




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	:		List[str]							       =   {
    """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 SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[Any]:



       """simple docstring"""

       A__       =    {}
       state_dict.pop('''pixel_mean''' ,				lowercase_						)
       state_dict.pop('''pixel_std''' ,				lowercase_						)

       A__       =    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:
                            A__       =    key.replace(lowercase_ ,				lowercase_						)

              if re.match(lowercase_ ,				lowercase_						):
                     A__       =    int(re.match(lowercase_ ,				lowercase_						).group(2						)						)
                     if layer_nb == 0:
                            A__       =    key.replace('''layers.0''' ,				'''proj_in'''						)
                     elif layer_nb == 1:
                            A__       =    key.replace('''layers.1''' ,				'''layers.0'''						)
                     elif layer_nb == 2:
                            A__       =    key.replace('''layers.2''' ,				'''proj_out'''						)

              A__       =    value

       A__       =    model_state_dict[
           '''prompt_encoder.shared_embedding.positional_embedding'''
       ]

       return model_state_dict







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_="ybelkada/segment-anything"						)   ->       Union[str, Any]:



       """simple docstring"""

       A__       =    hf_hub_download(lowercase_ ,				f"""checkpoints/{model_name}.pth"""						)

       if "sam_vit_b" in model_name:
              A__       =    SamConfig()
       elif "sam_vit_l" in model_name:
              A__       =    SamVisionConfig(
                  hidden_size=1_024 ,				num_hidden_layers=24 ,				num_attention_heads=16 ,				global_attn_indexes=[5, 11, 17, 23] ,				)

              A__       =    SamConfig(
                  vision_config=lowercase_ ,				)
       elif "sam_vit_h" in model_name:
              A__       =    SamVisionConfig(
                  hidden_size=1_280 ,				num_hidden_layers=32 ,				num_attention_heads=16 ,				global_attn_indexes=[7, 15, 23, 31] ,				)

              A__       =    SamConfig(
                  vision_config=lowercase_ ,				)

       A__       =    torch.load(lowercase_ ,				map_location='''cpu'''						)
       A__       =    replace_keys(lowercase_						)

       A__       =    SamImageProcessor()

       A__       =    SamProcessor(image_processor=lowercase_						)
       A__       =    SamModel(lowercase_						)

       hf_model.load_state_dict(lowercase_						)
       A__       =    hf_model.to('''cuda'''						)

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

       A__       =    [[[400, 650]]]
       A__       =    [[1]]

       A__       =    processor(images=np.array(lowercase_						) ,				return_tensors='''pt'''						).to('''cuda'''						)

       with torch.no_grad():
              A__       =    hf_model(**lowercase_						)
       A__       =    output.iou_scores.squeeze()

       if model_name == "sam_vit_h_4b8939":
              assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

              A__       =    processor(
                  images=np.array(lowercase_						) ,				input_points=lowercase_ ,				input_labels=lowercase_ ,				return_tensors='''pt'''						).to('''cuda'''						)

              with torch.no_grad():
                     A__       =    hf_model(**lowercase_						)
              A__       =    output.iou_scores.squeeze()

              assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

              A__       =    ((75, 275, 1_725, 850),)

              A__       =    processor(images=np.array(lowercase_						) ,				input_boxes=lowercase_ ,				return_tensors='''pt'''						).to('''cuda'''						)

              with torch.no_grad():
                     A__       =    hf_model(**lowercase_						)
              A__       =    output.iou_scores.squeeze()

              assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

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

              A__       =    processor(
                  images=np.array(lowercase_						) ,				input_points=lowercase_ ,				input_labels=lowercase_ ,				return_tensors='''pt'''						).to('''cuda'''						)

              with torch.no_grad():
                     A__       =    hf_model(**lowercase_						)
              A__       =    output.iou_scores.squeeze()

              assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
  _lowerCamelCase	:		Any							       =   argparse.ArgumentParser()
  _lowerCamelCase	:		Union[str, 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	:		Optional[int]							       =   parser.parse_args()

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








import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase	:		Tuple							       =   logging.get_logger(__name__)

_lowerCamelCase	:		str							       =   {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''git_vision_model'''


    def __init__(			self      :	Any					, UpperCAmelCase__      :	Any=768					, UpperCAmelCase__      :	int=3_072					, UpperCAmelCase__      :	List[str]=12					, UpperCAmelCase__      :	Dict=12					, UpperCAmelCase__      :	Optional[int]=3					, UpperCAmelCase__      :	List[Any]=224					, UpperCAmelCase__      :	Union[str, Any]=16					, UpperCAmelCase__      :	Union[str, Any]="quick_gelu"					, UpperCAmelCase__      :	Dict=1e-5					, UpperCAmelCase__      :	Union[str, Any]=0.0					, UpperCAmelCase__      :	Any=0.02					, **UpperCAmelCase__      :	Any					, )						->Optional[int]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           A__       =    hidden_size
           A__       =    intermediate_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    num_channels
           A__       =    patch_size
           A__       =    image_size
           A__       =    initializer_range
           A__       =    attention_dropout
           A__       =    layer_norm_eps
           A__       =    hidden_act





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Any					, UpperCAmelCase__      :	Union[str, os.PathLike]					, **UpperCAmelCase__      :	int)						->"PretrainedConfig":





           '''simple docstring'''

           cls._set_token_in_kwargs(UpperCAmelCase__)

           A__ ,							A__       =    cls.get_config_dict(UpperCAmelCase__					, **UpperCAmelCase__)

           # get the vision config dict if we are loading from GITConfig
           if config_dict.get('''model_type''') == "git":
                  A__       =    config_dict['''vision_config''']

           if "model_type" in config_dict and hasattr(cls					, '''model_type''') and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                      f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

           return cls.from_dict(UpperCAmelCase__					, **UpperCAmelCase__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''git'''


    def __init__(			self      :	Dict					, UpperCAmelCase__      :	Dict=None					, UpperCAmelCase__      :	int=30_522					, UpperCAmelCase__      :	Optional[int]=768					, UpperCAmelCase__      :	Dict=6					, UpperCAmelCase__      :	int=12					, UpperCAmelCase__      :	List[str]=3_072					, UpperCAmelCase__      :	str="gelu"					, UpperCAmelCase__      :	int=0.1					, UpperCAmelCase__      :	Union[str, Any]=0.1					, UpperCAmelCase__      :	List[Any]=1_024					, UpperCAmelCase__      :	List[str]=0.02					, UpperCAmelCase__      :	Any=1e-12					, UpperCAmelCase__      :	Union[str, Any]=0					, UpperCAmelCase__      :	List[Any]="absolute"					, UpperCAmelCase__      :	int=True					, UpperCAmelCase__      :	Any=False					, UpperCAmelCase__      :	int=101					, UpperCAmelCase__      :	Tuple=102					, UpperCAmelCase__      :	Dict=None					, **UpperCAmelCase__      :	List[str]					, )						->Any:





           '''simple docstring'''

           super().__init__(bos_token_id=UpperCAmelCase__					, eos_token_id=UpperCAmelCase__					, pad_token_id=UpperCAmelCase__					, **UpperCAmelCase__)

           if vision_config is None:
                  A__       =    {}
                  logger.info('''vision_config is None. initializing the GitVisionConfig with default values.''')

           A__       =    GitVisionConfig(**UpperCAmelCase__)
           A__       =    vocab_size
           A__       =    hidden_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    hidden_act
           A__       =    intermediate_size
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    max_position_embeddings
           A__       =    initializer_range
           A__       =    layer_norm_eps
           A__       =    position_embedding_type
           A__       =    use_cache
           A__       =    tie_word_embeddings
           A__       =    num_image_with_embedding

           A__       =    bos_token_id
           A__       =    eos_token_id





    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[Any]:





           '''simple docstring'''

           A__       =    copy.deepcopy(self.__dict__)
           A__       =    self.vision_config.to_dict()
           A__       =    self.__class__.model_type
           return output


