


'''simple docstring'''



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


__lowercase      :		List[Any]			=      logging.get_logger(__name__)

__lowercase      :		Tuple			=      {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}





class 				__UpperCamelCase							(						UpperCamelCase__   ):
	A_           =       'openai-gpt'
	A_           =       {
	    'max_position_embeddings': 'n_positions',
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}




	def __init__(  self		,							__a=4_0478		,							__a=512		,							__a=768		,							__a=12		,							__a=12		,							__a="gelu"		,							__a=0.1		,							__a=0.1		,							__a=0.1		,							__a=1E-5		,							__a=0.02		,							__a="cls_index"		,							__a=True		,							__a=None		,							__a=True		,							__a=0.1		,							**__a		,							):

						'''simple docstring'''





						__a     :			Optional[Any]          =    vocab_size
						__a     :			Dict          =    n_positions
						__a     :			List[Any]          =    n_embd
						__a     :			str          =    n_layer
						__a     :			Optional[int]          =    n_head
						__a     :			int          =    afn
						__a     :			Optional[int]          =    resid_pdrop
						__a     :			List[Any]          =    embd_pdrop
						__a     :			List[Any]          =    attn_pdrop
						__a     :			List[Any]          =    layer_norm_epsilon
						__a     :			int          =    initializer_range
						__a     :			str          =    summary_type
						__a     :			List[str]          =    summary_use_proj
						__a     :			int          =    summary_activation
						__a     :			Tuple          =    summary_first_dropout
						__a     :			str          =    summary_proj_to_labels
						super().__init__(**__a				)





'''simple docstring'''
import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__=False  ,      ):
	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      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__ = False						):
	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__":
		lowercase__											=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')

		lowercase__											=parser.parse_args()
		print(args.output_path)
		convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
		print('SD: Done: ONNX')
