


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_snake_case	:						str         =							version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")







def 							lowerCAmelCase_	(   __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 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase = False      ):
							__snake_case						:     Any									=  torch.floataa if fpaa else torch.floataa
							if fpaa and torch.cuda.is_available():
														__snake_case						:     Union[str, Any]									=  "cuda"
							elif fpaa and not torch.cuda.is_available():
														raise ValueError("`float16` model export is only supported on GPUs with CUDA"      )
							else:
														__snake_case						:     str									=  "cpu"
							__snake_case						:     List[Any]									=  Path(__lowerCamelCase      )

							# VAE DECODER
							__snake_case						:     Tuple									=  AutoencoderKL.from_pretrained(model_path + "/vae"      )
							__snake_case						:     str									=  vae_decoder.config.latent_channels
							# forward only through the decoder part
							__snake_case						:     Tuple									=  vae_decoder.decode
							onnx_export(
							    __lowerCamelCase				,      model_args=(
							        torch.randn(1				,      __lowerCamelCase				,      2_5				,      2_5      ).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__":
			_snake_case	:						List[Any]         =							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")

			_snake_case	:						Optional[int]         =							parser.parse_args()
			print(args.output_path)
			convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
			print("SD: Done: ONNX")








import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A=1024     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=[], []

   UpperCAmelCase__						=list(zip(A				,							A     )     )
   UpperCAmelCase__			,							UpperCAmelCase__						=sorted_examples[0]

   def is_too_big(A     ):
      return tok(A				,							return_tensors="pt"     ).input_ids.shape[1] > max_tokens

   for src, tgt in tqdm(sorted_examples[1:]     ):
      UpperCAmelCase__						=new_src + " " + src
      UpperCAmelCase__						=new_tgt + " " + tgt
      if is_too_big(A     ) or is_too_big(A     ):  # cant fit, finalize example
         finished_src.append(A     )
         finished_tgt.append(A     )
         UpperCAmelCase__			,							UpperCAmelCase__						=src, tgt
      else:  # can fit, keep adding
         UpperCAmelCase__			,							UpperCAmelCase__						=cand_src, cand_tgt

    # cleanup
   if new_src:
      assert new_tgt
      finished_src.append(A     )
      finished_tgt.append(A     )
   return finished_src, finished_tgt






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=Path(A     )
   save_path.mkdir(exist_ok=A     )
   for split in ["train"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__			,							UpperCAmelCase__						=pack_examples(A				,							A				,							A				,							A     )
      print(F"""packed {split} split from {len(A     )} examples -> {len(A     )}."""     )
      Path(save_path / F"""{split}.source"""     ).open("w"     ).write("\n".join(A     )     )
      Path(save_path / F"""{split}.target"""     ).open("w"     ).write("\n".join(A     )     )
   for split in ["val", "test"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      shutil.copyfile(A				,							save_path / F"""{split}.source"""     )
      shutil.copyfile(A				,							save_path / F"""{split}.target"""     )






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=argparse.ArgumentParser()
   parser.add_argument("--tok_name"				,							type=A				,							help="like facebook/bart-large-cnn,t5-base, etc."     )
   parser.add_argument("--max_seq_len"				,							type=A				,							default=128     )
   parser.add_argument("--data_dir"				,							type=A     )
   parser.add_argument("--save_path"				,							type=A     )
   UpperCAmelCase__						=parser.parse_args()
   UpperCAmelCase__						=AutoTokenizer.from_pretrained(args.tok_name     )
   return pack_data_dir(A				,							Path(args.data_dir     )				,							args.max_seq_len				,							args.save_path     )


if __name__ == "__main__":
    packer_cli()



