






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       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[Any]     )    ->   List[Any]:





    """simple docstring"""

    __A       =						{}
    state_dict.pop("""pixel_mean"""      ,					A__     )
    state_dict.pop("""pixel_std"""      ,					A__     )

    __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(A__      ,					A__     )

        if re.match(A__      ,					A__     ):
            __A       =						int(re.match(A__      ,					A__     ).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 :      Optional[Any]      ,					__lowercase :      List[str]      ,					__lowercase :      Optional[int]      ,					__lowercase :      Dict="ybelkada/segment-anything"     )    ->   Optional[int]:





    """simple docstring"""

    __A       =						hf_hub_download(A__      ,					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_0_2_4      ,					num_hidden_layers=2_4      ,					num_attention_heads=1_6      ,					global_attn_indexes=[5, 1_1, 1_7, 2_3]      ,					)

        __A       =						SamConfig(
            vision_config=A__      ,					)
    elif "sam_vit_h" in model_name:
        __A       =						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]      ,					)

        __A       =						SamConfig(
            vision_config=A__      ,					)

    __A       =						torch.load(A__      ,					map_location="""cpu"""     )
    __A       =						replace_keys(A__     )

    __A       =						SamImageProcessor()

    __A       =						SamProcessor(image_processor=A__     )
    __A       =						SamModel(A__     )

    hf_model.load_state_dict(A__     )
    __A       =						hf_model.to("""cuda"""     )

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

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

    __A       =						processor(images=np.array(A__     )      ,					return_tensors="""pt"""     ).to("""cuda"""     )

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

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

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

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

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

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

        __A       =						processor(images=np.array(A__     )      ,					input_boxes=A__      ,					return_tensors="""pt"""     ).to("""cuda"""     )

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

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

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

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

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

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


if __name__ == "__main__":
  __a   :							str						= argparse.ArgumentParser()
  __a   :							Dict						= ['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   :							Any						= parser.parse_args()

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








snake_case__				:	List[Any]					       =					[
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

snake_case__				:	Tuple					       =					[
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

snake_case__				:	List[Any]					       =					[
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

snake_case__				:	List[str]					       =					[
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

snake_case__				:	Any					       =					[
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

snake_case__				:	int					       =					[
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

snake_case__				:	Any					       =					[
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

snake_case__				:	Union[str, Any]					       =					[
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]





