import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCamelCase					:	Dict       =  version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   tuple   , lowerCAmelCase							:   Path   , lowerCAmelCase							:   str   , lowerCAmelCase							:   int   , lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   Any   , lowerCAmelCase							:   List[str]=False   , ):
		"""simple docstring"""







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




@torch.no_grad()
def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   int   , lowerCAmelCase							:   bool = False	):
		"""simple docstring"""







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

		# VAE DECODER
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AutoencoderKL.from_pretrained(model_path + "/vae"	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    vae_decoder.config.latent_channels
		# forward only through the decoder part
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    vae_decoder.decode
		onnx_export(
		    lowerCAmelCase__   , model_args=(
		        torch.randn(1   , lowerCAmelCase__   , 2_5   , 2_5	).to(device=lowerCAmelCase__   , dtype=lowerCAmelCase__	),
		        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=lowerCAmelCase__   , )
		del vae_decoder


if __name__ == "__main__":
		__lowerCamelCase					:	Tuple       =  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''')

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

"""simple docstring"""






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


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

__lowerCAmelCase    :       Union[str, Any]					     ={
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Union[str, Any]							  =	'open-llama'

      def __init__(						self		,       __lowerCAmelCase=10_0000		,       __lowerCAmelCase=4096		,       __lowerCAmelCase=1_1008		,       __lowerCAmelCase=32		,       __lowerCAmelCase=32		,       __lowerCAmelCase="silu"		,       __lowerCAmelCase=2048		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=1E-6		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0		,       __lowerCAmelCase=1		,       __lowerCAmelCase=2		,       __lowerCAmelCase=False		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=True		,       __lowerCAmelCase=True		,       __lowerCAmelCase=None		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	vocab_size
            lowercase														=	max_position_embeddings
            lowercase														=	hidden_size
            lowercase														=	intermediate_size
            lowercase														=	num_hidden_layers
            lowercase														=	num_attention_heads
            lowercase														=	hidden_act
            lowercase														=	initializer_range
            lowercase														=	rms_norm_eps
            lowercase														=	use_cache
            lowercase														=	kwargs.pop(
                """use_memorry_efficient_attention"""		,       __lowerCAmelCase						)
            lowercase														=	hidden_dropout_prob
            lowercase														=	attention_dropout_prob
            lowercase														=	use_stable_embedding
            lowercase														=	shared_input_output_embedding
            lowercase														=	rope_scaling
            self._rope_scaling_validation()

            super().__init__(
                pad_token_id=__lowerCAmelCase		,       bos_token_id=__lowerCAmelCase		,       eos_token_id=__lowerCAmelCase		,       tie_word_embeddings=__lowerCAmelCase		,       **__lowerCAmelCase		,       )







      def 				A__       (						self						):







            """simple docstring"""



            if self.rope_scaling is None:
                  return

            if not isinstance(self.rope_scaling		,       __lowerCAmelCase						) or len(self.rope_scaling						) != 2:
                  raise ValueError(
                      """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
                      f'got {self.rope_scaling}'						)
            lowercase														=	self.rope_scaling.get("""type"""		,       __lowerCAmelCase						)
            lowercase														=	self.rope_scaling.get("""factor"""		,       __lowerCAmelCase						)
            if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                  raise ValueError(
                      f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'						)
            if rope_scaling_factor is None or not isinstance(__lowerCAmelCase		,       __lowerCAmelCase						) or rope_scaling_factor <= 1.0:
                  raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'						)



