


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


__snake_case            =   version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		tuple       ,  SCREAMING_SNAKE_CASE_   :		Path       ,  SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		Any       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]=False       ,  ):





			"""simple docstring"""




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






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		bool = False	):





			"""simple docstring"""




			UpperCamelCase         =					torch.floataa if fpaa else torch.floataa
			if fpaa and torch.cuda.is_available():
						UpperCamelCase         =					'''cuda'''
			elif fpaa and not torch.cuda.is_available():
						raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""	)
			else:
						UpperCamelCase         =					'''cpu'''
			UpperCamelCase         =					StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_       ,  torch_dtype=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					Path(SCREAMING_SNAKE_CASE_	)

			# TEXT ENCODER
			UpperCamelCase         =					pipeline.text_encoder.config.max_position_embeddings
			UpperCamelCase         =					pipeline.text_encoder.config.hidden_size
			UpperCamelCase         =					pipeline.tokenizer(
			    """A sample prompt"""       ,  padding="""max_length"""       ,  max_length=pipeline.tokenizer.model_max_length       ,  truncation=SCREAMING_SNAKE_CASE_       ,  return_tensors="""pt"""       ,  )
			onnx_export(
			    pipeline.text_encoder       ,  model_args=(text_input.input_ids.to(device=SCREAMING_SNAKE_CASE_       ,  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=SCREAMING_SNAKE_CASE_       ,  )
			del pipeline.text_encoder

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

			# VAE ENCODER
			UpperCamelCase         =					pipeline.vae
			UpperCamelCase         =					vae_encoder.config.in_channels
			UpperCamelCase         =					vae_encoder.config.sample_size
			# need to get the raw tensor output (sample) from the encoder
			UpperCamelCase         =					lambda SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	: vae_encoder.encode(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)[0].sample()
			onnx_export(
			    SCREAMING_SNAKE_CASE_       ,  model_args=(
			        torch.randn(1       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	).to(device=SCREAMING_SNAKE_CASE_       ,  dtype=SCREAMING_SNAKE_CASE_	),
			        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=SCREAMING_SNAKE_CASE_       ,  )

			# VAE DECODER
			UpperCamelCase         =					pipeline.vae
			UpperCamelCase         =					vae_decoder.config.latent_channels
			UpperCamelCase         =					vae_decoder.config.out_channels
			# forward only through the decoder part
			UpperCamelCase         =					vae_encoder.decode
			onnx_export(
			    SCREAMING_SNAKE_CASE_       ,  model_args=(
			        torch.randn(1       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	).to(device=SCREAMING_SNAKE_CASE_       ,  dtype=SCREAMING_SNAKE_CASE_	),
			        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=SCREAMING_SNAKE_CASE_       ,  )
			del pipeline.vae

			# SAFETY CHECKER
			if pipeline.safety_checker is not None:
						UpperCamelCase         =					pipeline.safety_checker
						UpperCamelCase         =					safety_checker.config.vision_config.num_channels
						UpperCamelCase         =					safety_checker.config.vision_config.image_size
						UpperCamelCase         =					safety_checker.forward_onnx
						onnx_export(
						    pipeline.safety_checker       ,  model_args=(
						        torch.randn(
						            1       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  ).to(device=SCREAMING_SNAKE_CASE_       ,  dtype=SCREAMING_SNAKE_CASE_	),
						        torch.randn(1       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	).to(device=SCREAMING_SNAKE_CASE_       ,  dtype=SCREAMING_SNAKE_CASE_	),
						    )       ,  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=SCREAMING_SNAKE_CASE_       ,  )
						del pipeline.safety_checker
						UpperCamelCase         =					OnnxRuntimeModel.from_pretrained(output_path / """safety_checker"""	)
						UpperCamelCase         =					pipeline.feature_extractor
			else:
						UpperCamelCase         =					None
						UpperCamelCase         =					None

			UpperCamelCase         =					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=SCREAMING_SNAKE_CASE_       ,  feature_extractor=SCREAMING_SNAKE_CASE_       ,  requires_safety_checker=safety_checker is not None       ,  )

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

			del pipeline
			del onnx_pipeline
			UpperCamelCase         =					OnnxStableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_       ,  provider="""CPUExecutionProvider"""	)
			print("""ONNX pipeline is loadable"""	)


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()

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








import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= ["""image_processor""", """tokenizer"""]
			lowercase						= """FlavaImageProcessor"""
			lowercase						= ("""BertTokenizer""", """BertTokenizerFast""")




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		str=None       ,       **__magic_name__							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""       ,       __magic_name__       ,       )
									UpperCamelCase         =					kwargs.pop("""feature_extractor"""						)

						UpperCamelCase         =					image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError("""You need to specify an `image_processor`."""						)
						if tokenizer is None:
									raise ValueError("""You need to specify a `tokenizer`."""						)

						super().__init__(__magic_name__       ,       __magic_name__						)
						UpperCamelCase         =					self.image_processor




			def __call__(						self							:		int       ,       __magic_name__							:		Optional[ImageInput] = None       ,       __magic_name__							:		Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Union[bool, str, PaddingStrategy] = False       ,       __magic_name__							:		Union[bool, str, TruncationStrategy] = False       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		int = 0       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Optional[Union[str, TensorType]] = None       ,       **__magic_name__							:		str       ,       ):







						"""simple docstring"""







						if text is None and images is None:
									raise ValueError("""You have to specify either text or images. Both cannot be none."""						)

						if text is not None:
									UpperCamelCase         =					self.tokenizer(
									    text=__magic_name__       ,       add_special_tokens=__magic_name__       ,       padding=__magic_name__       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       stride=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_token_type_ids=__magic_name__       ,       return_attention_mask=__magic_name__       ,       return_overflowing_tokens=__magic_name__       ,       return_special_tokens_mask=__magic_name__       ,       return_offsets_mapping=__magic_name__       ,       return_length=__magic_name__       ,       verbose=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )
						if images is not None:
									UpperCamelCase         =					self.image_processor(
									    __magic_name__       ,       return_image_mask=__magic_name__       ,       return_codebook_pixels=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )

						if text is not None and images is not None:
									encoding.update(__magic_name__						)
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**__magic_name__						)       ,       tensor_type=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Tuple       ,       *__magic_name__							:		Dict       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.tokenizer.batch_decode(*__magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		int       ,       *__magic_name__							:		Tuple       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						return self.tokenizer.decode(*__magic_name__       ,       **__magic_name__						)




			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					self.tokenizer.model_input_names
						UpperCamelCase         =					self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,       __magic_name__       ,       )
						return self.image_processor_class





			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,       __magic_name__       ,       )
						return self.image_processor




