






"""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_     :				Tuple         =				version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")



def       _A    (__a					,       __a					,       __a					,       __a					,       __a					,       __a					,       __a					,       __a=False					,       )				-> str:
    """simple docstring"""






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



@torch.no_grad()
def       _A    (__a					,       __a					,       __a					,       __a = False							)				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	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_   :						Tuple							=	'''cpu'''
    SCREAMING_SNAKE_CASE_   :						Any							=	StableDiffusionPipeline.from_pretrained(__a					,       torch_dtype=__a							).to(__a							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	Path(__a							)

    # TEXT ENCODER
    SCREAMING_SNAKE_CASE_   :						Dict							=	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=__a					,       return_tensors='''pt'''					,       )
    onnx_export(
        pipeline.text_encoder					,       model_args=(text_input.input_ids.to(device=__a					,       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=__a					,       )
    del pipeline.text_encoder

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

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

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

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

    SCREAMING_SNAKE_CASE_   :						Tuple							=	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=__a					,       feature_extractor=__a					,       requires_safety_checker=safety_checker is not None					,       )

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

    del pipeline
    del onnx_pipeline
    SCREAMING_SNAKE_CASE_   :						Any							=	OnnxStableDiffusionPipeline.from_pretrained(__a					,       provider='''CPUExecutionProvider'''							)
    print('''ONNX pipeline is loadable'''							)


if __name__ == "__main__":
  UpperCAmelCase_     :				List[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=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_     :				Any         =				parser.parse_args()

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










"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_     :				List[str]         =				logging.get_logger(__name__)

UpperCAmelCase_     :				Dict         =				{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						"data2vec-text"
      def __init__(		self  :						Any		,			lowercase_  :						Any=30522		,			lowercase_  :						Any=768		,			lowercase_  :						Union[str, Any]=12		,			lowercase_  :						Dict=12		,			lowercase_  :						List[Any]=3072		,			lowercase_  :						str="gelu"		,			lowercase_  :						int=0.1		,			lowercase_  :						Dict=0.1		,			lowercase_  :						str=512		,			lowercase_  :						Optional[int]=2		,			lowercase_  :						int=0.02		,			lowercase_  :						int=1e-12		,			lowercase_  :						Any=1		,			lowercase_  :						Any=0		,			lowercase_  :						List[Any]=2		,			lowercase_  :						Tuple="absolute"		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						int=None		,			**lowercase_  :						Tuple		,			):




          '''simple docstring'''


          super().__init__(pad_token_id=lowercase_		,			bos_token_id=lowercase_		,			eos_token_id=lowercase_		,			**lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	vocab_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	hidden_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	num_hidden_layers
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	num_attention_heads
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	hidden_act
          SCREAMING_SNAKE_CASE_   :						Any							=	intermediate_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Tuple							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	type_vocab_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	initializer_range
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	layer_norm_eps
          SCREAMING_SNAKE_CASE_   :						str							=	position_embedding_type
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	use_cache
          SCREAMING_SNAKE_CASE_   :						str							=	classifier_dropout




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          if self.task == "multiple-choice":
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              SCREAMING_SNAKE_CASE_   :						Any							=	{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ])


