase_ (       __a		)     ->		bool:







  """simple docstring"""





  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5	,    int(math.sqrt(__a		) + 1		)	,    6		):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True





def      lowerCAmelCase_ (       )     ->		str:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =2
  while True:
    if is_prime(__a		):
      yield num
    num += 1





def      lowerCAmelCase_ (       __a = 10001		)     ->		int:







  """simple docstring"""





  return next(itertools.islice(prime_generator()	,    nth - 1	,    __a		)		)


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

