




import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


_UpperCAmelCase	:						List[str]			=     version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase=False,			):
  output_path.parent.mkdir(parents=lowerCamelCase,			exist_ok=lowerCamelCase   )
  # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
  # so we check the torch version for backwards compatibility
  if is_torch_less_than_1_11:
    export(
        lowerCamelCase,			lowerCamelCase,			f=output_path.as_posix(),			input_names=lowerCamelCase,			output_names=lowerCamelCase,			dynamic_axes=lowerCamelCase,			do_constant_folding=lowerCamelCase,			use_external_data_format=lowerCamelCase,			enable_onnx_checker=lowerCamelCase,			opset_version=lowerCamelCase,			)
  else:
    export(
        lowerCamelCase,			lowerCamelCase,			f=output_path.as_posix(),			input_names=lowerCamelCase,			output_names=lowerCamelCase,			dynamic_axes=lowerCamelCase,			do_constant_folding=lowerCamelCase,			opset_version=lowerCamelCase,			)



@torch.no_grad()
def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase = False   ):
  lowercase    :Any      	=						torch.floataa if fpaa else torch.floataa
  if fpaa and torch.cuda.is_available():
    lowercase    :int      	=						"cuda"
  elif fpaa and not torch.cuda.is_available():
    raise ValueError("`float16` model export is only supported on GPUs with CUDA"   )
  else:
    lowercase    :Tuple      	=						"cpu"
  lowercase    :Union[str, Any]      	=						Path(lowerCamelCase   )

  # VAE DECODER
  lowercase    :List[str]      	=						AutoencoderKL.from_pretrained(model_path + "/vae"   )
  lowercase    :Any      	=						vae_decoder.config.latent_channels
  # forward only through the decoder part
  lowercase    :Optional[Any]      	=						vae_decoder.decode
  onnx_export(
      lowerCamelCase,			model_args=(
          torch.randn(1,			lowerCamelCase,			25,			25   ).to(device=lowerCamelCase,			dtype=lowerCamelCase   ),
          False,
      ),			output_path=output_path / "vae_decoder" / "model.onnx",			ordered_input_names=["latent_sample", "return_dict"],			output_names=["sample"],			dynamic_axes={
          "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
      },			opset=lowerCamelCase,			)
  del vae_decoder


if __name__ == "__main__":
     _UpperCAmelCase	:						str			=     argparse.ArgumentParser()

     parser.add_argument(
         "--model_path",
         type=str,
         required=True,
         help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
     )

     parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
     parser.add_argument(
         "--opset",
         default=14,
         type=int,
         help="The version of the ONNX operator set to use.",
     )
     parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

     _UpperCAmelCase	:						Union[str, Any]			=     parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")









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       UpperCAmelCase__  (     ):
  lowercase    :List[str]      	=						torch.nn.Linear(2,			4   )
  lowercase    :List[Any]      	=						torch.optim.AdamW(model.parameters(),			lr=1.0   )
  lowercase    :int      	=						torch.optim.lr_scheduler.OneCycleLR(lowerCamelCase,			max_lr=0.01,			steps_per_epoch=2,			epochs=1   )
  lowercase    :Optional[int]      	=						DataLoader(TensorDataset(torch.tensor([1, 2, 3]   )   )   )
  lowercase    :List[str]      	=						DataLoader(TensorDataset(torch.tensor([4, 5, 6]   )   )   )

  return model, optimizer, scheduler, train_dl, valid_dl



def       UpperCAmelCase__  (     lowerCamelCase   ):
  return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Dict      	=						torch.nn.Linear(*tuple(model.weight.T.shape   )   ).state_dict()
  model.load_state_dict(lowerCamelCase   )







class    __lowerCAmelCase       (							lowerCAmelCase):






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






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Any      	=						Accelerator()
         lowercase    :Dict      	=						GradientState()
         assert state.num_steps == 1
         lowercase    :Tuple      	=						4
         assert state.num_steps == 4

         assert state.sync_gradients is True
         lowercase    :List[Any]      	=						False
         assert state.sync_gradients is False
         GradientState._reset_state()






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :str      	=						Accelerator()
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :Any      	=						create_components()

         (
             (
             lowercase
         )	,    (
             lowercase
         )	,    (
             lowercase
         )	,    (
             lowercase
         )	,    (
             lowercase
         )	,    
         )    :Tuple      	=						accelerator.prepare(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				)

         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 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :Dict      	=						Accelerator()
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :List[str]      	=						create_components()
         accelerator.prepare(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				)
         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 					SCREAMING_SNAKE_CASE    (   self:					int				):
         PartialState._reset_state()

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

         with patch("torch.cuda.set_device"       ,					_lowerCAmelCase				), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"				):
           lowercase    :List[Any]      	=						Accelerator()
           self.assertEqual(str(accelerator.state.device				)       ,					"cuda:64"				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Tuple      	=						Accelerator()
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :Optional[Any]      	=						create_components()
         accelerator.prepare(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				)

         lowercase    :Tuple      	=						get_signature(_lowerCAmelCase				)

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

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

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         lowercase    :Dict      	=						Accelerator()
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :int      	=						create_components()
         accelerator.prepare(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				)

         lowercase    :int      	=						get_signature(_lowerCAmelCase				)

         # saving hook
         def save_config(_lowerCAmelCase:					Union[str, Any]       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					List[Any]				):
           lowercase    :Dict      	=						{"class_name": models[0].__class__.__name__}

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

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

           lowercase    :Optional[int]      	=						config["class_name"]

         lowercase    :Optional[Any]      	=						accelerator.register_save_state_pre_hook(_lowerCAmelCase				)
         lowercase    :Tuple      	=						accelerator.register_load_state_pre_hook(_lowerCAmelCase				)

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

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

           # random class name to verify correct one is loaded
           lowercase    :Optional[int]      	=						"random"

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

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

           # random class name to verify correct one is loaded
           lowercase    :str      	=						"random"

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

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         lowercase    :List[str]      	=						Accelerator()
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :List[Any]      	=						create_components()
         lowercase    :List[str]      	=						None

         # This should work
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :str      	=						accelerator.prepare(
             _lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				)
         self.assertTrue(dummy_obj is None				)






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

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






       @slow
       @require_bnb
       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         from transformers import AutoModelForCausalLM

         lowercase    :Any      	=						AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"       ,					load_in_abit=_lowerCAmelCase       ,					device_map={"": 0}       ,					)
         lowercase    :int      	=						Accelerator()

         # This should work
         lowercase    :Optional[int]      	=						accelerator.prepare(_lowerCAmelCase				)






       @slow
       @require_bnb
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         from transformers import AutoModelForCausalLM

         lowercase    :Optional[Any]      	=						Accelerator()

         with init_empty_weights():
           lowercase    :Any      	=						AutoModelForCausalLM.from_pretrained(
               "EleutherAI/gpt-neo-125m"       ,					)
           model.tie_weights()
           lowercase    :Dict      	=						infer_auto_device_map(_lowerCAmelCase				)
           lowercase    :int      	=						"cpu"

         lowercase    :str      	=						AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"       ,					device_map=_lowerCAmelCase       ,					load_in_abit=_lowerCAmelCase       ,					llm_inta_enable_fpaa_cpu_offload=_lowerCAmelCase				)

         # This should not work and get value error
         with self.assertRaises(_lowerCAmelCase				):
           lowercase    :Dict      	=						accelerator.prepare(_lowerCAmelCase				)






       @slow
       @require_bnb
       @require_multi_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         from transformers import AutoModelForCausalLM

         lowercase    :List[str]      	=						{"distributed_type": DistributedType.MULTI_GPU}

         with init_empty_weights():
           lowercase    :str      	=						AutoModelForCausalLM.from_pretrained(
               "EleutherAI/gpt-neo-125m"       ,					)
           model.tie_weights()
           lowercase    :Optional[Any]      	=						infer_auto_device_map(_lowerCAmelCase				)
           lowercase    :Any      	=						1

         lowercase    :str      	=						AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"       ,					load_in_abit=_lowerCAmelCase       ,					device_map=_lowerCAmelCase       ,					)
         lowercase    :List[str]      	=						Accelerator()

         # This should not work and get value error
         with self.assertRaises(_lowerCAmelCase				):
           lowercase    :Optional[Any]      	=						accelerator.prepare(_lowerCAmelCase				)

         PartialState._reset_state()






       @slow
       @require_bnb
       @require_multi_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         from transformers import AutoModelForCausalLM

         with init_empty_weights():
           lowercase    :List[str]      	=						AutoModelForCausalLM.from_pretrained(
               "EleutherAI/gpt-neo-125m"       ,					)
           lowercase    :List[Any]      	=						infer_auto_device_map(_lowerCAmelCase				)
           lowercase    :Optional[int]      	=						1

         lowercase    :Union[str, Any]      	=						AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"       ,					load_in_abit=_lowerCAmelCase       ,					device_map=_lowerCAmelCase       ,					)
         lowercase    :Union[str, Any]      	=						Accelerator()

         # This should work
         lowercase    :List[Any]      	=						accelerator.prepare(_lowerCAmelCase				)






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



