



'''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


_A				:    Optional[Any]     						=version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase=False,				) ->			Tuple:
	output_path.parent.mkdir(parents=_lowercase,				exist_ok=_lowercase					)
	# 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(
		    _lowercase,				_lowercase,				f=output_path.as_posix(),				input_names=_lowercase,				output_names=_lowercase,				dynamic_axes=_lowercase,				do_constant_folding=_lowercase,				use_external_data_format=_lowercase,				enable_onnx_checker=_lowercase,				opset_version=_lowercase,				)
	else:
		export(
		    _lowercase,				_lowercase,				f=output_path.as_posix(),				input_names=_lowercase,				output_names=_lowercase,				dynamic_axes=_lowercase,				do_constant_folding=_lowercase,				opset_version=_lowercase,				)







@torch.no_grad()
def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase = False					) ->			Dict:
	_lowercase					:       Dict       =		torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		_lowercase					:       str       =		'''cuda'''
	elif fpaa and not torch.cuda.is_available():
		raise ValueError('`float16` model export is only supported on GPUs with CUDA'					)
	else:
		_lowercase					:       int       =		'''cpu'''
	_lowercase					:       Dict       =		StableDiffusionPipeline.from_pretrained(_lowercase,				torch_dtype=_lowercase					).to(_lowercase					)
	_lowercase					:       str       =		Path(_lowercase					)

	# TEXT ENCODER
	_lowercase					:       List[str]       =		pipeline.text_encoder.config.max_position_embeddings
	_lowercase					:       Any       =		pipeline.text_encoder.config.hidden_size
	_lowercase					:       Tuple       =		pipeline.tokenizer(
	    'A sample prompt',				padding='max_length',				max_length=pipeline.tokenizer.model_max_length,				truncation=_lowercase,				return_tensors='pt',				)
	onnx_export(
	    pipeline.text_encoder,				model_args=(text_input.input_ids.to(device=_lowercase,				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=_lowercase,				)
	del pipeline.text_encoder

	# UNET
	_lowercase					:       Any       =		pipeline.unet.config.in_channels
	_lowercase					:       Tuple       =		pipeline.unet.config.sample_size
	_lowercase					:       Union[str, Any]       =		output_path / '''unet''' / '''model.onnx'''
	onnx_export(
	    pipeline.unet,				model_args=(
	        torch.randn(2,				_lowercase,				_lowercase,				_lowercase					).to(device=_lowercase,				dtype=_lowercase					),
	        torch.randn(2					).to(device=_lowercase,				dtype=_lowercase					),
	        torch.randn(2,				_lowercase,				_lowercase					).to(device=_lowercase,				dtype=_lowercase					),
	        False,
	    ),				output_path=_lowercase,				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=_lowercase,				use_external_data_format=_lowercase,				)
	_lowercase					:       Any       =		str(unet_path.absolute().as_posix()					)
	_lowercase					:       Any       =		os.path.dirname(_lowercase					)
	_lowercase					:       Any       =		onnx.load(_lowercase					)
	# clean up existing tensor files
	shutil.rmtree(_lowercase					)
	os.mkdir(_lowercase					)
	# collate external tensor files into one
	onnx.save_model(
	    _lowercase,				_lowercase,				save_as_external_data=_lowercase,				all_tensors_to_one_file=_lowercase,				location='weights.pb',				convert_attribute=_lowercase,				)
	del pipeline.unet

	# VAE ENCODER
	_lowercase					:       Dict       =		pipeline.vae
	_lowercase					:       Optional[int]       =		vae_encoder.config.in_channels
	_lowercase					:       Union[str, Any]       =		vae_encoder.config.sample_size
	# need to get the raw tensor output (sample) from the encoder
	_lowercase					:       List[str]       =		lambda _lowercase,				_lowercase					: vae_encoder.encode(_lowercase,				_lowercase					)[0].sample()
	onnx_export(
	    _lowercase,				model_args=(
	        torch.randn(1,				_lowercase,				_lowercase,				_lowercase					).to(device=_lowercase,				dtype=_lowercase					),
	        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=_lowercase,				)

	# VAE DECODER
	_lowercase					:       List[Any]       =		pipeline.vae
	_lowercase					:       Tuple       =		vae_decoder.config.latent_channels
	_lowercase					:       List[str]       =		vae_decoder.config.out_channels
	# forward only through the decoder part
	_lowercase					:       Optional[Any]       =		vae_encoder.decode
	onnx_export(
	    _lowercase,				model_args=(
	        torch.randn(1,				_lowercase,				_lowercase,				_lowercase					).to(device=_lowercase,				dtype=_lowercase					),
	        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=_lowercase,				)
	del pipeline.vae

	# SAFETY CHECKER
	if pipeline.safety_checker is not None:
		_lowercase					:       int       =		pipeline.safety_checker
		_lowercase					:       str       =		safety_checker.config.vision_config.num_channels
		_lowercase					:       int       =		safety_checker.config.vision_config.image_size
		_lowercase					:       Optional[Any]       =		safety_checker.forward_onnx
		onnx_export(
		    pipeline.safety_checker,				model_args=(
		        torch.randn(
		            1,				_lowercase,				_lowercase,				_lowercase,				).to(device=_lowercase,				dtype=_lowercase					),
		        torch.randn(1,				_lowercase,				_lowercase,				_lowercase					).to(device=_lowercase,				dtype=_lowercase					),
		    ),				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=_lowercase,				)
		del pipeline.safety_checker
		_lowercase					:       List[Any]       =		OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'					)
		_lowercase					:       List[str]       =		pipeline.feature_extractor
	else:
		_lowercase					:       Union[str, Any]       =		None
		_lowercase					:       int       =		None

	_lowercase					:       List[str]       =		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=_lowercase,				feature_extractor=_lowercase,				requires_safety_checker=safety_checker is not None,				)

	onnx_pipeline.save_pretrained(_lowercase					)
	print('ONNX pipeline saved to',				_lowercase					)

	del pipeline
	del onnx_pipeline
	_lowercase					:       Tuple       =		OnnxStableDiffusionPipeline.from_pretrained(_lowercase,				provider='CPUExecutionProvider'					)
	print('ONNX pipeline is loadable'					)


if __name__ == "__main__":
						_A				:    str     						=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''')

						_A				:    List[str]     						=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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A				:    Optional[Any]     						={'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Tuple     						=['''XLNetTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=['''XLNetTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Any     						=[
		    '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XLNetForMultipleChoice''',
		    '''XLNetForQuestionAnswering''',
		    '''XLNetForQuestionAnsweringSimple''',
		    '''XLNetForSequenceClassification''',
		    '''XLNetForTokenClassification''',
		    '''XLNetLMHeadModel''',
		    '''XLNetModel''',
		    '''XLNetPreTrainedModel''',
		    '''load_tf_weights_in_xlnet''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=[
		    '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFXLNetForMultipleChoice''',
		    '''TFXLNetForQuestionAnsweringSimple''',
		    '''TFXLNetForSequenceClassification''',
		    '''TFXLNetForTokenClassification''',
		    '''TFXLNetLMHeadModel''',
		    '''TFXLNetMainLayer''',
		    '''TFXLNetModel''',
		    '''TFXLNetPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlnet import XLNetTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlnet_fast import XLNetTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlnet import (
				    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLNetForMultipleChoice,
				    XLNetForQuestionAnswering,
				    XLNetForQuestionAnsweringSimple,
				    XLNetForSequenceClassification,
				    XLNetForTokenClassification,
				    XLNetLMHeadModel,
				    XLNetModel,
				    XLNetPreTrainedModel,
				    load_tf_weights_in_xlnet,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xlnet import (
				    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXLNetForMultipleChoice,
				    TFXLNetForQuestionAnsweringSimple,
				    TFXLNetForSequenceClassification,
				    TFXLNetForTokenClassification,
				    TFXLNetLMHeadModel,
				    TFXLNetMainLayer,
				    TFXLNetModel,
				    TFXLNetPreTrainedModel,
				)

else:
		import sys

		_A				:    str     						=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
