


"""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,
)


__lowercase    =				{
    """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 					lowercase      (    A_	)->				List[str]:
  '''simple docstring'''







  a						:		List[str]											=      {}
  state_dict.pop("pixel_mean"  ,       A_	)
  state_dict.pop("pixel_std"  ,       A_	)

  a						:		Optional[Any]											=      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:
        a						:		Any											=      key.replace(A_  ,       A_	)

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

    a						:		List[str]											=      value

  a						:		int											=      model_state_dict[
      "prompt_encoder.shared_embedding.positional_embedding"
  ]

  return model_state_dict


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_="ybelkada/segment-anything"	)->				List[Any]:
  '''simple docstring'''







  a						:		int											=      hf_hub_download(A_  ,       F'''checkpoints/{model_name}.pth'''	)

  if "sam_vit_b" in model_name:
    a						:		Optional[Any]											=      SamConfig()
  elif "sam_vit_l" in model_name:
    a						:		List[Any]											=      SamVisionConfig(
        hidden_size=1_024  ,       num_hidden_layers=24  ,       num_attention_heads=16  ,       global_attn_indexes=[5, 11, 17, 23]  ,       )

    a						:		Tuple											=      SamConfig(
        vision_config=A_  ,       )
  elif "sam_vit_h" in model_name:
    a						:		Any											=      SamVisionConfig(
        hidden_size=1_280  ,       num_hidden_layers=32  ,       num_attention_heads=16  ,       global_attn_indexes=[7, 15, 23, 31]  ,       )

    a						:		Dict											=      SamConfig(
        vision_config=A_  ,       )

  a						:		Any											=      torch.load(A_  ,       map_location="cpu"	)
  a						:		Optional[Any]											=      replace_keys(A_	)

  a						:		Optional[Any]											=      SamImageProcessor()

  a						:		Tuple											=      SamProcessor(image_processor=A_	)
  a						:		Union[str, Any]											=      SamModel(A_	)

  hf_model.load_state_dict(A_	)
  a						:		Optional[int]											=      hf_model.to("cuda"	)

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

  a						:		Dict											=      [[[400, 650]]]
  a						:		Dict											=      [[1]]

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

  with torch.no_grad():
    a						:		Tuple											=      hf_model(**A_	)
  a						:		Dict											=      output.iou_scores.squeeze()

  if model_name == "sam_vit_h_4b8939":
    assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

    a						:		Tuple											=      processor(
        images=np.array(A_	)  ,       input_points=A_  ,       input_labels=A_  ,       return_tensors="pt"	).to("cuda"	)

    with torch.no_grad():
      a						:		Union[str, Any]											=      hf_model(**A_	)
    a						:		Any											=      output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

    a						:		str											=      ((75, 275, 1_725, 850),)

    a						:		Optional[int]											=      processor(images=np.array(A_	)  ,       input_boxes=A_  ,       return_tensors="pt"	).to("cuda"	)

    with torch.no_grad():
      a						:		Optional[int]											=      hf_model(**A_	)
    a						:		Dict											=      output.iou_scores.squeeze()

    assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

    # Test with 2 points and 1 image.
    a						:		List[str]											=      [[[400, 650], [800, 650]]]
    a						:		Tuple											=      [[1, 1]]

    a						:		Optional[Any]											=      processor(
        images=np.array(A_	)  ,       input_points=A_  ,       input_labels=A_  ,       return_tensors="pt"	).to("cuda"	)

    with torch.no_grad():
      a						:		Union[str, Any]											=      hf_model(**A_	)
    a						:		Union[str, Any]											=      output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


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

 __lowercase    =				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 json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment


def 					lowercase      (    )->				Union[str, Any]:
  '''simple docstring'''







  a						:		Union[str, Any]											=      torch.nn.Linear(2  ,       4	)
  a						:		Tuple											=      torch.optim.AdamW(model.parameters()  ,       lr=1.0	)
  a						:		Union[str, Any]											=      torch.optim.lr_scheduler.OneCycleLR(A_  ,       max_lr=0.0_1  ,       steps_per_epoch=2  ,       epochs=1	)
  a						:		List[str]											=      DataLoader(TensorDataset(torch.tensor([1, 2, 3]	)	)	)
  a						:		int											=      DataLoader(TensorDataset(torch.tensor([4, 5, 6]	)	)	)

  return model, optimizer, scheduler, train_dl, valid_dl


def 					lowercase      (    A_	)->				List[Any]:
  '''simple docstring'''







  return (model.weight.abs().sum() + model.bias.abs().sum()).item()


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		Optional[int]											=      torch.nn.Linear(*tuple(model.weight.T.shape	)	).state_dict()
  model.load_state_dict(A_	)







class 						_A  (  _a  ):







    """simple docstring"""




    @require_cuda
    def      __snake_case							( self    :   Any):
      a						:		List[str]											=      Accelerator()
      assert PartialState._shared_state["_cpu"] is False
      assert PartialState._shared_state["device"].type == "cuda"
      with self.assertRaises(__UpperCAmelCase):
        a						:		Any											=      Accelerator(cpu=__UpperCAmelCase)






    def      __snake_case							( self    :   List[Any]):
      a						:		str											=      Accelerator()
      a						:		Optional[Any]											=      GradientState()
      assert state.num_steps == 1
      a						:		Dict											=      4
      assert state.num_steps == 4

      assert state.sync_gradients is True
      a						:		Optional[int]											=      False
      assert state.sync_gradients is False
      GradientState._reset_state()






    def      __snake_case							( self    :   str):
      a						:		int											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Tuple											=      create_components()

      (
          (
          a
      )							,		(
          a
      )							,		(
          a
      )							,		(
          a
      )							,		(
          a
      )							,		
      )						:		Tuple											=      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      self.assertTrue(prepared_model in accelerator._models)
      self.assertTrue(prepared_optimizer in accelerator._optimizers)
      self.assertTrue(prepared_scheduler in accelerator._schedulers)
      self.assertTrue(prepared_train_dl in accelerator._dataloaders)
      self.assertTrue(prepared_valid_dl in accelerator._dataloaders)






    def      __snake_case							( self    :   Dict):
      a						:		Dict											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Any											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      accelerator.free_memory()

      self.assertTrue(len(accelerator._models) == 0)
      self.assertTrue(len(accelerator._optimizers) == 0)
      self.assertTrue(len(accelerator._schedulers) == 0)
      self.assertTrue(len(accelerator._dataloaders) == 0)






    def      __snake_case							( self    :   int):
      PartialState._reset_state()

      # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
      def noop(*__UpperCAmelCase    :   Union[str, Any]    ,			**__UpperCAmelCase    :   Any):
        pass

      with patch("torch.cuda.set_device"    ,			__UpperCAmelCase), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"):
        a						:		int											=      Accelerator()
        self.assertEqual(str(accelerator.state.device)    ,			"cuda:64")






    def      __snake_case							( self    :   List[str]):
      a						:		Tuple											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Optional[Any]											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Dict											=      get_signature(__UpperCAmelCase)

      with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(__UpperCAmelCase)

        # make sure random weights don't match
        load_random_weights(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)

        # make sure loaded weights match
        accelerator.load_state(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)






    def      __snake_case							( self    :   Optional[int]):
      a						:		str											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Dict											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Union[str, Any]											=      get_signature(__UpperCAmelCase)

      # saving hook
      def save_config(__UpperCAmelCase    :   Tuple    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Union[str, Any]):
        a						:		Tuple											=      {"class_name": models[0].__class__.__name__}

        with open(os.path.join(__UpperCAmelCase    ,			"data.json")    ,			"w") as f:
          json.dump(__UpperCAmelCase    ,			__UpperCAmelCase)

        # loading hook
      def load_config(__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Union[str, Any]):
        with open(os.path.join(__UpperCAmelCase    ,			"data.json")    ,			"r") as f:
          a						:		Optional[Any]											=      json.load(__UpperCAmelCase)

        a						:		Tuple											=      config["class_name"]

      a						:		Optional[int]											=      accelerator.register_save_state_pre_hook(__UpperCAmelCase)
      a						:		Tuple											=      accelerator.register_load_state_pre_hook(__UpperCAmelCase)

      with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(__UpperCAmelCase)

        # make sure random weights don't match with hooks
        load_random_weights(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)

        # random class name to verify correct one is loaded
        a						:		int											=      "random"

        # make sure loaded weights match with hooks
        accelerator.load_state(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)

        # mode.class_name is loaded from config
        self.assertTrue(model.class_name == model.__class__.__name__)

      # remove hooks
      save_hook.remove()
      load_hook.remove()

      with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(__UpperCAmelCase)

        # make sure random weights don't match with hooks removed
        load_random_weights(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)

        # random class name to verify correct one is loaded
        a						:		Dict											=      "random"

        # make sure loaded weights match with hooks removed
        accelerator.load_state(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)

        # mode.class_name is NOT loaded from config
        self.assertTrue(model.class_name != model.__class__.__name__)






    def      __snake_case							( self    :   Optional[Any]):
      a						:		List[str]											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		int											=      create_components()
      a						:		Tuple											=      None

      # This should work
      a							,		a							,		a							,		a							,		a							,		a						:		Any											=      accelerator.prepare(
          __UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      self.assertTrue(dummy_obj is None)






    def      __snake_case							( self    :   List[str]):
      a						:		str											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		List[Any]											=      create_components()
      a						:		Union[str, Any]											=      [1, 2, 3]

      # This should work
      a							,		a							,		a							,		a							,		a							,		a						:		str											=      accelerator.prepare(
          __UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Dummy object should have `_is_accelerate_prepared` set to `True`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Model is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Optimizer is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Scheduler is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,			)






    @slow
    @require_bnb
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Optional[int]											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map={"": 0}    ,			)
      a						:		Tuple											=      Accelerator()

      # This should work
      a						:		List[Any]											=      accelerator.prepare(__UpperCAmelCase)






    @slow
    @require_bnb
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Dict											=      Accelerator()

      with init_empty_weights():
        a						:		Any											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        model.tie_weights()
        a						:		Union[str, Any]											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		str											=      "cpu"

      a						:		int											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			device_map=__UpperCAmelCase    ,			load_in_abit=__UpperCAmelCase    ,			llm_inta_enable_fpaa_cpu_offload=__UpperCAmelCase)

      # This should not work and get value error
      with self.assertRaises(__UpperCAmelCase):
        a						:		Optional[int]											=      accelerator.prepare(__UpperCAmelCase)






    @slow
    @require_bnb
    @require_multi_gpu
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Union[str, Any]											=      {"distributed_type": DistributedType.MULTI_GPU}

      with init_empty_weights():
        a						:		List[str]											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        model.tie_weights()
        a						:		Any											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		Dict											=      1

      a						:		Any											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map=__UpperCAmelCase    ,			)
      a						:		int											=      Accelerator()

      # This should not work and get value error
      with self.assertRaises(__UpperCAmelCase):
        a						:		Optional[int]											=      accelerator.prepare(__UpperCAmelCase)

      PartialState._reset_state()






    @slow
    @require_bnb
    @require_multi_gpu
    def      __snake_case							( self    :   Tuple):
      from transformers import AutoModelForCausalLM

      with init_empty_weights():
        a						:		List[str]											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        a						:		Tuple											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		str											=      1

      a						:		Optional[int]											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map=__UpperCAmelCase    ,			)
      a						:		str											=      Accelerator()

      # This should work
      a						:		Any											=      accelerator.prepare(__UpperCAmelCase)






    @require_cuda
    def      __snake_case							( self    :   List[Any]):
      a						:		Tuple											=      torch.nn.Linear(10    ,			10)
      a						:		int											=      torch.optim.SGD(model.parameters()    ,			lr=0.01)
      a						:		Optional[Any]											=      Accelerator(cpu=__UpperCAmelCase)
      a						:		List[str]											=      accelerator.prepare(__UpperCAmelCase)






