





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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False			,      )						->  Tuple:
				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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = False				)						->  int:
				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	 =StableDiffusionPipeline.from_pretrained(_UpperCAmelCase			,      torch_dtype=_UpperCAmelCase				).to(_UpperCAmelCase				)
				lowerCamelCase	 =Path(_UpperCAmelCase				)

				# TEXT ENCODER
				lowerCamelCase	 =pipeline.text_encoder.config.max_position_embeddings
				lowerCamelCase	 =pipeline.text_encoder.config.hidden_size
				lowerCamelCase	 =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
				lowerCamelCase	 =pipeline.unet.config.in_channels
				lowerCamelCase	 =pipeline.unet.config.sample_size
				lowerCamelCase	 =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			,      )
				lowerCamelCase	 =str(unet_path.absolute().as_posix()				)
				lowerCamelCase	 =os.path.dirname(_UpperCAmelCase				)
				lowerCamelCase	 =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
				lowerCamelCase	 =pipeline.vae
				lowerCamelCase	 =vae_encoder.config.in_channels
				lowerCamelCase	 =vae_encoder.config.sample_size
				# need to get the raw tensor output (sample) from the encoder
				lowerCamelCase	 =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
				lowerCamelCase	 =pipeline.vae
				lowerCamelCase	 =vae_decoder.config.latent_channels
				lowerCamelCase	 =vae_decoder.config.out_channels
				# forward only through the decoder part
				lowerCamelCase	 =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:
								lowerCamelCase	 =pipeline.safety_checker
								lowerCamelCase	 =safety_checker.config.vision_config.num_channels
								lowerCamelCase	 =safety_checker.config.vision_config.image_size
								lowerCamelCase	 =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
								lowerCamelCase	 =OnnxRuntimeModel.from_pretrained(output_path / """safety_checker"""				)
								lowerCamelCase	 =pipeline.feature_extractor
				else:
								lowerCamelCase	 =None
								lowerCamelCase	 =None

				lowerCamelCase	 =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
				lowerCamelCase	 =OnnxStableDiffusionPipeline.from_pretrained(_UpperCAmelCase			,      provider="""CPUExecutionProvider"""				)
				print("""ONNX pipeline is loadable"""				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=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__						:			Tuple									=parser.parse_args()

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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__						:			str									={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			List[Any]									=['''FNetTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Any									=['''FNetTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Dict									=[
							    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FNetForMaskedLM''',
							    '''FNetForMultipleChoice''',
							    '''FNetForNextSentencePrediction''',
							    '''FNetForPreTraining''',
							    '''FNetForQuestionAnswering''',
							    '''FNetForSequenceClassification''',
							    '''FNetForTokenClassification''',
							    '''FNetLayer''',
							    '''FNetModel''',
							    '''FNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet import FNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet_fast import FNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_fnet import (
														    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FNetForMaskedLM,
														    FNetForMultipleChoice,
														    FNetForNextSentencePrediction,
														    FNetForPreTraining,
														    FNetForQuestionAnswering,
														    FNetForSequenceClassification,
														    FNetForTokenClassification,
														    FNetLayer,
														    FNetModel,
														    FNetPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__						:			Optional[int]									=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





