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__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__    ,     a__    ,     a__    ,     a__=False    ,     )      ->Tuple:




  '''simple docstring'''

  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 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__ = False )      ->Dict:




  '''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(_UpperCamelCase    ,     torch_dtype=_UpperCamelCase ).to(_UpperCamelCase )
  _UpperCamelCase           =    Path(_UpperCamelCase )

  # 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=_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
  _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    ,     _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    ,     )
  _UpperCamelCase           =    str(unet_path.absolute().as_posix() )
  _UpperCamelCase           =    os.path.dirname(_UpperCamelCase )
  _UpperCamelCase           =    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
  _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 a__    ,     a__ : 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
  _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(
      _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:
    _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    ,     _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
    _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=_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
  _UpperCamelCase           =    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 argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A__ :       List[Any]				= 1_6
A__ :       Union[str, Any]				= 3_2




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase = 16    ):






  """simple docstring"""



  _lowercase:      Any   			=  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
  _lowercase:      List[str]   			=  load_dataset('''glue'''			,					'''mrpc'''    )

  def tokenize_function(_UpperCamelCase    ):
    # max_length=None => use the model max length (it's actually the default)
    _lowercase:      Union[str, Any]   			=  tokenizer(examples['''sentence1''']			,					examples['''sentence2''']			,					truncation=_UpperCamelCase			,					max_length=_UpperCamelCase    )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    _lowercase:      Tuple   			=  datasets.map(
        _UpperCamelCase			,					batched=_UpperCamelCase			,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,					)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  _lowercase:      Optional[Any]   			=  tokenized_datasets.rename_column('''label'''			,					'''labels'''    )

  def collate_fn(_UpperCamelCase    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _lowercase:      Union[str, Any]   			=  128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _lowercase:      int   			=  16
    elif accelerator.mixed_precision != "no":
      _lowercase:      Tuple   			=  8
    else:
      _lowercase:      str   			=  None

    return tokenizer.pad(
        _UpperCamelCase			,					padding='''longest'''			,					max_length=_UpperCamelCase			,					pad_to_multiple_of=_UpperCamelCase			,					return_tensors='''pt'''			,					)

  # Instantiate dataloaders.
  _lowercase:      List[Any]   			=  DataLoader(
      tokenized_datasets['''train''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )
  _lowercase:      int   			=  DataLoader(
      tokenized_datasets['''validation''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  A__ :       List[Any]				= mocked_dataloaders  # noqa: F811




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,					_UpperCamelCase    ) == "1":
    _lowercase:      Tuple   			=  2
  # Initialize accelerator
  _lowercase:      Optional[Any]   			=  Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowercase:      Optional[Any]   			=  config['''lr''']
  _lowercase:      Tuple   			=  int(config['''num_epochs''']    )
  _lowercase:      Any   			=  int(config['''seed''']    )
  _lowercase:      List[Any]   			=  int(config['''batch_size''']    )

  _lowercase:      Optional[int]   			=  evaluate.load('''glue'''			,					'''mrpc'''    )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=_UpperCamelCase    )
  def inner_training_loop(_UpperCamelCase    ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(_UpperCamelCase    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase:      Optional[int]   			=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,					return_dict=_UpperCamelCase    )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    _lowercase:      List[Any]   			=  model.to(accelerator.device    )

    # Instantiate optimizer
    _lowercase:      List[str]   			=  AdamW(params=model.parameters()			,					lr=_UpperCamelCase    )
    _lowercase			,					_lowercase:      Tuple   			=  get_dataloaders(_UpperCamelCase			,					_UpperCamelCase    )

    # Instantiate scheduler
    _lowercase:      str   			=  get_linear_schedule_with_warmup(
        optimizer=_UpperCamelCase			,					num_warmup_steps=100			,					num_training_steps=(len(_UpperCamelCase    ) * num_epochs)			,					)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    _lowercase			,					_lowercase			,					_lowercase			,					_lowercase			,					_lowercase:      Union[str, Any]   			=  accelerator.prepare(
        _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )

    # Now we train the model
    for epoch in range(_UpperCamelCase    ):
      model.train()
      for step, batch in enumerate(_UpperCamelCase    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        _lowercase:      Tuple   			=  model(**_UpperCamelCase    )
        _lowercase:      Union[str, Any]   			=  outputs.loss
        accelerator.backward(_UpperCamelCase    )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(_UpperCamelCase    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        with torch.no_grad():
          _lowercase:      str   			=  model(**_UpperCamelCase    )
        _lowercase:      int   			=  outputs.logits.argmax(dim=-1    )
        _lowercase			,					_lowercase:      List[str]   			=  accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
        metric.add_batch(
            predictions=_UpperCamelCase			,					references=_UpperCamelCase			,					)

      _lowercase:      int   			=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''			,					_UpperCamelCase    )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  argparse.ArgumentParser(description='''Simple example of training script.'''    )
  parser.add_argument(
      '''--mixed_precision'''			,					type=_UpperCamelCase			,					default=_UpperCamelCase			,					choices=['''no''', '''fp16''', '''bf16''', '''fp8''']			,					help='''Whether to use mixed precision. Choose'''
      '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
      '''and an Nvidia Ampere GPU.'''			,					)
  parser.add_argument('''--cpu'''			,					action='''store_true'''			,					help='''If passed, will train on the CPU.'''    )
  _lowercase:      Tuple   			=  parser.parse_args()
  _lowercase:      str   			=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  main()
