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


def    lowerCamelCase       (			UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  tuple				,						UpperCAmelCase__				:  Path				,						UpperCAmelCase__				:  Dict				,						UpperCAmelCase__				:  List[Any]				,						UpperCAmelCase__				:  Tuple				,						UpperCAmelCase__				:  Tuple				,						UpperCAmelCase__				:  Union[str, Any]=False				,						)			->	Optional[Any]:


							'''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__				:  str				,						UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  bool = False		)			->	str:


							'''simple docstring'''






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

							# TEXT ENCODER
							SCREAMING_SNAKE_CASE__     :List[Any]						    = pipeline.text_encoder.config.max_position_embeddings
							SCREAMING_SNAKE_CASE__     :List[Any]						    = pipeline.text_encoder.config.hidden_size
							SCREAMING_SNAKE_CASE__     :Any						    = 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
							SCREAMING_SNAKE_CASE__     :int						    = pipeline.unet.config.in_channels
							SCREAMING_SNAKE_CASE__     :Any						    = pipeline.unet.config.sample_size
							SCREAMING_SNAKE_CASE__     :str						    = 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__				,						)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = str(unet_path.absolute().as_posix()		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = os.path.dirname(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :List[str]						    = 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
							SCREAMING_SNAKE_CASE__     :int						    = pipeline.vae
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = vae_encoder.config.in_channels
							SCREAMING_SNAKE_CASE__     :str						    = vae_encoder.config.sample_size
							# need to get the raw tensor output (sample) from the encoder
							SCREAMING_SNAKE_CASE__     :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
							SCREAMING_SNAKE_CASE__     :Any						    = pipeline.vae
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = vae_decoder.config.latent_channels
							SCREAMING_SNAKE_CASE__     :List[str]						    = vae_decoder.config.out_channels
							# forward only through the decoder part
							SCREAMING_SNAKE_CASE__     :str						    = 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:
														SCREAMING_SNAKE_CASE__     :Tuple						    = pipeline.safety_checker
														SCREAMING_SNAKE_CASE__     :int						    = safety_checker.config.vision_config.num_channels
														SCREAMING_SNAKE_CASE__     :Dict						    = safety_checker.config.vision_config.image_size
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'		)
														SCREAMING_SNAKE_CASE__     :List[str]						    = pipeline.feature_extractor
							else:
														SCREAMING_SNAKE_CASE__     :int						    = None
														SCREAMING_SNAKE_CASE__     :List[Any]						    = None

							SCREAMING_SNAKE_CASE__     :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
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = OnnxStableDiffusionPipeline.from_pretrained(UpperCAmelCase__				,						provider='CPUExecutionProvider'		)
							print('ONNX pipeline is loadable'		)


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()

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







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_      	=	{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






