






'''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   :							int         =   version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						tuple,       SCREAMING_SNAKE_CASE__:						Path,       SCREAMING_SNAKE_CASE__:						Dict,       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						Dict,       SCREAMING_SNAKE_CASE__:						Optional[int]=False,       )	->      Union[str, Any]:

   """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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						bool = False     )	->      Optional[Any]:

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

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

   __a					  =     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
   __a					  =     OnnxStableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__,       provider='CPUExecutionProvider'     )
   print('ONNX pipeline is loadable'     )


if __name__ == "__main__":
 __UpperCamelCase   :							List[Any]         =   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   :							List[str]         =   parser.parse_args()

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







'''simple docstring'''



import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Dict     )	->      List[Any]:

   """simple docstring"""
   # Initialise PyTorch model
   __a					  =     RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
   print('Building PyTorch model from configuration: {}'.format(str(SCREAMING_SNAKE_CASE__     )     )     )
   __a					  =     RemBertModel(SCREAMING_SNAKE_CASE__     )

   # Load weights from tf checkpoint
   load_tf_weights_in_rembert(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

   # Save pytorch-model
   print('Save PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__     )     )
   torch.save(model.state_dict(),       SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 __UpperCamelCase   :							List[str]         =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--rembert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained RemBERT model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 __UpperCamelCase   :							List[str]         =   parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)