"""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,
)


__lowerCAmelCase    :       Dict					     ={
    """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__       (				lowerCAmelCase__     :Dict		) ->    str:
      '''simple docstring'''


      lowercase														=	{}
      state_dict.pop("""pixel_mean"""					,		lowerCAmelCase__		)
      state_dict.pop("""pixel_std"""					,		lowerCAmelCase__		)

      lowercase														=	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:
                        lowercase														=	key.replace(lowerCAmelCase__					,		lowerCAmelCase__		)

            if re.match(lowerCAmelCase__					,		lowerCAmelCase__		):
                  lowercase														=	int(re.match(lowerCAmelCase__					,		lowerCAmelCase__		).group(2		)		)
                  if layer_nb == 0:
                        lowercase														=	key.replace("""layers.0"""					,		"""proj_in"""		)
                  elif layer_nb == 1:
                        lowercase														=	key.replace("""layers.1"""					,		"""layers.0"""		)
                  elif layer_nb == 2:
                        lowercase														=	key.replace("""layers.2"""					,		"""proj_out"""		)

            lowercase														=	value

      lowercase														=	model_state_dict[
          """prompt_encoder.shared_embedding.positional_embedding"""
      ]

      return model_state_dict


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :Optional[int]					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :List[Any]="ybelkada/segment-anything"		) ->    Optional[int]:
      '''simple docstring'''


      lowercase														=	hf_hub_download(lowerCAmelCase__					,		f'checkpoints/{model_name}.pth'		)

      if "sam_vit_b" in model_name:
            lowercase														=	SamConfig()
      elif "sam_vit_l" in model_name:
            lowercase														=	SamVisionConfig(
                hidden_size=1_0_2_4					,		num_hidden_layers=2_4					,		num_attention_heads=1_6					,		global_attn_indexes=[5, 1_1, 1_7, 2_3]					,		)

            lowercase														=	SamConfig(
                vision_config=lowerCAmelCase__					,		)
      elif "sam_vit_h" in model_name:
            lowercase														=	SamVisionConfig(
                hidden_size=1_2_8_0					,		num_hidden_layers=3_2					,		num_attention_heads=1_6					,		global_attn_indexes=[7, 1_5, 2_3, 3_1]					,		)

            lowercase														=	SamConfig(
                vision_config=lowerCAmelCase__					,		)

      lowercase														=	torch.load(lowerCAmelCase__					,		map_location="""cpu"""		)
      lowercase														=	replace_keys(lowerCAmelCase__		)

      lowercase														=	SamImageProcessor()

      lowercase														=	SamProcessor(image_processor=lowerCAmelCase__		)
      lowercase														=	SamModel(lowerCAmelCase__		)

      hf_model.load_state_dict(lowerCAmelCase__		)
      lowercase														=	hf_model.to("""cuda"""		)

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

      lowercase														=	[[[4_0_0, 6_5_0]]]
      lowercase														=	[[1]]

      lowercase														=	processor(images=np.array(lowerCAmelCase__		)					,		return_tensors="""pt"""		).to("""cuda"""		)

      with torch.no_grad():
            lowercase														=	hf_model(**lowerCAmelCase__		)
      lowercase														=	output.iou_scores.squeeze()

      if model_name == "sam_vit_h_4b8939":
            assert scores[-1].item() == 0.579_890_251_159_668

            lowercase														=	processor(
                images=np.array(lowerCAmelCase__		)					,		input_points=lowerCAmelCase__					,		input_labels=lowerCAmelCase__					,		return_tensors="""pt"""		).to("""cuda"""		)

            with torch.no_grad():
                  lowercase														=	hf_model(**lowerCAmelCase__		)
            lowercase														=	output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9_712_603_092_193_604

            lowercase														=	((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

            lowercase														=	processor(images=np.array(lowerCAmelCase__		)					,		input_boxes=lowerCAmelCase__					,		return_tensors="""pt"""		).to("""cuda"""		)

            with torch.no_grad():
                  lowercase														=	hf_model(**lowerCAmelCase__		)
            lowercase														=	output.iou_scores.squeeze()

            assert scores[-1].item() == 0.8_686_015_605_926_514

            # Test with 2 points and 1 image.
            lowercase														=	[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
            lowercase														=	[[1, 1]]

            lowercase														=	processor(
                images=np.array(lowerCAmelCase__		)					,		input_points=lowerCAmelCase__					,		input_labels=lowerCAmelCase__					,		return_tensors="""pt"""		).to("""cuda"""		)

            with torch.no_grad():
                  lowercase														=	hf_model(**lowerCAmelCase__		)
            lowercase														=	output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
       __lowerCAmelCase    :       Union[str, Any]					     =argparse.ArgumentParser()
       __lowerCAmelCase    :       Optional[Any]					     =["""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""",
       )

       __lowerCAmelCase    :       List[str]					     =parser.parse_args()

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




"""simple docstring"""






from ..utils import DummyObject, requires_backends





class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   List[str]							  =	['onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""onnx"""]						)



