
"""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 	lowerCamelCase__			(							UpperCAmelCase_				)->							str:







       """simple docstring"""
       UpperCamelCase					 =		{}
       state_dict.pop("pixel_mean"		,      UpperCAmelCase_				)
       state_dict.pop("pixel_std"		,      UpperCAmelCase_				)

       UpperCamelCase					 =		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					 =		key.replace(UpperCAmelCase_		,      UpperCAmelCase_				)

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

              UpperCamelCase					 =		value

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

       return model_state_dict



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_="ybelkada/segment-anything"				)->							List[str]:







       """simple docstring"""
       UpperCamelCase					 =		hf_hub_download(UpperCAmelCase_		,      F"checkpoints/{model_name}.pth"				)

       if "sam_vit_b" in model_name:
              UpperCamelCase					 =		SamConfig()
       elif "sam_vit_l" in model_name:
              UpperCamelCase					 =		SamVisionConfig(
                  hidden_size=10_24		,      num_hidden_layers=24		,      num_attention_heads=16		,      global_attn_indexes=[5, 11, 17, 23]		,      )

              UpperCamelCase					 =		SamConfig(
                  vision_config=UpperCAmelCase_		,      )
       elif "sam_vit_h" in model_name:
              UpperCamelCase					 =		SamVisionConfig(
                  hidden_size=12_80		,      num_hidden_layers=32		,      num_attention_heads=16		,      global_attn_indexes=[7, 15, 23, 31]		,      )

              UpperCamelCase					 =		SamConfig(
                  vision_config=UpperCAmelCase_		,      )

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

       UpperCamelCase					 =		SamImageProcessor()

       UpperCamelCase					 =		SamProcessor(image_processor=UpperCAmelCase_				)
       UpperCamelCase					 =		SamModel(UpperCAmelCase_				)

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

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

       UpperCamelCase					 =		[[[4_00, 6_50]]]
       UpperCamelCase					 =		[[1]]

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

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

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

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

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

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

              UpperCamelCase					 =		((75, 2_75, 17_25, 8_50),)

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

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

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

              # Test with 2 points and 1 image.
              UpperCamelCase					 =		[[[4_00, 6_50], [8_00, 6_50]]]
              UpperCamelCase					 =		[[1, 1]]

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

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

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


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"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment



@require_huggingface_suite
class   __a  (      unittest.TestCase	):






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		inspect.getfile(accelerate.test_utils			)
              UpperCamelCase					 =		os.path.sep.join(
                  mod_file.split(os.path.sep			)[:-1] + ["scripts", "external_deps", "test_metrics.py"]			)

              from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

              UpperCamelCase					 =		test_metrics






       @require_cpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Union[str, Any]:







              """simple docstring"""
              debug_launcher(self.test_metrics.main       ,      num_processes=1			)






       @require_cpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  List[str]:







              """simple docstring"""
              debug_launcher(self.test_metrics.main			)






       @require_single_gpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  str:







              """simple docstring"""
              self.test_metrics.main()






       @require_multi_gpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Optional[Any]:







              """simple docstring"""
              print(f"Found {torch.cuda.device_count()} devices."			)
              UpperCamelCase					 =		["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
              with patch_environment(omp_num_threads=1			):
                     execute_subprocess_async(UpperCAmelCase_       ,      env=os.environ.copy()			)





