
'''simple docstring'''







import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowercase    :							Optional[Any]      =					version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A			,				__A			,				__A			,				__A			,				__A=False			,				)       ->	int:
	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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A = False	)       ->	Optional[Any]:
	_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			,				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    :							Dict      =					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    :							Union[str, Any]      =					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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				'beit'

		def __init__(  self							, _a=8_192							, _a=768							, _a=12							, _a=12							, _a=3_072							, _a="gelu"							, _a=0.0							, _a=0.0							, _a=0.02							, _a=1E-12							, _a=224							, _a=16							, _a=3							, _a=False							, _a=False							, _a=False							, _a=False							, _a=0.1							, _a=0.1							, _a=True							, _a=[3, 5, 7, 11]							, _a=[1, 2, 3, 6]							, _a=True							, _a=0.4							, _a=256							, _a=1							, _a=False							, _a=255							, **_a							, ):
				super().__init__(**_a      )

				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			initializer_range
				__a           =			layer_norm_eps

				__a           =			image_size
				__a           =			patch_size
				__a           =			num_channels
				__a           =			use_mask_token
				__a           =			use_absolute_position_embeddings
				__a           =			use_relative_position_bias
				__a           =			use_shared_relative_position_bias
				__a           =			layer_scale_init_value
				__a           =			drop_path_rate
				__a           =			use_mean_pooling
				# decode head attributes (semantic segmentation)
				__a           =			out_indices
				__a           =			pool_scales
				# auxiliary head attributes (semantic segmentation)
				__a           =			use_auxiliary_head
				__a           =			auxiliary_loss_weight
				__a           =			auxiliary_channels
				__a           =			auxiliary_num_convs
				__a           =			auxiliary_concat_input
				__a           =			semantic_loss_ignore_index







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[Any]										=				version.parse('1.11'       )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )





		@property
		def 			__UpperCAmelCase       (  self      ):
				return 1E-4






