'''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,
)


SCREAMING_SNAKE_CASE_	   =			{
    '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__					(					_lowercase    :				int     )							->	int:
  __UpperCAmelCase:					Union[str, Any]		= {}
  state_dict.pop("""pixel_mean"""   ,    lowerCamelCase_     )
  state_dict.pop("""pixel_std"""   ,    lowerCamelCase_     )

  __UpperCAmelCase:					str		= 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(lowerCamelCase_   ,    lowerCamelCase_     )

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

    __UpperCAmelCase:					Any		= value

  __UpperCAmelCase:					int		= model_state_dict[
      'prompt_encoder.shared_embedding.positional_embedding'
  ]

  return model_state_dict






def  UpperCamelCase__					(					_lowercase    :				Tuple   ,    _lowercase    :				List[str]   ,    _lowercase    :				int   ,    _lowercase    :				Optional[Any]="ybelkada/segment-anything"     )							->	Any:
  __UpperCAmelCase:					Dict		= hf_hub_download(lowerCamelCase_   ,    F'''checkpoints/{model_name}.pth'''     )

  if "sam_vit_b" in model_name:
    __UpperCAmelCase:					Tuple		= SamConfig()
  elif "sam_vit_l" in model_name:
    __UpperCAmelCase:					Union[str, Any]		= 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]   ,    )

    __UpperCAmelCase:					Tuple		= SamConfig(
        vision_config=lowerCamelCase_   ,    )
  elif "sam_vit_h" in model_name:
    __UpperCAmelCase:					Optional[Any]		= 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]   ,    )

    __UpperCAmelCase:					str		= SamConfig(
        vision_config=lowerCamelCase_   ,    )

  __UpperCAmelCase:					Optional[int]		= torch.load(lowerCamelCase_   ,    map_location="""cpu"""     )
  __UpperCAmelCase:					Union[str, Any]		= replace_keys(lowerCamelCase_     )

  __UpperCAmelCase:					int		= SamImageProcessor()

  __UpperCAmelCase:					Dict		= SamProcessor(image_processor=lowerCamelCase_     )
  __UpperCAmelCase:					Union[str, Any]		= SamModel(lowerCamelCase_     )

  hf_model.load_state_dict(lowerCamelCase_     )
  __UpperCAmelCase:					Dict		= hf_model.to("""cuda"""     )

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

  __UpperCAmelCase:					Dict		= [[[4_0_0, 6_5_0]]]
  __UpperCAmelCase:					List[str]		= [[1]]

  __UpperCAmelCase:					Any		= processor(images=np.array(lowerCamelCase_     )   ,    return_tensors="""pt"""     ).to("""cuda"""     )

  with torch.no_grad():
    __UpperCAmelCase:					Tuple		= hf_model(**lowerCamelCase_     )
  __UpperCAmelCase:					str		= output.iou_scores.squeeze()

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

    __UpperCAmelCase:					Union[str, Any]		= processor(
        images=np.array(lowerCamelCase_     )   ,    input_points=lowerCamelCase_   ,    input_labels=lowerCamelCase_   ,    return_tensors="""pt"""     ).to("""cuda"""     )

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

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

    __UpperCAmelCase:					Tuple		= ((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

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

    with torch.no_grad():
      __UpperCAmelCase:					Tuple		= hf_model(**lowerCamelCase_     )
    __UpperCAmelCase:					Union[str, Any]		= output.iou_scores.squeeze()

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

    # Test with 2 points and 1 image.
    __UpperCAmelCase:					str		= [[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
    __UpperCAmelCase:					Optional[int]		= [[1, 1]]

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

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

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    SCREAMING_SNAKE_CASE_	   =			['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',
    )

    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()

    convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				list[int]     )							->	None:
  __UpperCAmelCase:					Dict		= len(_lowercase     )
  print("""The following activities are selected:"""     )

  # The first activity is always selected
  __UpperCAmelCase:					Dict		= 0
  print(_lowercase   ,    end=""","""     )

  # Consider rest of the activities
  for j in range(_lowercase     ):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(_lowercase   ,    end=""","""     )
      __UpperCAmelCase:					Optional[int]		= j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			[1, 3, 0, 5, 8, 5]
    SCREAMING_SNAKE_CASE_	   =			[2, 4, 6, 7, 9, 9]
    print_max_activities(start, finish)