

'''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  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__=False,    )     ->     List[str]:
       output_path.parent.mkdir(parents=UpperCAmelCase__,    exist_ok=UpperCAmelCase__			)
       # 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(
                  UpperCAmelCase__,    UpperCAmelCase__,    f=output_path.as_posix(),    input_names=UpperCAmelCase__,    output_names=UpperCAmelCase__,    dynamic_axes=UpperCAmelCase__,    do_constant_folding=UpperCAmelCase__,    use_external_data_format=UpperCAmelCase__,    enable_onnx_checker=UpperCAmelCase__,    opset_version=UpperCAmelCase__,    )
       else:
              export(
                  UpperCAmelCase__,    UpperCAmelCase__,    f=output_path.as_posix(),    input_names=UpperCAmelCase__,    output_names=UpperCAmelCase__,    dynamic_axes=UpperCAmelCase__,    do_constant_folding=UpperCAmelCase__,    opset_version=UpperCAmelCase__,    )




@torch.no_grad()
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__ = False			)     ->     str:
       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(UpperCAmelCase__,    torch_dtype=UpperCAmelCase__			).to(UpperCAmelCase__			)
       A_		   = Path(UpperCAmelCase__			)

       # 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=UpperCAmelCase__,    return_tensors="""pt""",    )
       onnx_export(
           pipeline.text_encoder,    model_args=(text_input.input_ids.to(device=UpperCAmelCase__,    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=UpperCAmelCase__,    )
       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,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			).to(device=UpperCAmelCase__,    dtype=UpperCAmelCase__			),
               torch.randn(2			).to(device=UpperCAmelCase__,    dtype=UpperCAmelCase__			),
               torch.randn(2,    UpperCAmelCase__,    UpperCAmelCase__			).to(device=UpperCAmelCase__,    dtype=UpperCAmelCase__			),
               False,
           ),    output_path=UpperCAmelCase__,    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=UpperCAmelCase__,    use_external_data_format=UpperCAmelCase__,    )
       A_		   = str(unet_path.absolute().as_posix()			)
       A_		   = os.path.dirname(UpperCAmelCase__			)
       A_		   = onnx.load(UpperCAmelCase__			)
       # clean up existing tensor files
       shutil.rmtree(UpperCAmelCase__			)
       os.mkdir(UpperCAmelCase__			)
       # collate external tensor files into one
       onnx.save_model(
           UpperCAmelCase__,    UpperCAmelCase__,    save_as_external_data=UpperCAmelCase__,    all_tensors_to_one_file=UpperCAmelCase__,    location="""weights.pb""",    convert_attribute=UpperCAmelCase__,    )
       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 UpperCAmelCase__,    UpperCAmelCase__			: vae_encoder.encode(UpperCAmelCase__,    UpperCAmelCase__			)[0].sample()
       onnx_export(
           UpperCAmelCase__,    model_args=(
               torch.randn(1,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			).to(device=UpperCAmelCase__,    dtype=UpperCAmelCase__			),
               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=UpperCAmelCase__,    )

       # 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(
           UpperCAmelCase__,    model_args=(
               torch.randn(1,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			).to(device=UpperCAmelCase__,    dtype=UpperCAmelCase__			),
               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=UpperCAmelCase__,    )
       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,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    ).to(device=UpperCAmelCase__,    dtype=UpperCAmelCase__			),
                      torch.randn(1,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			).to(device=UpperCAmelCase__,    dtype=UpperCAmelCase__			),
                  ),    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=UpperCAmelCase__,    )
              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=UpperCAmelCase__,    feature_extractor=UpperCAmelCase__,    requires_safety_checker=safety_checker is not None,    )

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

       del pipeline
       del onnx_pipeline
       A_		   = OnnxStableDiffusionPipeline.from_pretrained(UpperCAmelCase__,    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'''







import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCamelCase						    =      logging.get_logger(__name__)


@dataclass
class    A__ :
    lowercase		   =						field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()     )}     )
    lowercase		   =						field(
        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}     )
    lowercase		   =						field(
        default=128  ,  metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        }  ,  )
    lowercase		   =						field(
        default=_snake_case  ,  metadata={"help": "Overwrite the cached training and evaluation sets"}     )



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = self.task_name.lower()


class    A__ ( _snake_case     ):
    lowercase		   =						"train"
    lowercase		   =						"dev"
    lowercase		   =						"test"


class    A__ ( _snake_case     ):
    lowercase		   =						42
    lowercase		   =						42
    lowercase		   =						42



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = Split.train						,			UpperCamelCase__ = None						,			)  -> List[Any]:





           '''simple docstring'''
           warnings.warn(
               """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
               """library. You can have a look at this example script for pointers: """
               """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""						,			UpperCamelCase__						,			)
           A_		   = args
           A_		   = glue_processors[args.task_name]()
           A_		   = glue_output_modes[args.task_name]
           if isinstance(UpperCamelCase__						,			UpperCamelCase__    ):
                  try:
                         A_		   = Split[mode]
                  except KeyError:
                         raise KeyError("""mode is not a valid split name"""    )
        # Load data features from cache or dataset file
           A_		   = os.path.join(
               cache_dir if cache_dir is not None else args.data_dir						,			f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''						,			)
           A_		   = self.processor.get_labels()
           if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
               "RobertaTokenizer",
               "RobertaTokenizerFast",
               "XLMRobertaTokenizer",
               "BartTokenizer",
               "BartTokenizerFast",
           ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  A_   ,     A_		   = label_list[2], label_list[1]
           A_		   = label_list

           # Make sure only the first process in distributed training processes the dataset,
           # and the others will use the cache.
           A_		   = cached_features_file + """.lock"""
           with FileLock(UpperCamelCase__    ):
                  if os.path.exists(UpperCamelCase__    ) and not args.overwrite_cache:
                         A_		   = time.time()
                         A_		   = torch.load(UpperCamelCase__    )
                         logger.info(
                             f'''Loading features from cached file {cached_features_file} [took %.3f s]'''						,			time.time() - start    )
                  else:
                         logger.info(f'''Creating features from dataset file at {args.data_dir}'''    )

                         if mode == Split.dev:
                                A_		   = self.processor.get_dev_examples(args.data_dir    )
                         elif mode == Split.test:
                                A_		   = self.processor.get_test_examples(args.data_dir    )
                         else:
                                A_		   = self.processor.get_train_examples(args.data_dir    )
                         if limit_length is not None:
                                A_		   = examples[:limit_length]
                         A_		   = glue_convert_examples_to_features(
                             UpperCamelCase__						,			UpperCamelCase__						,			max_length=args.max_seq_length						,			label_list=UpperCamelCase__						,			output_mode=self.output_mode						,			)
                         A_		   = time.time()
                         torch.save(self.features						,			UpperCamelCase__    )
                         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                         logger.info(
                             f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''    )



    def __len__(   self    )  -> str:





           '''simple docstring'''
           return len(self.features    )



    def __getitem__(   self						,			UpperCamelCase__    )  -> InputFeatures:





           '''simple docstring'''
           return self.features[i]







    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           return self.label_list
