



"""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      =		{
    """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__   ):
      UpperCAmelCase_    				=							{}
      state_dict.pop("pixel_mean"			,    lowerCAmelCase__   )
      state_dict.pop("pixel_std"			,    lowerCAmelCase__   )

      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(lowerCAmelCase__			,    lowerCAmelCase__   )

            if re.match(lowerCAmelCase__			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							int(re.match(lowerCAmelCase__			,    lowerCAmelCase__   ).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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__="ybelkada/segment-anything"   ):
      UpperCAmelCase_    				=							hf_hub_download(lowerCAmelCase__			,    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=lowerCAmelCase__			,    )
      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=lowerCAmelCase__			,    )

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

      UpperCAmelCase_    				=							SamImageProcessor()

      UpperCAmelCase_    				=							SamProcessor(image_processor=lowerCAmelCase__   )
      UpperCAmelCase_    				=							SamModel(lowerCAmelCase__   )

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

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

      UpperCAmelCase_    				=							[[[400, 650]]]
      UpperCAmelCase_    				=							[[1]]

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

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

      if model_name == "sam_vit_h_4b8939":
            assert scores[-1].item() == 0.579890251159668

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

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

            assert scores[-1].item() == 0.9712603092193604

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

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

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

            assert scores[-1].item() == 0.8686015605926514

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

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

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

            assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
  lowerCamelCase      =		argparse.ArgumentParser()
  lowerCamelCase      =		["""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      =		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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''mobilenet_v1'''



       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						int=3	,       _UpperCAmelCase    :						Union[str, Any]=224	,       _UpperCAmelCase    :						Any=1.0	,       _UpperCAmelCase    :						Any=8	,       _UpperCAmelCase    :						List[Any]="relu6"	,       _UpperCAmelCase    :						List[str]=True	,       _UpperCAmelCase    :						Dict=0.999	,       _UpperCAmelCase    :						List[Any]=0.02	,       _UpperCAmelCase    :						List[Any]=0.001	,       **_UpperCAmelCase    :						str	,       )				->		Optional[int]:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)

             if depth_multiplier <= 0:
                   raise ValueError("depth_multiplier must be greater than zero."					)

             UpperCAmelCase_    				=							num_channels
             UpperCAmelCase_    				=							image_size
             UpperCAmelCase_    				=							depth_multiplier
             UpperCAmelCase_    				=							min_depth
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							tf_padding
             UpperCAmelCase_    				=							classifier_dropout_prob
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							layer_norm_eps



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  version.parse('''1.11'''   )



       @property
       def   lowercase__       (							self    :						List[Any]					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             return OrderedDict([("pixel_values", {0: "batch"})]					)



       @property
       def   lowercase__       (							self    :						List[Any]					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             if self.task == "image-classification":
                   return OrderedDict([("logits", {0: "batch"})]					)
             else:
                   return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]					)







       @property
       def   lowercase__       (							self    :						Tuple					)				->		float:


             '''simple docstring'''




             return 1e-4




