
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class 			__A( nn.Module			):




				def __init__(     self		,     _snake_case = 16		,     _snake_case = 88		,     _snake_case = None		,     _snake_case = 1		,     _snake_case = 0.0		,     _snake_case = 32		,     _snake_case = None		,     _snake_case = False		,     _snake_case = None		,     _snake_case = None		,     _snake_case = "geglu"		,     _snake_case = None		,     )    ->      Any:


								'''simple docstring'''




								super().__init__()
								__a										=    nn.ModuleList(
								    [
								        TransformeraDModel(
								            num_attention_heads=_snake_case		,     attention_head_dim=_snake_case		,     in_channels=_snake_case		,     num_layers=_snake_case		,     dropout=_snake_case		,     norm_num_groups=_snake_case		,     cross_attention_dim=_snake_case		,     attention_bias=_snake_case		,     sample_size=_snake_case		,     num_vector_embeds=_snake_case		,     activation_fn=_snake_case		,     num_embeds_ada_norm=_snake_case		,     )
								        for _ in range(2				)
								    ]				)

								# Variables that can be set by a pipeline:

								# The ratio of transformer1 to transformer2's output states to be combined during inference
								__a										=    0.5

								# The shape of `encoder_hidden_states` is expected to be
								# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
								__a										=    [77, 257]

								# Which transformer to use to encode which condition.
								# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
								__a										=    [1, 0]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case = True		,     )    ->      Tuple:


								'''simple docstring'''




								__a										=    hidden_states

								__a										=    []
								__a										=    0
								# attention_mask is not used yet
								for i in range(2				):
												# for each of the two transformers, pass the corresponding condition tokens
												__a										=    encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
												__a										=    self.transformer_index_for_condition[i]
												__a										=    self.transformers[transformer_index](
												    _snake_case		,     encoder_hidden_states=_snake_case		,     timestep=_snake_case		,     cross_attention_kwargs=_snake_case		,     return_dict=_snake_case		,     )[0]
												encoded_states.append(encoded_state - input_states				)
												tokens_start += self.condition_lengths[i]

								__a										=    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
								__a										=    output_states + input_states

								if not return_dict:
												return (output_states,)

								return TransformeraDModelOutput(sample=_snake_case				)


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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	tuple    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=False    ,			)							->      Union[str, Any]:





    '''simple docstring'''






    output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__    ,			exist_ok=SCREAMING_SNAKE_CASE__      )
    # 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(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			use_external_data_format=SCREAMING_SNAKE_CASE__    ,			enable_onnx_checker=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
    else:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      Tuple:





    '''simple docstring'''






    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(SCREAMING_SNAKE_CASE__      )

    # 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(
        SCREAMING_SNAKE_CASE__    ,			model_args=(
            torch.randn(1    ,			SCREAMING_SNAKE_CASE__    ,			25    ,			25      ).to(device=SCREAMING_SNAKE_CASE__    ,			dtype=SCREAMING_SNAKE_CASE__      ),
            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=SCREAMING_SNAKE_CASE__    ,			)
    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")

