





"""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


lowerCAmelCase_			=							version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=False,)		->       List[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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = False							)		->       Any:






       """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__":
    lowerCAmelCase_			=							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''')

    lowerCAmelCase_			=							parser.parse_args()

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













"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """philschmid/bart-large-cnn-samsum"""
       lowerCAmelCase__						 =  (
           """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
           """and returns a summary of the text."""
       )
       lowerCAmelCase__						 =  """summarizer"""
       lowerCAmelCase__						 =  AutoTokenizer
       lowerCAmelCase__						 =  AutoModelForSeqaSeqLM

       lowerCAmelCase__						 =  ["""text"""]
       lowerCAmelCase__						 =  ["""text"""]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor(a__    ,  return_tensors='pt'    ,  truncation=a__     )






       def 							__A (   self    ,  a__     ):
              return self.model.generate(**a__     )[0]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor.decode(a__    ,  skip_special_tokens=a__    ,  clean_up_tokenization_spaces=a__     )






