


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[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	(			__lowerCAmelCase				)    ->				List[Any]:
  snake_case__						= {}
  state_dict.pop('''pixel_mean'''							,   __lowerCAmelCase				)
  state_dict.pop('''pixel_std'''							,   __lowerCAmelCase				)

  snake_case__						= 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:
        snake_case__						= key.replace(__lowerCAmelCase							,   __lowerCAmelCase				)

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

    snake_case__						= value

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

  return model_state_dict







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase="ybelkada/segment-anything"				)    ->				Tuple:
  snake_case__						= hf_hub_download(__lowerCAmelCase							,   F"""checkpoints/{model_name}.pth"""				)

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

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

    snake_case__						= SamConfig(
        vision_config=__lowerCAmelCase							,   )

  snake_case__						= torch.load(__lowerCAmelCase							,   map_location='''cpu'''				)
  snake_case__						= replace_keys(__lowerCAmelCase				)

  snake_case__						= SamImageProcessor()

  snake_case__						= SamProcessor(image_processor=__lowerCAmelCase				)
  snake_case__						= SamModel(__lowerCAmelCase				)

  hf_model.load_state_dict(__lowerCAmelCase				)
  snake_case__						= hf_model.to('''cuda'''				)

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

  snake_case__						= [[[400, 650]]]
  snake_case__						= [[1]]

  snake_case__						= processor(images=np.array(__lowerCAmelCase				)							,   return_tensors='''pt'''				).to('''cuda'''				)

  with torch.no_grad():
    snake_case__						= hf_model(**__lowerCAmelCase				)
  snake_case__						= output.iou_scores.squeeze()

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

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

    with torch.no_grad():
      snake_case__						= hf_model(**__lowerCAmelCase				)
    snake_case__						= output.iou_scores.squeeze()

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

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

    snake_case__						= processor(images=np.array(__lowerCAmelCase				)							,   input_boxes=__lowerCAmelCase							,   return_tensors='''pt'''				).to('''cuda'''				)

    with torch.no_grad():
      snake_case__						= hf_model(**__lowerCAmelCase				)
    snake_case__						= output.iou_scores.squeeze()

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

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

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

    with torch.no_grad():
      snake_case__						= hf_model(**__lowerCAmelCase				)
    snake_case__						= output.iou_scores.squeeze()

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


if __name__ == "__main__":
   lowerCamelCase__							:						Tuple							     =   argparse.ArgumentParser()
   lowerCamelCase__							:						int							     =   ["""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__							:						Union[str, 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'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def       _lowercase							(					):
 __A	:		Dict	  =      ArgumentParser('Accelerate CLI tool',					usage='accelerate <command> [<args>]',					allow_abbrev=UpperCamelCase__      )
 __A	:		Any	  =      parser.add_subparsers(help='accelerate command helpers'      )

 # Register commands
 get_config_parser(subparsers=UpperCamelCase__      )
 env_command_parser(subparsers=UpperCamelCase__      )
 launch_command_parser(subparsers=UpperCamelCase__      )
 tpu_command_parser(subparsers=UpperCamelCase__      )
 test_command_parser(subparsers=UpperCamelCase__      )

 # Let's go
 __A	:		Optional[Any]	  =      parser.parse_args()

 if not hasattr(UpperCamelCase__,					'func'      ):
  parser.print_help()
  exit(1      )

 # Run
 args.func(UpperCamelCase__      )


if __name__ == "__main__":
 main()






