


'''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,
)


a_  :				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 __snake_case					(      UpperCAmelCase_						:						Any		):
 lowerCamelCase_      =  {}
 state_dict.pop("pixel_mean"					,				UpperCAmelCase_		)
 state_dict.pop("pixel_std"					,				UpperCAmelCase_		)

 lowerCamelCase_      =  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_      =  key.replace(UpperCAmelCase_					,				UpperCAmelCase_		)

  if re.match(UpperCAmelCase_					,				UpperCAmelCase_		):
   lowerCamelCase_      =  int(re.match(UpperCAmelCase_					,				UpperCAmelCase_		).group(2		)		)
   if layer_nb == 0:
    lowerCamelCase_      =  key.replace("layers.0"					,				"proj_in"		)
   elif layer_nb == 1:
    lowerCamelCase_      =  key.replace("layers.1"					,				"layers.0"		)
   elif layer_nb == 2:
    lowerCamelCase_      =  key.replace("layers.2"					,				"proj_out"		)

  lowerCamelCase_      =  value

 lowerCamelCase_      =  model_state_dict[
     "prompt_encoder.shared_embedding.positional_embedding"
 ]

 return model_state_dict





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Dict="ybelkada/segment-anything"		):
 lowerCamelCase_      =  hf_hub_download(UpperCAmelCase_					,				F'''checkpoints/{model_name}.pth'''		)

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

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

  lowerCamelCase_      =  SamConfig(
      vision_config=UpperCAmelCase_					,				)

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

 lowerCamelCase_      =  SamImageProcessor()

 lowerCamelCase_      =  SamProcessor(image_processor=UpperCAmelCase_		)
 lowerCamelCase_      =  SamModel(UpperCAmelCase_		)

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

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

 lowerCamelCase_      =  [[[400, 650]]]
 lowerCamelCase_      =  [[1]]

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

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

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

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

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

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
     a_  :				int						= argparse.ArgumentParser()
     a_  :				List[str]						= ["""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_  :				Optional[Any]						= parser.parse_args()

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







'''simple docstring'''

a_  :				Any						= """0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
     from .utils import rich



