






"""simple docstring"""




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_snake_case          =   version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=False     ,   ):



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

@torch.no_grad()
def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = False				):



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

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


if __name__ == "__main__":
 _snake_case          =   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')

 _snake_case          =   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"""


import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
A     :   Tuple			    =			logging.get_logger(__name__)

A     :   Tuple			    =			[
    ("bert.bert", "visual_bert"),
    ("bert.cls", "cls"),
    ("bert.classifier", "cls"),
    ("token_type_embeddings_visual", "visual_token_type_embeddings"),
    ("position_embeddings_visual", "visual_position_embeddings"),
    ("projection", "visual_projection"),
]

A     :   Optional[Any]			    =			[
    "nlvr2_coco_pre_trained.th",
    "nlvr2_fine_tuned.th",
    "nlvr2_pre_trained.th",
    "vcr_coco_pre_train.th",
    "vcr_fine_tune.th",
    "vcr_pre_train.th",
    "vqa_coco_pre_trained.th",
    "vqa_fine_tuned.th",
    "vqa_pre_trained.th",
]


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location="cpu"      )
			return sd


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=rename_keys_prefix      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			OrderedDict()
			__lowerCAmelCase				    =			torch.arange(config.max_position_embeddings      ).expand((1, -1)      )
			# detector_d = OrderedDict()
			for key in d:
						if "detector" in key:
									# detector_d[key.replace('detector.','')] = d[key]
									continue
						__lowerCAmelCase				    =			key
						for name_pair in rename_keys_prefix:
									__lowerCAmelCase				    =			new_key.replace(name_pair[0]    , name_pair[1]      )
						__lowerCAmelCase				    =			d[key]
						if key == "bert.cls.predictions.decoder.weight":
									# Old bert code didn't have `decoder.bias`, but was added separately
									__lowerCAmelCase				    =			new_d["cls.predictions.bias"]
			return new_d


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			assert (
			    checkpoint_path.split("/"      )[-1] in ACCEPTABLE_CHECKPOINTS
			), f"The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."

			# Get Config
			if "pre" in checkpoint_path:
						__lowerCAmelCase				    =			"pretraining"
						if "vcr" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 512}
						elif "vqa_advanced" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 2048}
						elif "vqa" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 2048}
						elif "nlvr" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 1024}
						else:
									raise NotImplementedError(f"No implementation found for `{checkpoint_path}`."      )
			else:
						if "vcr" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 512}
									__lowerCAmelCase				    =			"multichoice"
						elif "vqa_advanced" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 2048}
									__lowerCAmelCase				    =			"vqa_advanced"
						elif "vqa" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 2048, "num_labels": 3129}
									__lowerCAmelCase				    =			"vqa"
						elif "nlvr" in checkpoint_path:
									__lowerCAmelCase				    =			{
									    "visual_embedding_dim": 1024,
									    "num_labels": 2,
									}
									__lowerCAmelCase				    =			"nlvr"

			__lowerCAmelCase				    =			VisualBertConfig(**_UpperCamelCase      )

			# Load State Dict
			__lowerCAmelCase				    =			load_state_dict(_UpperCamelCase      )

			__lowerCAmelCase				    =			get_new_dict(_UpperCamelCase    , _UpperCamelCase      )

			if model_type == "pretraining":
						__lowerCAmelCase				    =			VisualBertForPreTraining(_UpperCamelCase      )
			elif model_type == "vqa":
						__lowerCAmelCase				    =			VisualBertForQuestionAnswering(_UpperCamelCase      )
			elif model_type == "nlvr":
						__lowerCAmelCase				    =			VisualBertForVisualReasoning(_UpperCamelCase      )
			elif model_type == "multichoice":
						__lowerCAmelCase				    =			VisualBertForMultipleChoice(_UpperCamelCase      )

			model.load_state_dict(_UpperCamelCase      )
			# Save Checkpoints
			Path(_UpperCamelCase      ).mkdir(exist_ok=_UpperCamelCase      )
			model.save_pretrained(_UpperCamelCase      )


if __name__ == "__main__":
	A     :   Union[str, Any]			    =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
	parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
	A     :   Optional[int]			    =			parser.parse_args()
	convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)




