


"""simple docstring"""






import argparse
import os
import shutil
from pathlib import Path

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

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


UpperCAmelCase       :	Tuple  		=						version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def 	lowerCamelCase			(	_UpperCamelCase						: Dict			,     _UpperCamelCase						: List[str]			,     _UpperCamelCase						: Union[str, Any]			,     _UpperCamelCase						: Dict			,     _UpperCamelCase						: int			,     _UpperCamelCase						: Optional[Any]			,     _UpperCamelCase						: Optional[int]			,     _UpperCamelCase						: List[str]=False			,     )						->			str:

							'''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						: Union[str, Any]			,     _UpperCamelCase						: Union[str, Any]			,     _UpperCamelCase						: List[str]			,     _UpperCamelCase						: Optional[int] = False				)						->			Optional[Any]:

							'''simple docstring'''



							__UpperCAmelCase  :		Dict 				=   torch.floataa if fpaa else torch.floataa
							if fpaa and torch.cuda.is_available():
														__UpperCAmelCase  :		Optional[Any] 				=   'cuda'
							elif fpaa and not torch.cuda.is_available():
														raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""				)
							else:
														__UpperCAmelCase  :		Dict 				=   'cpu'
							__UpperCAmelCase  :		str 				=   StableDiffusionPipeline.from_pretrained(_UpperCamelCase			,     torch_dtype=_UpperCamelCase				).to(_UpperCamelCase				)
							__UpperCAmelCase  :		Optional[Any] 				=   Path(_UpperCamelCase				)

							# TEXT ENCODER
							__UpperCAmelCase  :		Optional[int] 				=   pipeline.text_encoder.config.max_position_embeddings
							__UpperCAmelCase  :		Union[str, Any] 				=   pipeline.text_encoder.config.hidden_size
							__UpperCAmelCase  :		str 				=   pipeline.tokenizer(
							    """A sample prompt"""			,     padding="""max_length"""			,     max_length=pipeline.tokenizer.model_max_length			,     truncation=_UpperCamelCase			,     return_tensors="""pt"""			,     )
							onnx_export(
							    pipeline.text_encoder			,     model_args=(text_input.input_ids.to(device=_UpperCamelCase			,     dtype=torch.intaa				))			,     output_path=output_path / """text_encoder""" / """model.onnx"""			,     ordered_input_names=["""input_ids"""]			,     output_names=["""last_hidden_state""", """pooler_output"""]			,     dynamic_axes={
							        """input_ids""": {0: """batch""", 1: """sequence"""},
							    }			,     opset=_UpperCamelCase			,     )
							del pipeline.text_encoder

							# UNET
							__UpperCAmelCase  :		Optional[int] 				=   pipeline.unet.config.in_channels
							__UpperCAmelCase  :		Tuple 				=   pipeline.unet.config.sample_size
							__UpperCAmelCase  :		Optional[Any] 				=   output_path / 'unet' / 'model.onnx'
							onnx_export(
							    pipeline.unet			,     model_args=(
							        torch.randn(2			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase				).to(device=_UpperCamelCase			,     dtype=_UpperCamelCase				),
							        torch.randn(2				).to(device=_UpperCamelCase			,     dtype=_UpperCamelCase				),
							        torch.randn(2			,     _UpperCamelCase			,     _UpperCamelCase				).to(device=_UpperCamelCase			,     dtype=_UpperCamelCase				),
							        False,
							    )			,     output_path=_UpperCamelCase			,     ordered_input_names=["""sample""", """timestep""", """encoder_hidden_states""", """return_dict"""]			,     output_names=["""out_sample"""]			,     dynamic_axes={
							        """sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
							        """timestep""": {0: """batch"""},
							        """encoder_hidden_states""": {0: """batch""", 1: """sequence"""},
							    }			,     opset=_UpperCamelCase			,     use_external_data_format=_UpperCamelCase			,     )
							__UpperCAmelCase  :		List[str] 				=   str(unet_path.absolute().as_posix()				)
							__UpperCAmelCase  :		str 				=   os.path.dirname(_UpperCamelCase				)
							__UpperCAmelCase  :		int 				=   onnx.load(_UpperCamelCase				)
							# clean up existing tensor files
							shutil.rmtree(_UpperCamelCase				)
							os.mkdir(_UpperCamelCase				)
							# collate external tensor files into one
							onnx.save_model(
							    _UpperCamelCase			,     _UpperCamelCase			,     save_as_external_data=_UpperCamelCase			,     all_tensors_to_one_file=_UpperCamelCase			,     location="""weights.pb"""			,     convert_attribute=_UpperCamelCase			,     )
							del pipeline.unet

							# VAE ENCODER
							__UpperCAmelCase  :		List[str] 				=   pipeline.vae
							__UpperCAmelCase  :		str 				=   vae_encoder.config.in_channels
							__UpperCAmelCase  :		Optional[Any] 				=   vae_encoder.config.sample_size
							# need to get the raw tensor output (sample) from the encoder
							__UpperCAmelCase  :		List[str] 				=   lambda _UpperCamelCase			,     _UpperCamelCase				: vae_encoder.encode(_UpperCamelCase			,     _UpperCamelCase				)[0].sample()
							onnx_export(
							    _UpperCamelCase			,     model_args=(
							        torch.randn(1			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase				).to(device=_UpperCamelCase			,     dtype=_UpperCamelCase				),
							        False,
							    )			,     output_path=output_path / """vae_encoder""" / """model.onnx"""			,     ordered_input_names=["""sample""", """return_dict"""]			,     output_names=["""latent_sample"""]			,     dynamic_axes={
							        """sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
							    }			,     opset=_UpperCamelCase			,     )

							# VAE DECODER
							__UpperCAmelCase  :		List[str] 				=   pipeline.vae
							__UpperCAmelCase  :		str 				=   vae_decoder.config.latent_channels
							__UpperCAmelCase  :		str 				=   vae_decoder.config.out_channels
							# forward only through the decoder part
							__UpperCAmelCase  :		Any 				=   vae_encoder.decode
							onnx_export(
							    _UpperCamelCase			,     model_args=(
							        torch.randn(1			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase				).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 pipeline.vae

							# SAFETY CHECKER
							if pipeline.safety_checker is not None:
														__UpperCAmelCase  :		Any 				=   pipeline.safety_checker
														__UpperCAmelCase  :		List[str] 				=   safety_checker.config.vision_config.num_channels
														__UpperCAmelCase  :		str 				=   safety_checker.config.vision_config.image_size
														__UpperCAmelCase  :		Optional[Any] 				=   safety_checker.forward_onnx
														onnx_export(
														    pipeline.safety_checker			,     model_args=(
														        torch.randn(
														            1			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     ).to(device=_UpperCamelCase			,     dtype=_UpperCamelCase				),
														        torch.randn(1			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase				).to(device=_UpperCamelCase			,     dtype=_UpperCamelCase				),
														    )			,     output_path=output_path / """safety_checker""" / """model.onnx"""			,     ordered_input_names=["""clip_input""", """images"""]			,     output_names=["""out_images""", """has_nsfw_concepts"""]			,     dynamic_axes={
														        """clip_input""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
														        """images""": {0: """batch""", 1: """height""", 2: """width""", 3: """channels"""},
														    }			,     opset=_UpperCamelCase			,     )
														del pipeline.safety_checker
														__UpperCAmelCase  :		Optional[int] 				=   OnnxRuntimeModel.from_pretrained(output_path / """safety_checker"""				)
														__UpperCAmelCase  :		List[str] 				=   pipeline.feature_extractor
							else:
														__UpperCAmelCase  :		int 				=   None
														__UpperCAmelCase  :		Optional[int] 				=   None

							__UpperCAmelCase  :		Any 				=   OnnxStableDiffusionPipeline(
							    vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / """vae_encoder"""				)			,     vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / """vae_decoder"""				)			,     text_encoder=OnnxRuntimeModel.from_pretrained(output_path / """text_encoder"""				)			,     tokenizer=pipeline.tokenizer			,     unet=OnnxRuntimeModel.from_pretrained(output_path / """unet"""				)			,     scheduler=pipeline.scheduler			,     safety_checker=_UpperCamelCase			,     feature_extractor=_UpperCamelCase			,     requires_safety_checker=safety_checker is not None			,     )

							onnx_pipeline.save_pretrained(_UpperCamelCase				)
							print("""ONNX pipeline saved to"""			,     _UpperCamelCase				)

							del pipeline
							del onnx_pipeline
							__UpperCAmelCase  :		List[Any] 				=   OnnxStableDiffusionPipeline.from_pretrained(_UpperCamelCase			,     provider="""CPUExecutionProvider"""				)
							print("""ONNX pipeline is loadable"""				)


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=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       :	Optional[int]  		=						parser.parse_args()

							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)








"""simple docstring"""
import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


lowerCAmelCase__    						=			threading.Lock()
lowerCAmelCase__    						=			None

lowerCAmelCase__    						=			{
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

lowerCAmelCase__    						=			logging.WARNING

lowerCAmelCase__    						=			True





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		int       		=    os.getenv('TRANSFORMERS_VERBOSITY',						lowerCamelCase       )
				if env_level_str:
								if env_level_str in log_levels:
												return log_levels[env_level_str]
								else:
												logging.getLogger().warning(
												    f"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
												    f"""has to be one of: { ", ".join(log_levels.keys()       ) }"""       )
				return _default_log_level





def 				lowercase__		(					):
				return __name__.split('.'       )[0]





def 				lowercase__		(					):
				return logging.getLogger(_get_library_name()       )





def 				lowercase__		(					):
				global _default_handler

				with _lock:
								if _default_handler:
												# This library has already configured the library root logger.
												return
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    logging.StreamHandler()  # Set sys.stderr as stream.
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    sys.stderr.flush

								# Apply our default configuration to the library root logger.
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    _get_library_root_logger()
								library_root_logger.addHandler(_default_handler       )
								library_root_logger.setLevel(_get_default_logging_level()       )
								_SCREAMING_SNAKE_CASE   :		int       		=    False





def 				lowercase__		(					):
				global _default_handler

				with _lock:
								if not _default_handler:
												return

								_SCREAMING_SNAKE_CASE   :		Any       		=    _get_library_root_logger()
								library_root_logger.removeHandler(_default_handler       )
								library_root_logger.setLevel(logging.NOTSET       )
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    None





def 				lowercase__		(					):
				return log_levels





def 				lowercase__		(					lowerCamelCase = None       ):

				if name is None:
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    _get_library_name()

				_configure_library_root_logger()
				return logging.getLogger(lowerCamelCase       )





def 				lowercase__		(					):
				_configure_library_root_logger()
				return _get_library_root_logger().getEffectiveLevel()





def 				lowercase__		(					lowerCamelCase       ):
				_configure_library_root_logger()
				_get_library_root_logger().setLevel(lowerCamelCase       )





def 				lowercase__		(					):
				return set_verbosity(lowerCamelCase       )





def 				lowercase__		(					):
				return set_verbosity(lowerCamelCase       )





def 				lowercase__		(					):
				return set_verbosity(lowerCamelCase       )





def 				lowercase__		(					):
				return set_verbosity(lowerCamelCase       )





def 				lowercase__		(					):
				_configure_library_root_logger()

				assert _default_handler is not None
				_get_library_root_logger().removeHandler(_default_handler       )





def 				lowercase__		(					):
				_configure_library_root_logger()

				assert _default_handler is not None
				_get_library_root_logger().addHandler(_default_handler       )





def 				lowercase__		(					lowerCamelCase       ):
				_configure_library_root_logger()

				assert handler is not None
				_get_library_root_logger().addHandler(lowerCamelCase       )





def 				lowercase__		(					lowerCamelCase       ):
				_configure_library_root_logger()

				assert handler is not None and handler not in _get_library_root_logger().handlers
				_get_library_root_logger().removeHandler(lowerCamelCase       )





def 				lowercase__		(					):
				_configure_library_root_logger()
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    False





def 				lowercase__		(					):
				_configure_library_root_logger()
				_SCREAMING_SNAKE_CASE   :		int       		=    True





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		Any       		=    _get_library_root_logger().handlers

				for handler in handlers:
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    logging.Formatter('[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'       )
								handler.setFormatter(lowerCamelCase       )





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    _get_library_root_logger().handlers

				for handler in handlers:
								handler.setFormatter(lowerCamelCase       )





def 				lowercase__		(					self,						*lowerCamelCase,						**lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		str       		=    os.getenv('TRANSFORMERS_NO_ADVISORY_WARNINGS',						lowerCamelCase       )
				if no_advisory_warnings:
								return
				self.warning(*lowerCamelCase,						**lowerCamelCase       )


lowerCAmelCase__    						=			warning_advice





@functools.lru_cache(lowerCamelCase       )
def 				lowercase__		(					self,						*lowerCamelCase,						**lowerCamelCase       ):
				self.warning(*lowerCamelCase,						**lowerCamelCase       )


lowerCAmelCase__    						=			warning_once


class _lowerCAmelCase						:







						def __init__(    self   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							Optional[int]:  # pylint: disable=unused-argument
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    args[0] if args else None







						def __iter__(    self							)		->							Dict:
										return iter(self._iterator							)







						def __getattr__(    self   ,				lowerCAmelCase_							)		->							Tuple:

										def empty_fn(*lowerCAmelCase_   ,				**lowerCAmelCase_							):  # pylint: disable=unused-argument
														return

										return empty_fn







						def __enter__(    self							)		->							List[Any]:
										return self







						def __exit__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Optional[Any]:
										return







class _lowerCAmelCase						:







						def __call__(    self   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							Optional[Any]:
										if _tqdm_active:
														return tqdm_lib.tqdm(*lowerCAmelCase_   ,				**lowerCAmelCase_							)
										else:
														return EmptyTqdm(*lowerCAmelCase_   ,				**lowerCAmelCase_							)







						def 			A      (    self   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    None
										if _tqdm_active:
														return tqdm_lib.tqdm.set_lock(*lowerCAmelCase_   ,				**lowerCAmelCase_							)







						def 			A      (    self							)		->							Any:
										if _tqdm_active:
														return tqdm_lib.tqdm.get_lock()







lowerCAmelCase__    						=			_tqdm_cls()





def 				lowercase__		(					):
				global _tqdm_active
				return bool(_tqdm_active       )





def 				lowercase__		(					):
				global _tqdm_active
				_SCREAMING_SNAKE_CASE   :		int       		=    True
				hf_hub_utils.enable_progress_bars()






def 				lowercase__		(					):
				global _tqdm_active
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    False
				hf_hub_utils.disable_progress_bars()

