






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 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    '''xlnet'''
      __snake_case	    =    ['''mems''']
      __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 :		Any					,   __UpperCAmelCase :		Dict=32_000					,   __UpperCAmelCase :		List[Any]=1_024					,   __UpperCAmelCase :		Any=24					,   __UpperCAmelCase :		List[str]=16					,   __UpperCAmelCase :		Dict=4_096					,   __UpperCAmelCase :		int="gelu"					,   __UpperCAmelCase :		Optional[Any]=True					,   __UpperCAmelCase :		Optional[Any]="bi"					,   __UpperCAmelCase :		Union[str, Any]=0.02					,   __UpperCAmelCase :		int=1e-1_2					,   __UpperCAmelCase :		Tuple=0.1					,   __UpperCAmelCase :		Optional[int]=512					,   __UpperCAmelCase :		Optional[int]=None					,   __UpperCAmelCase :		Union[str, Any]=True					,   __UpperCAmelCase :		List[Any]=False					,   __UpperCAmelCase :		Dict=False					,   __UpperCAmelCase :		Union[str, Any]=-1					,   __UpperCAmelCase :		int=False					,   __UpperCAmelCase :		Optional[int]="last"					,   __UpperCAmelCase :		Union[str, Any]=True					,   __UpperCAmelCase :		str="tanh"					,   __UpperCAmelCase :		Any=0.1					,   __UpperCAmelCase :		Optional[int]=5					,   __UpperCAmelCase :		Optional[Any]=5					,   __UpperCAmelCase :		List[Any]=5					,   __UpperCAmelCase :		Dict=1					,   __UpperCAmelCase :		str=2					,   **__UpperCAmelCase :		Dict					,   )       ->Union[str, Any]:




       """simple docstring"""


       a						    = vocab_size
       a						    = d_model
       a						    = n_layer
       a						    = 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})"""    )
       a						    = d_model // n_head
       a						    = ff_activation
       a						    = d_inner
       a						    = untie_r
       a						    = attn_type

       a						    = initializer_range
       a						    = layer_norm_eps

       a						    = dropout
       a						    = mem_len
       a						    = reuse_len
       a						    = bi_data
       a						    = clamp_len
       a						    = same_length

       a						    = summary_type
       a						    = summary_use_proj
       a						    = summary_activation
       a						    = summary_last_dropout
       a						    = start_n_top
       a						    = end_n_top

       a						    = bos_token_id
       a						    = pad_token_id
       a						    = 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					,   )
        a						    = kwargs['''use_cache''']

       a						    = use_mems_eval
       a						    = use_mems_train
       super().__init__(pad_token_id=__UpperCAmelCase					,   bos_token_id=__UpperCAmelCase					,   eos_token_id=__UpperCAmelCase					,   **__UpperCAmelCase    )







      @property
      def  __lowerCAmelCase   ( self :		List[str]    )       ->str:




       """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  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		Union[str, Any]    )       ->List[Any]:




       """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 		_a       ( a					:Union[str, Any]      ,				a					:tuple      ,				a					:Path      ,				a					:List[Any]      ,				a					:Union[str, Any]      ,				a					:Optional[int]      ,				a					:List[Any]      ,				a					:Union[str, Any]=False      ,				)				->     Any:
 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 		_a       ( a					:str      ,				a					:str      ,				a					:int      ,				a					:bool = False					)				->     Union[str, Any]:
 a						    = torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  a						    = '''cuda'''
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''					)
 else:
  a						    = '''cpu'''
 a						    = Path(a					)

 # VAE DECODER
 a						    = AutoencoderKL.from_pretrained(model_path + '''/vae'''					)
 a						    = vae_decoder.config.latent_channels
 # forward only through the decoder part
 a						    = vae_decoder.decode
 onnx_export(
     a      ,				model_args=(
         torch.randn(1      ,				a      ,				25      ,				25					).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")


