

import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__		 =     logging.get_logger(__name__)

UpperCamelCase__		 =     {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}

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







       __SCREAMING_SNAKE_CASE				    =							"xlnet"
       __SCREAMING_SNAKE_CASE				    =							["mems"]
       __SCREAMING_SNAKE_CASE				    =							{
           "n_token": "vocab_size",  # Backward compatibility
           "hidden_size": "d_model",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }


       def __init__(		self      , _UpperCamelCase=3_20_00      , _UpperCamelCase=10_24      , _UpperCamelCase=24      , _UpperCamelCase=16      , _UpperCamelCase=40_96      , _UpperCamelCase="gelu"      , _UpperCamelCase=True      , _UpperCamelCase="bi"      , _UpperCamelCase=0.02      , _UpperCamelCase=1E-12      , _UpperCamelCase=0.1      , _UpperCamelCase=5_12      , _UpperCamelCase=None      , _UpperCamelCase=True      , _UpperCamelCase=False      , _UpperCamelCase=False      , _UpperCamelCase=-1      , _UpperCamelCase=False      , _UpperCamelCase="last"      , _UpperCamelCase=True      , _UpperCamelCase="tanh"      , _UpperCamelCase=0.1      , _UpperCamelCase=5      , _UpperCamelCase=5      , _UpperCamelCase=5      , _UpperCamelCase=1      , _UpperCamelCase=2      , **_UpperCamelCase      , )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							vocab_size
              __snake_case       	=							d_model
              __snake_case       	=							n_layer
              __snake_case       	=							n_head
              if d_model % n_head != 0:
                     raise ValueError(F'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'   )
              if "d_head" in kwargs:
                     if kwargs["d_head"] != d_model // n_head:
                            raise ValueError(
                                F'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})'   )
              __snake_case       	=							d_model // n_head
              __snake_case       	=							ff_activation
              __snake_case       	=							d_inner
              __snake_case       	=							untie_r
              __snake_case       	=							attn_type

              __snake_case       	=							initializer_range
              __snake_case       	=							layer_norm_eps

              __snake_case       	=							dropout
              __snake_case       	=							mem_len
              __snake_case       	=							reuse_len
              __snake_case       	=							bi_data
              __snake_case       	=							clamp_len
              __snake_case       	=							same_length

              __snake_case       	=							summary_type
              __snake_case       	=							summary_use_proj
              __snake_case       	=							summary_activation
              __snake_case       	=							summary_last_dropout
              __snake_case       	=							start_n_top
              __snake_case       	=							end_n_top

              __snake_case       	=							bos_token_id
              __snake_case       	=							pad_token_id
              __snake_case       	=							eos_token_id

              if "use_cache" in kwargs:
                     warnings.warn(
                         """The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"""
                         """ instead."""      , _UpperCamelCase      , )
                     __snake_case       	=							kwargs["""use_cache"""]

              __snake_case       	=							use_mems_eval
              __snake_case       	=							use_mems_train
              super().__init__(pad_token_id=_UpperCamelCase      , bos_token_id=_UpperCamelCase      , eos_token_id=_UpperCamelCase      , **_UpperCamelCase   )


       @property
       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              logger.info(F'The model {self.model_type} is one of the few models that has no sequence length limit.'   )
              return -1






       @max_position_embeddings.setter
       def 			a		(		self      , _UpperCamelCase   )      ->							Dict:






              """simple docstring"""


              raise NotImplementedError(
                  F'The model {self.model_type} is one of the few models that has no sequence length limit.'   )




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''')

