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

    # VAE DECODER
    lowercase__ :   Optional[int]    					=				AutoencoderKL.from_pretrained(model_path + "/vae")
    lowercase__ :   str    					=				vae_decoder.config.latent_channels
    # forward only through the decoder part
    lowercase__ :   Optional[int]    					=				vae_decoder.decode
    onnx_export(
        _lowerCamelCase		,	model_args=(
            torch.randn(1		,	_lowerCamelCase		,	25		,	25).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__":
      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''')





import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)  # pylint: disable=invalid-name







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=768       ):
		super().__init__(snake_case       )
		lowercase       =    proj_size

		lowercase       =    CLIPVisionModel(snake_case       )
		lowercase       =    PaintByExampleMapper(snake_case       )
		lowercase       =    nn.LayerNorm(config.hidden_size       )
		lowercase       =    nn.Linear(config.hidden_size		,    self.proj_size       )

		# uncondition for scaling
		lowercase       =    nn.Parameter(torch.randn((1, 1, self.proj_size)       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=False       ):
		lowercase       =    self.model(pixel_values=snake_case       )
		lowercase       =    clip_output.pooler_output
		lowercase       =    self.mapper(latent_states[:, None]       )
		lowercase       =    self.final_layer_norm(snake_case       )
		lowercase       =    self.proj_out(snake_case       )
		if return_uncond_vector:
			return latent_states, self.uncond_vector

		return latent_states







class        A_   (			nn.Module   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		super().__init__()
		lowercase       =    (config.num_hidden_layers + 1) // 5
		lowercase       =    config.hidden_size
		lowercase       =    1
		lowercase       =    nn.ModuleList(
		    [
		        BasicTransformerBlock(snake_case		,    snake_case		,    snake_case		,    activation_fn='gelu'		,    attention_bias=snake_case       )
		        for _ in range(snake_case       )
		    ]       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		for block in self.blocks:
			lowercase       =    block(snake_case       )

		return hidden_states

