


'''simple docstring'''

import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_UpperCAmelCase :       str										= version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")

def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=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 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = False):
						__lowerCAmelCase											= torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												__lowerCAmelCase											= '''cuda'''
						elif fpaa and not torch.cuda.is_available():
												raise ValueError('''`float16` model export is only supported on GPUs with CUDA''')
						else:
												__lowerCAmelCase											= '''cpu'''
						__lowerCAmelCase											= Path(a_)

						# VAE DECODER
						__lowerCAmelCase											= AutoencoderKL.from_pretrained(model_path + '''/vae''')
						__lowerCAmelCase											= vae_decoder.config.latent_channels
						# forward only through the decoder part
						__lowerCAmelCase											= vae_decoder.decode
						onnx_export(
						    a_, model_args=(
						        torch.randn(1, a_, 2_5, 2_5).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__":
	_UpperCAmelCase :       Optional[Any]										= 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=1_4,
	    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 :       int										= parser.parse_args()
	print(args.output_path)
	convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
	print("""SD: Done: ONNX""")



import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()						)


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= {}

				for key, value in state_dict.items():
								if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
												continue

								__A   			= key.replace("heads.cmd.mim_head.cls.predictions"		,   "mmm_image_head"						)
								__A   			= key.replace("heads.cmd.mlm_head.cls.predictions"		,   "mmm_text_head"						)
								__A   			= key.replace("heads.cmd.itm_head.cls"		,   "itm_head"						)
								__A   			= key.replace("heads.cmd.itm_head.pooler"		,   "itm_head.pooler"						)
								__A   			= key.replace("heads.cmd.clip_head.logit_scale"		,   "flava.logit_scale"						)
								__A   			= key.replace("heads.fairseq_mlm.cls.predictions"		,   "mlm_head"						)
								__A   			= key.replace("heads.imagenet.mim_head.cls.predictions"		,   "mim_head"						)
								__A   			= key.replace("mm_text_projection"		,   "flava.text_to_mm_projection"						)
								__A   			= key.replace("mm_image_projection"		,   "flava.image_to_mm_projection"						)
								__A   			= key.replace("image_encoder.module"		,   "flava.image_model"						)
								__A   			= key.replace("text_encoder.module"		,   "flava.text_model"						)
								__A   			= key.replace("mm_encoder.module.encoder.cls_token"		,   "flava.multimodal_model.cls_token"						)
								__A   			= key.replace("mm_encoder.module"		,   "flava.multimodal_model"						)
								__A   			= key.replace("text_projection"		,   "flava.text_projection"						)
								__A   			= key.replace("image_projection"		,   "flava.image_projection"						)

								__A   			= value.float()

				for key, value in codebook_state_dict.items():
								__A   			= value

				return upgrade


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_=None						)		->	Tuple:







				"""simple docstring"""



				if config_path is not None:
								__A   			= FlavaConfig.from_pretrained(a_						)
				else:
								__A   			= FlavaConfig()

				__A   			= FlavaForPreTraining(a_						).eval()

				__A   			= convert_dalle_checkpoint(a_		,   a_		,   save_checkpoint=a_						)

				if os.path.exists(a_						):
								__A   			= torch.load(a_		,   map_location="cpu"						)
				else:
								__A   			= torch.hub.load_state_dict_from_url(a_		,   map_location="cpu"						)

				__A   			= upgrade_state_dict(a_		,   a_						)
				hf_model.load_state_dict(a_						)
				__A   			= hf_model.state_dict()
				__A   			= count_parameters(a_						)
				__A   			= count_parameters(a_						) + count_parameters(a_						)

				assert torch.allclose(a_		,   a_		,   atol=1E-3						)

				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Any							 =				argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
					parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
