


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,
)


snake_case__				:	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    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    List[Any]:
   lowerCamelCase_						:   List[Any]		= {}
   state_dict.pop("""pixel_mean"""						,						A__					)
   state_dict.pop("""pixel_std"""						,						A__					)

   lowerCamelCase_						:   int		= 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:
            lowerCamelCase_						:   List[Any]		= key.replace(A__						,						A__					)

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

      lowerCamelCase_						:   Any		= value

   lowerCamelCase_						:   Optional[int]		= model_state_dict[
       """prompt_encoder.shared_embedding.positional_embedding"""
   ]

   return model_state_dict


def    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: List[str]						,						A__						: Optional[int]						,						A__						: Dict="ybelkada/segment-anything"					)     ->    Optional[int]:
   lowerCamelCase_						:   Any		= hf_hub_download(A__						,						f'''checkpoints/{model_name}.pth'''					)

   if "sam_vit_b" in model_name:
      lowerCamelCase_						:   Tuple		= SamConfig()
   elif "sam_vit_l" in model_name:
      lowerCamelCase_						:   Optional[int]		= SamVisionConfig(
          hidden_size=1024						,						num_hidden_layers=24						,						num_attention_heads=16						,						global_attn_indexes=[5, 11, 17, 23]						,						)

      lowerCamelCase_						:   Any		= SamConfig(
          vision_config=A__						,						)
   elif "sam_vit_h" in model_name:
      lowerCamelCase_						:   Tuple		= SamVisionConfig(
          hidden_size=1280						,						num_hidden_layers=32						,						num_attention_heads=16						,						global_attn_indexes=[7, 15, 23, 31]						,						)

      lowerCamelCase_						:   Tuple		= SamConfig(
          vision_config=A__						,						)

   lowerCamelCase_						:   Dict		= torch.load(A__						,						map_location="""cpu"""					)
   lowerCamelCase_						:   Union[str, Any]		= replace_keys(A__					)

   lowerCamelCase_						:   Dict		= SamImageProcessor()

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

   hf_model.load_state_dict(A__					)
   lowerCamelCase_						:   Any		= hf_model.to("""cuda"""					)

   lowerCamelCase_						:   Optional[Any]		= """https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
   lowerCamelCase_						:   Union[str, Any]		= Image.open(requests.get(A__						,						stream=A__					).raw					).convert("""RGB"""					)

   lowerCamelCase_						:   Optional[int]		= [[[400, 650]]]
   lowerCamelCase_						:   List[str]		= [[1]]

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

   with torch.no_grad():
      lowerCamelCase_						:   Optional[Any]		= hf_model(**A__					)
   lowerCamelCase_						:   Any		= output.iou_scores.squeeze()

   if model_name == "sam_vit_h_4b8939":
      assert scores[-1].item() == 0.579_8902_5115_9668

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

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

      assert scores[-1].item() == 0.9712_6030_9219_3604

      lowerCamelCase_						:   Tuple		= ((75, 275, 1725, 850),)

      lowerCamelCase_						:   List[str]		= processor(images=np.array(A__					)						,						input_boxes=A__						,						return_tensors="""pt"""					).to("""cuda"""					)

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

      assert scores[-1].item() == 0.8686_0156_0592_6514

      # Test with 2 points and 1 image.
      lowerCamelCase_						:   Tuple		= [[[400, 650], [800, 650]]]
      lowerCamelCase_						:   Optional[Any]		= [[1, 1]]

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

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

      assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
      snake_case__				:	str					       =					argparse.ArgumentParser()
      snake_case__				:	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',
      )

      snake_case__				:	Any					       =					parser.parse_args()

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









def    __lowerCamelCase   (      A__						: float						,						A__						: float						,						A__						: float						,						A__						: float						,						A__						: float						,						)     ->    float:
   lowerCamelCase_						:   List[str]		= [redshift, radiation_density, matter_density, dark_energy]
   if any(p < 0 for p in parameters					):
      raise ValueError("""All input parameters must be positive"""					)

   if any(p > 1 for p in parameters[1:4]					):
      raise ValueError("""Relative densities cannot be greater than one"""					)
   else:
      lowerCamelCase_						:   Union[str, Any]		= 1 - (matter_density + radiation_density + dark_energy)

      lowerCamelCase_						:   int		= (
          radiation_density * (redshift + 1) ** 4
          + matter_density * (redshift + 1) ** 3
          + curvature * (redshift + 1) ** 2
          + dark_energy
      )

      lowerCamelCase_						:   Dict		= hubble_constant * e_a ** (1 / 2)
      return hubble


if __name__ == "__main__":
      import doctest

      # run doctest
      doctest.testmod()

      # demo LCDM approximation
      snake_case__				:	str					       =					0.3

      print(
          hubble_parameter(
              hubble_constant=6_8.3,
              radiation_density=1e-4,
              matter_density=matter_density,
              dark_energy=1 - matter_density,
              redshift=0,
          )
      )





