


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__UpperCamelCase       :       Union[str, Any]						       =				version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[Any]    , SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				List[Any]    , SCREAMING_SNAKE_CASE	:				List[Any]=False    , ):







	"""simple docstring"""



	output_path.parent.mkdir(parents=_UpperCAmelCase    , exist_ok=_UpperCAmelCase     )
	# 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(
		    _UpperCAmelCase    , _UpperCAmelCase    , f=output_path.as_posix()    , input_names=_UpperCAmelCase    , output_names=_UpperCAmelCase    , dynamic_axes=_UpperCAmelCase    , do_constant_folding=_UpperCAmelCase    , use_external_data_format=_UpperCAmelCase    , enable_onnx_checker=_UpperCAmelCase    , opset_version=_UpperCAmelCase    , )
	else:
		export(
		    _UpperCAmelCase    , _UpperCAmelCase    , f=output_path.as_posix()    , input_names=_UpperCAmelCase    , output_names=_UpperCAmelCase    , dynamic_axes=_UpperCAmelCase    , do_constant_folding=_UpperCAmelCase    , opset_version=_UpperCAmelCase    , )

@torch.no_grad()
def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				Optional[Any] = False     ):







	"""simple docstring"""



	UpperCamelCase__					:    Dict          =   torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		UpperCamelCase__					:    Dict          =   '''cuda'''
	elif fpaa and not torch.cuda.is_available():
		raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''     )
	else:
		UpperCamelCase__					:    List[str]          =   '''cpu'''
	UpperCamelCase__					:    str          =   Path(_UpperCAmelCase     )

	# VAE DECODER
	UpperCamelCase__					:    Optional[int]          =   AutoencoderKL.from_pretrained(model_path + '''/vae'''     )
	UpperCamelCase__					:    Union[str, Any]          =   vae_decoder.config.latent_channels
	# forward only through the decoder part
	UpperCamelCase__					:    int          =   vae_decoder.decode
	onnx_export(
	    _UpperCAmelCase    , model_args=(
	        torch.randn(1    , _UpperCAmelCase    , 25    , 25     ).to(device=_UpperCAmelCase    , dtype=_UpperCAmelCase     ),
	        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=_UpperCAmelCase    , )
	del vae_decoder


if __name__ == "__main__":
							__UpperCamelCase       :       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")

							__UpperCamelCase       :       str						       =				parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print("SD: Done: ONNX")






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_camembert import CamembertTokenizer
else:
       __UpperCamelCase       :       Tuple						       =				None


__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase       :       Tuple						       =				{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


     def __init__(      self							:     int							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Dict="<s>"							,	lowerCamelCase__							:     List[str]="</s>"							,	lowerCamelCase__							:     int="</s>"							,	lowerCamelCase__							:     List[str]="<s>"							,	lowerCamelCase__							:     Optional[int]="<unk>"							,	lowerCamelCase__							:     Union[str, Any]="<pad>"							,	lowerCamelCase__							:     List[Any]="<mask>"							,	lowerCamelCase__							:     List[Any]=["<s>NOTUSED", "</s>NOTUSED"]							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      super().__init__(
          lowerCamelCase__							,	tokenizer_file=lowerCamelCase__							,	bos_token=lowerCamelCase__							,	eos_token=lowerCamelCase__							,	sep_token=lowerCamelCase__							,	cls_token=lowerCamelCase__							,	unk_token=lowerCamelCase__							,	pad_token=lowerCamelCase__							,	mask_token=lowerCamelCase__							,	additional_special_tokens=lowerCamelCase__							,	**lowerCamelCase__							,	)

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase__					:    Any          =   [self.cls_token_id]
      UpperCamelCase__					:    Dict          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    Tuple          =   [self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep			) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.'''			)

      if not os.path.isdir(lowerCamelCase__			):
       logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
       return
      UpperCamelCase__					:    List[str]          =   os.path.join(
          lowerCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCamelCase__			):
       copyfile(self.vocab_file							,	lowerCamelCase__			)

      return (out_vocab_file,)


