'''simple docstring'''




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   :	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 _lowercase	(      lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


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

       __UpperCAmelCase  :				Union[str, Any]       				=		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  :				str       				=		key.replace(lowerCamelCase__							,    lowerCamelCase__						)

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

              __UpperCAmelCase  :				Optional[Any]       				=		value

       __UpperCAmelCase  :				str       				=		model_state_dict[
           "prompt_encoder.shared_embedding.positional_embedding"
       ]

       return model_state_dict

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__="ybelkada/segment-anything"						)					->       List[str]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		hf_hub_download(lowerCamelCase__							,    f"""checkpoints/{model_name}.pth"""						)

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

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

              __UpperCAmelCase  :				Any       				=		SamConfig(
                  vision_config=lowerCamelCase__							,    )

       __UpperCAmelCase  :				Union[str, Any]       				=		torch.load(lowerCamelCase__							,    map_location="cpu"						)
       __UpperCAmelCase  :				int       				=		replace_keys(lowerCamelCase__						)

       __UpperCAmelCase  :				int       				=		SamImageProcessor()

       __UpperCAmelCase  :				Any       				=		SamProcessor(image_processor=lowerCamelCase__						)
       __UpperCAmelCase  :				str       				=		SamModel(lowerCamelCase__						)

       hf_model.load_state_dict(lowerCamelCase__						)
       __UpperCAmelCase  :				List[str]       				=		hf_model.to("cuda"						)

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

       __UpperCAmelCase  :				List[str]       				=		[[[400, 650]]]
       __UpperCAmelCase  :				Any       				=		[[1]]

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

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

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

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

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

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

              __UpperCAmelCase  :				Any       				=		((75, 275, 1725, 850),)

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

              with torch.no_grad():
                     __UpperCAmelCase  :				Dict       				=		hf_model(**lowerCamelCase__						)
              __UpperCAmelCase  :				Tuple       				=		output.iou_scores.squeeze()

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

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

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

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

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


if __name__ == "__main__":
   _a   :	List[Any] 							=   argparse.ArgumentParser()
   _a   :	List[str] 							=   ["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   :	Dict 							=   parser.parse_args()

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







'''simple docstring'''




def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       List[str]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		(boundary[1] - boundary[0]) / steps
       __UpperCAmelCase  :				Tuple       				=		boundary[0]
       __UpperCAmelCase  :				List[str]       				=		boundary[1]
       __UpperCAmelCase  :				List[Any]       				=		make_points(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)
       __UpperCAmelCase  :				int       				=		0.0
       y += (h / 2.0) * f(lowerCamelCase__						)
       for i in x_i:
              # print(i)
              y += h * f(lowerCamelCase__						)
       y += (h / 2.0) * f(lowerCamelCase__						)
       return y

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		a + h
       while x < (b - h):
              yield x
              __UpperCAmelCase  :				List[str]       				=		x + h

def _lowercase	(      lowerCamelCase__						)					->       Optional[Any]:  # enter your function here





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		(x - 0) * (x - 0)
       return y

def _lowercase	(      )					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Tuple       				=		0.0  # Lower bound of integration
       __UpperCAmelCase  :				Union[str, Any]       				=		1.0  # Upper bound of integration
       __UpperCAmelCase  :				Union[str, Any]       				=		10.0  # define number of steps or resolution
       __UpperCAmelCase  :				Dict       				=		[a, b]  # define boundary of integration
       __UpperCAmelCase  :				Optional[int]       				=		method_a(lowerCamelCase__							,    lowerCamelCase__						)
       print(f"""y = {y}"""						)


if __name__ == "__main__":
   main()






