






from dataclasses import dataclass, field
from typing import Optional






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    field(
          default='''codeparrot/codeparrot'''					,    metadata={'''help''': '''Model name or path of model to be trained.'''}  )
      __snake_case	    =    field(
          default='''./'''					,    metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}  )
      __snake_case	    =    field(
          default='''codeparrot/codeparrot-clean-train'''					,    metadata={'''help''': '''Name or path of training dataset.'''}  )
      __snake_case	    =    field(
          default='''codeparrot/codeparrot-clean-valid'''					,    metadata={'''help''': '''Name or path of validation dataset.'''}  )
      __snake_case	    =    field(default=2					,    metadata={'''help''': '''Batch size for training.'''}  )
      __snake_case	    =    field(default=2					,    metadata={'''help''': '''Batch size for evaluation.'''}  )
      __snake_case	    =    field(default=0.1					,    metadata={'''help''': '''Value of weight decay.'''}  )
      __snake_case	    =    field(
          default=1_00_00					,    metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}  )
      __snake_case	    =    field(default=2e-4					,    metadata={'''help''': '''Learning rate fo training.'''}  )
      __snake_case	    =    field(default='''cosine'''					,    metadata={'''help''': '''Learning rate.'''}  )
      __snake_case	    =    field(
          default=7_50					,    metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}  )
      __snake_case	    =    field(
          default=16					,    metadata={'''help''': '''Number of gradient accumulation steps.'''}  )
      __snake_case	    =    field(
          default=lowercase					,    metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}  )
      __snake_case	    =    field(default=5_00_00					,    metadata={'''help''': '''Maximum number of training steps.'''}  )
      __snake_case	    =    field(
          default=-1					,    metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}  )
      __snake_case	    =    field(default=10_24					,    metadata={'''help''': '''Sequence lengths used for training.'''}  )
      __snake_case	    =    field(default=1					,    metadata={'''help''': '''Training seed.'''}  )
      __snake_case	    =    field(
          default=10_24					,    metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}					,    )
      __snake_case	    =    field(
          default=lowercase					,    metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}  )
      __snake_case	    =    field(default=lowercase					,    metadata={'''help''': '''If True the data is pretokenized.'''}  )






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    field(
          default='''codeparrot/codeparrot'''					,    metadata={'''help''': '''Model name or path of model to be evaluated.'''}  )
      __snake_case	    =    field(
          default='''codeparrot/codeparrot-clean-valid'''					,    metadata={'''help''': '''Name or path of validation dataset.'''}  )
      __snake_case	    =    field(default=2					,    metadata={'''help''': '''Batch size used for evaluation.'''}  )
      __snake_case	    =    field(
          default=-1					,    metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}  )
      __snake_case	    =    field(default=10_24					,    metadata={'''help''': '''Length of sequences to be evaluated.'''}  )
      __snake_case	    =    field(default=1					,    metadata={'''help''': '''Random seed used for evaluation.'''}  )






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    field(
          default='''codeparrot/codeparrot'''					,    metadata={'''help''': '''Model name or path of model to be evaluated.'''}  )
      __snake_case	    =    field(default=lowercase					,    metadata={'''help''': '''Number of workers used for code evaluation.'''}  )
      __snake_case	    =    field(
          default=lowercase					,    metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}					,    )
      __snake_case	    =    field(
          default=lowercase					,    metadata={'''help''': '''Sample from the language model\'s output distribution.'''}  )
      __snake_case	    =    field(default=0.2					,    metadata={'''help''': '''Sampling temperature used for generation.'''}  )
      __snake_case	    =    field(default=2_56					,    metadata={'''help''': '''Maximum number of newly generated tokens.'''}  )
      __snake_case	    =    field(default=0					,    metadata={'''help''': '''Top-k parameter used for generation.'''}  )
      __snake_case	    =    field(default=0.9_5					,    metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}  )
      __snake_case	    =    field(default=10					,    metadata={'''help''': '''Number of generations to run in parallel.'''}  )
      __snake_case	    =    field(
          default=2_00					,    metadata={'''help''': '''Number of completions to generate for each sample.'''}  )
      __snake_case	    =    field(default=1					,    metadata={'''help''': '''Random seed used for evaluation.'''}  )
      __snake_case	    =    field(
          default='''eval_results.json'''					,    metadata={'''help''': '''Random seed used for evaluation.'''}  )
      __snake_case	    =    field(
          default='''0'''					,    metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}  )
      __snake_case	    =    field(
          default=-1					,    metadata={
              '''help''': (
                  '''Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'''
                  ''' number corresponds to which GPU device id to run on.'''
              )
          }					,    )






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    field(
          default=lowercase					,    metadata={
              '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
          }					,    )
      __snake_case	    =    field(
          default='''transformersbook/codeparrot'''					,    metadata={'''help''': '''Folder or name of dataset to process.'''}  )
      __snake_case	    =    field(
          default='''codeparrot-clean'''					,    metadata={'''help''': '''Folder to save processed processed dataset.'''}  )
      __snake_case	    =    field(
          default=10_00_00					,    metadata={'''help''': '''Number of files to save per JSON output file.'''}  )
      __snake_case	    =    field(default='''content'''					,    metadata={'''help''': '''Column containing text data to process.'''}  )
      __snake_case	    =    field(
          default=10_00					,    metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}  )
      __snake_case	    =    field(
          default=1_00					,    metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}  )
      __snake_case	    =    field(
          default=0.2_5					,    metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}  )
      __snake_case	    =    field(
          default=1.5					,    metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}  )
      __snake_case	    =    field(
          default=0.7					,    metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}  )
      __snake_case	    =    field(
          default='''codeparrot/codeparrot'''					,    metadata={'''help''': '''Name or path to the tokenizer.'''}					,    )
      __snake_case	    =    field(
          default=lowercase					,    metadata={'''help''': '''If True, near-duplicate samples are removed.'''}  )
      __snake_case	    =    field(
          default=0.8_5					,    metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}  )






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    field(
          default='''gpt2'''					,    metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}  )
      __snake_case	    =    field(
          default='''transformersbook/codeparrot-train'''					,    metadata={'''help''': '''Dataset to train tokenizer on.'''}  )
      __snake_case	    =    field(default='''content'''					,    metadata={'''help''': '''Column containing text data to process.'''}  )
      __snake_case	    =    field(default=20_00_00					,    metadata={'''help''': '''Number of examples to train tokenizer on.'''}  )
      __snake_case	    =    field(
          default=3_27_68					,    metadata={'''help''': '''Number of examples to train the tokenizer on.'''}  )
      __snake_case	    =    field(default='''codeparrot'''					,    metadata={'''help''': '''Name of new tokenizer.'''}  )
      __snake_case	    =    field(default=lowercase					,    metadata={'''help''': '''Push saved tokenizer to the hub.'''}  )






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    field(
          default='''codeparrot/codeparrot'''					,    metadata={'''help''': '''Name or path to the tokenizer.'''}  )
      __snake_case	    =    field(
          default='''codeparrot/codeparrot-clean-train'''					,    metadata={'''help''': '''Name or path to the dataset to pretokenize.'''}  )
      __snake_case	    =    field(
          default='''tokenized-codeparrot-train'''					,    metadata={'''help''': '''Repo name of the pretokenized data.'''}  )
      __snake_case	    =    field(default=lowercase					,    metadata={'''help''': '''Number of workers used for code evaluation.'''}  )






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    field(
          default='''gpt2-large'''					,    metadata={'''help''': '''Configuration to use for model initialization.'''}  )
      __snake_case	    =    field(
          default='''codeparrot/codeparrot'''					,    metadata={'''help''': '''Tokenizer attached to model.'''}  )
      __snake_case	    =    field(default='''codeparrot'''					,    metadata={'''help''': '''Name of the created model.'''}  )
      __snake_case	    =    field(default=lowercase					,    metadata={'''help''': '''Push saved tokenizer to the hub.'''}  )










import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCAmelCase__							  =						version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 		_a       ( a					:Union[str, Any]      ,				a					:tuple      ,				a					:Path      ,				a					:List[Any]      ,				a					:Union[str, Any]      ,				a					:Optional[int]      ,				a					:List[Any]      ,				a					:Union[str, Any]=False      ,				)				->     Any:
 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					:str      ,				a					:str      ,				a					:int      ,				a					:bool = False					)				->     Union[str, Any]:
 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						    = Path(a					)

 # VAE DECODER
 a						    = AutoencoderKL.from_pretrained(model_path + '''/vae'''					)
 a						    = vae_decoder.config.latent_channels
 # forward only through the decoder part
 a						    = vae_decoder.decode
 onnx_export(
     a      ,				model_args=(
         torch.randn(1      ,				a      ,				25      ,				25					).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 vae_decoder


if __name__ == "__main__":
     UpperCAmelCase__							  =						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__							  =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")


