

import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__		 =     logging.get_logger(__name__)

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							["input_ids", "attention_mask"]


       def __init__(		self      , _UpperCamelCase="</s>"      , _UpperCamelCase="<unk>"      , _UpperCamelCase="<pad>"      , _UpperCamelCase=1_25      , _UpperCamelCase=None      , **_UpperCamelCase      , )      ->							None:






              """simple docstring"""


              if extra_ids > 0 and additional_special_tokens is None:
                     __snake_case       	=							[F'<extra_id_{i}>' for i in range(_UpperCamelCase   )]
              elif extra_ids > 0 and additional_special_tokens is not None:
                     # Check that we have the right number of extra_id special tokens
                     __snake_case       	=							len(set(filter(lambda _UpperCamelCase   : bool("""extra_id""" in str(_UpperCamelCase   )   )      , _UpperCamelCase   )   )   )
                     if extra_tokens != extra_ids:
                            raise ValueError(
                                F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                                """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
                                """ extra_ids tokens"""   )

              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else pad_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else eos_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else unk_token

              super().__init__(
                  eos_token=_UpperCamelCase      , unk_token=_UpperCamelCase      , pad_token=_UpperCamelCase      , extra_ids=_UpperCamelCase      , additional_special_tokens=_UpperCamelCase      , **_UpperCamelCase      , )

              __snake_case       	=							extra_ids

              __snake_case       	=							2**8  # utf is 8 bits

              # define special tokens dict
              __snake_case       	=							{
                  self.pad_token: 0,
                  self.eos_token: 1,
                  self.unk_token: 2,
              }
              __snake_case       	=							len(self.special_tokens_encoder   )
              __snake_case       	=							len(_UpperCamelCase   )
              for i, token in enumerate(_UpperCamelCase   ):
                     __snake_case       	=							self.vocab_size + i - n
              __snake_case       	=							{v: k for k, v in self.special_tokens_encoder.items()}


       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              return self._utf_vocab_size + self._num_special_tokens + self._extra_ids


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = False   )      ->							List[int]:






              """simple docstring"""


              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=_UpperCamelCase      , token_ids_a=_UpperCamelCase      , already_has_special_tokens=_UpperCamelCase   )

              # normal case: some special tokens
              if token_ids_a is None:
                     return ([0] * len(_UpperCamelCase   )) + [1]
              return ([0] * len(_UpperCamelCase   )) + [1] + ([0] * len(_UpperCamelCase   )) + [1]


       def 			a		(		self      , _UpperCamelCase   )      ->							List[int]:






              """simple docstring"""


              if len(_UpperCamelCase   ) > 0 and token_ids[-1] == self.eos_token_id:
                     warnings.warn(
                         F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
                         """ eos tokens being added."""   )
                     return token_ids
              else:
                     return token_ids + [self.eos_token_id]


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							List[int]:






              """simple docstring"""


              __snake_case       	=							[self.eos_token_id]

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


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							List[int]:






              """simple docstring"""


              __snake_case       	=							self._add_eos_if_not_present(_UpperCamelCase   )
              if token_ids_a is None:
                     return token_ids_a
              else:
                     __snake_case       	=							self._add_eos_if_not_present(_UpperCamelCase   )
                     return token_ids_a + token_ids_a


       def 			a		(		self      , _UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							[chr(_UpperCamelCase   ) for i in text.encode("""utf-8"""   )]
              return tokens


       def 			a		(		self      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              if token in self.special_tokens_encoder:
                     __snake_case       	=							self.special_tokens_encoder[token]
              elif token in self.added_tokens_encoder:
                     __snake_case       	=							self.added_tokens_encoder[token]
              elif len(_UpperCamelCase   ) != 1:
                     __snake_case       	=							self.unk_token_id
              else:
                     __snake_case       	=							ord(_UpperCamelCase   ) + self._num_special_tokens
              return token_id


       def 			a		(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              if index in self.special_tokens_decoder:
                     __snake_case       	=							self.special_tokens_decoder[index]
              else:
                     __snake_case       	=							chr(index - self._num_special_tokens   )
              return token


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              __snake_case       	=							B""""""
              for token in tokens:
                     if token in self.special_tokens_decoder:
                            __snake_case       	=							self.special_tokens_decoder[token].encode("""utf-8"""   )
                     elif token in self.added_tokens_decoder:
                            __snake_case       	=							self.special_tokens_decoder[token].encode("""utf-8"""   )
                     elif token in self.special_tokens_encoder:
                            __snake_case       	=							token.encode("""utf-8"""   )
                     elif token in self.added_tokens_encoder:
                            __snake_case       	=							token.encode("""utf-8"""   )
                     else:
                            __snake_case       	=							bytes([ord(_UpperCamelCase   )]   )
                     bstring += tok_string
              __snake_case       	=							bstring.decode("""utf-8"""      , errors="""ignore"""   )
              return string






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							Tuple[str]:






              """simple docstring"""


              return ()




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCamelCase__		 =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')







def 						lowerCamelCase__      ( __A    :Any	,__A    :tuple	,__A    :Path	,__A    :int	,__A    :Union[str, Any]	,__A    :Optional[Any]	,__A    :Optional[Any]	,__A    :List[Any]=False	,):


       """simple docstring"""




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







@torch.no_grad()
def 						lowerCamelCase__      ( __A    :str	,__A    :str	,__A    :int	,__A    :bool = False		):


       """simple docstring"""




       __snake_case       	=							torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              __snake_case       	=							"""cuda"""
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
       else:
              __snake_case       	=							"""cpu"""
       __snake_case       	=							Path(__A		)

       # VAE DECODER
       __snake_case       	=							AutoencoderKL.from_pretrained(model_path + """/vae"""		)
       __snake_case       	=							vae_decoder.config.latent_channels
       # forward only through the decoder part
       __snake_case       	=							vae_decoder.decode
       onnx_export(
           __A	,model_args=(
               torch.randn(1	,__A	,2_5	,2_5		).to(device=__A	,dtype=__A		),
               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=__A	,)
       del vae_decoder


if __name__ == "__main__":
    UpperCamelCase__		 =     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__		 =     parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print('''SD: Done: ONNX''')

