

'''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


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




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_=False  ,       ):
 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(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_ = False	):
 UpperCAmelCase       :	Union[str, Any]											= torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  UpperCAmelCase       :	int											= 'cuda'
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('`float16` model export is only supported on GPUs with CUDA'	)
 else:
  UpperCAmelCase       :	Tuple											= 'cpu'
 UpperCAmelCase       :	int											= StableDiffusionPipeline.from_pretrained(__lowercase  ,       torch_dtype=__lowercase	).to(__lowercase	)
 UpperCAmelCase       :	int											= Path(__lowercase	)

 # TEXT ENCODER
 UpperCAmelCase       :	Dict											= pipeline.text_encoder.config.max_position_embeddings
 UpperCAmelCase       :	Optional[int]											= pipeline.text_encoder.config.hidden_size
 UpperCAmelCase       :	str											= 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
 UpperCAmelCase       :	Union[str, Any]											= pipeline.unet.config.in_channels
 UpperCAmelCase       :	List[str]											= pipeline.unet.config.sample_size
 UpperCAmelCase       :	Dict											= 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  ,       )
 UpperCAmelCase       :	Dict											= str(unet_path.absolute().as_posix()	)
 UpperCAmelCase       :	Any											= os.path.dirname(__lowercase	)
 UpperCAmelCase       :	int											= 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
 UpperCAmelCase       :	List[Any]											= pipeline.vae
 UpperCAmelCase       :	Optional[Any]											= 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       :	Any											= lambda UpperCAmelCase_  ,       UpperCAmelCase_	: 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
 UpperCAmelCase       :	List[Any]											= pipeline.vae
 UpperCAmelCase       :	Optional[int]											= vae_decoder.config.latent_channels
 UpperCAmelCase       :	List[str]											= vae_decoder.config.out_channels
 # forward only through the decoder part
 UpperCAmelCase       :	Dict											= 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:
  UpperCAmelCase       :	int											= pipeline.safety_checker
  UpperCAmelCase       :	Any											= safety_checker.config.vision_config.num_channels
  UpperCAmelCase       :	Union[str, Any]											= safety_checker.config.vision_config.image_size
  UpperCAmelCase       :	str											= 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
  UpperCAmelCase       :	str											= OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'	)
  UpperCAmelCase       :	str											= pipeline.feature_extractor
 else:
  UpperCAmelCase       :	Dict											= None
  UpperCAmelCase       :	Union[str, Any]											= None

 UpperCAmelCase       :	List[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=__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
 UpperCAmelCase       :	Optional[int]											= OnnxStableDiffusionPipeline.from_pretrained(__lowercase  ,       provider='CPUExecutionProvider'	)
 print('ONNX pipeline is loadable'	)


if __name__ == "__main__":
 lowercase__			    =			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")

 lowercase__			    =			parser.parse_args()

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






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


_lowerCamelCase       :				Dict       =			logging.get_logger(__name__)




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    Tuple			,  *lowercase	:    Optional[int]			,  **lowercase	:    Any	):


				'''simple docstring'''


				warnings.warn(
				    'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
				    ' Please use ChineseCLIPImageProcessor instead.'			,  lowercase			,  )
				super().__init__(*lowercase			,  **lowercase	)