
"""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__      :	Tuple          =    {
    "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 							a__ (				lowerCAmelCase	:       Tuple		):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[Any] 							=						{}
     state_dict.pop("pixel_mean"       ,    __snake_case		)
     state_dict.pop("pixel_std"       ,    __snake_case		)

     UpperCAmelCase__							: int 							=						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__							: int 							=						key.replace(__snake_case       ,    __snake_case		)

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

          UpperCAmelCase__							: Any 							=						value

     UpperCAmelCase__							: List[Any] 							=						model_state_dict[
         "prompt_encoder.shared_embedding.positional_embedding"
     ]

     return model_state_dict



def 							a__ (				lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       List[Any]="ybelkada/segment-anything"		):






     '''simple docstring'''
     UpperCAmelCase__							: int 							=						hf_hub_download(__snake_case       ,    F"checkpoints/{model_name}.pth"		)

     if "sam_vit_b" in model_name:
          UpperCAmelCase__							: List[Any] 							=						SamConfig()
     elif "sam_vit_l" in model_name:
          UpperCAmelCase__							: Any 							=						SamVisionConfig(
              hidden_size=1024       ,    num_hidden_layers=24       ,    num_attention_heads=16       ,    global_attn_indexes=[5, 11, 17, 23]       ,    )

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

          UpperCAmelCase__							: Optional[Any] 							=						SamConfig(
              vision_config=__snake_case       ,    )

     UpperCAmelCase__							: Optional[Any] 							=						torch.load(__snake_case       ,    map_location="cpu"		)
     UpperCAmelCase__							: List[str] 							=						replace_keys(__snake_case		)

     UpperCAmelCase__							: List[str] 							=						SamImageProcessor()

     UpperCAmelCase__							: Any 							=						SamProcessor(image_processor=__snake_case		)
     UpperCAmelCase__							: Dict 							=						SamModel(__snake_case		)

     hf_model.load_state_dict(__snake_case		)
     UpperCAmelCase__							: Optional[Any] 							=						hf_model.to("cuda"		)

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

     UpperCAmelCase__							: Tuple 							=						[[[400, 650]]]
     UpperCAmelCase__							: Tuple 							=						[[1]]

     UpperCAmelCase__							: str 							=						processor(images=np.array(__snake_case		)       ,    return_tensors="pt"		).to("cuda"		)

     with torch.no_grad():
          UpperCAmelCase__							: Tuple 							=						hf_model(**__snake_case		)
     UpperCAmelCase__							: Optional[Any] 							=						output.iou_scores.squeeze()

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

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

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

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

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

          UpperCAmelCase__							: str 							=						processor(images=np.array(__snake_case		)       ,    input_boxes=__snake_case       ,    return_tensors="pt"		).to("cuda"		)

          with torch.no_grad():
               UpperCAmelCase__							: Optional[int] 							=						hf_model(**__snake_case		)
          UpperCAmelCase__							: List[str] 							=						output.iou_scores.squeeze()

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

          # Test with 2 points and 1 image.
          UpperCAmelCase__							: Dict 							=						[[[400, 650], [800, 650]]]
          UpperCAmelCase__							: Optional[Any] 							=						[[1, 1]]

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

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

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


if __name__ == "__main__":
    A__      :	int          =    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__      :	List[Any]          =    parser.parse_args()

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





"""simple docstring"""




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


A__      :	Optional[int]          =    ["""small""", """medium""", """large"""]

A__      :	Optional[int]          =    """lm_head.decoder.weight"""
A__      :	Dict          =    """lm_head.weight"""



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						torch.load(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						d.pop(lowerCAmelCase		)
     os.makedirs(lowerCAmelCase       ,    exist_ok=lowerCAmelCase		)
     torch.save(lowerCAmelCase       ,    os.path.join(lowerCAmelCase       ,    lowerCAmelCase		)		)


if __name__ == "__main__":
    A__      :	List[Any]          =    argparse.ArgumentParser()
    parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
    A__      :	Tuple          =    parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        A__      :	Tuple          =    os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
        A__      :	str          =    f"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )



