


import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType



class 							_UpperCamelCase :

   '''simple docstring'''







   def __init__(  self  : Dict       ,  _lowerCamelCase  : Tuple=2       ,  _lowerCamelCase  : Optional[int]=3       ,  _lowerCamelCase  : Optional[int]=6_4       ,  _lowerCamelCase  : Tuple=None							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						np.random.default_rng(lowerCAmelCase__							)
         __lowerCamelCase     :      str       						=						length
         __lowerCamelCase     :      List[str]       						=						rng.normal(size=(length,)							).astype(np.floataa							)
         __lowerCamelCase     :      Union[str, Any]       						=						a * self.x + b + rng.normal(scale=0.1       ,  size=(length,)							).astype(np.floataa							)

   def __len__(  self  : Optional[int]							):






         '''simple docstring'''



         return self.length


   def __getitem__(  self  : str       ,  _lowerCamelCase  : Union[str, Any]							):






         '''simple docstring'''



         return {"x": self.x[i], "y": self.y[i]}



class 							_UpperCamelCase (						torch.nn.Module						):

   '''simple docstring'''







   def __init__(  self  : int       ,  _lowerCamelCase  : Optional[int]=0       ,  _lowerCamelCase  : Any=0       ,  _lowerCamelCase  : List[Any]=False							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      int       						=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
         __lowerCamelCase     :      Optional[Any]       						=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
         __lowerCamelCase     :      List[str]       						=						True


   def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : int=None							):






         '''simple docstring'''



         if self.first_batch:
               print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
               __lowerCamelCase     :      Tuple       						=						False
         return x * self.a[0] + self.b[0]



class 							_UpperCamelCase (						torch.nn.Module						):

   '''simple docstring'''







   def __init__(  self  : List[Any]       ,  _lowerCamelCase  : List[Any]=0       ,  _lowerCamelCase  : List[Any]=0       ,  _lowerCamelCase  : int=False							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      Tuple       						=						torch.nn.Parameter(torch.tensor(lowerCAmelCase__							).float()							)
         __lowerCamelCase     :      Any       						=						torch.nn.Parameter(torch.tensor(lowerCAmelCase__							).float()							)
         __lowerCamelCase     :      Optional[int]       						=						True


   def   _snake_case    (  self  : Union[str, Any]       ,  _lowerCamelCase  : Any=None							):






         '''simple docstring'''



         if self.first_batch:
               print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
               __lowerCamelCase     :      Optional[Any]       						=						False
         return x * self.a + self.b





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			int = 16      ):
      """simple docstring"""




      from datasets import load_dataset
      from transformers import AutoTokenizer

      __lowerCamelCase     :      Optional[int]       						=						AutoTokenizer.from_pretrained("""bert-base-cased"""      )
      __lowerCamelCase     :      Optional[int]       						=						{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
      __lowerCamelCase     :      str       						=						load_dataset("""csv"""						,	data_files=_lowercase      )
      __lowerCamelCase     :      Dict       						=						datasets["train"].unique("""label"""      )

      __lowerCamelCase     :      List[str]       						=						{v: i for i, v in enumerate(_lowercase      )}

      def tokenize_function(UpperCAmelCase      :			Optional[int]      ):
            # max_length=None => use the model max length (it's actually the default)
            __lowerCamelCase     :      List[str]       						=						tokenizer(
                examples["""sentence1"""]						,	examples["""sentence2"""]						,	truncation=_lowercase						,	max_length=_lowercase						,	padding="""max_length"""      )
            if "label" in examples:
                  __lowerCamelCase     :      Optional[Any]       						=						[label_to_id[l] for l in examples["label"]]
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      __lowerCamelCase     :      Any       						=						datasets.map(
          _lowercase						,	batched=_lowercase						,	remove_columns=["""sentence1""", """sentence2""", """label"""]						,	)

      def collate_fn(UpperCAmelCase      :			Any      ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(_lowercase						,	padding="""max_length"""						,	max_length=128						,	return_tensors="""pt"""      )
            return tokenizer.pad(_lowercase						,	padding="""longest"""						,	return_tensors="""pt"""      )

      # Instantiate dataloaders.
      __lowerCamelCase     :      List[Any]       						=						DataLoader(tokenized_datasets["""train"""]						,	shuffle=_lowercase						,	collate_fn=_lowercase						,	batch_size=2      )
      __lowerCamelCase     :      Optional[Any]       						=						DataLoader(tokenized_datasets["""validation"""]						,	shuffle=_lowercase						,	collate_fn=_lowercase						,	batch_size=1      )

      return train_dataloader, eval_dataloader










import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Any=1_024      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      str       						=						[], []

      __lowerCamelCase     :      Any       						=						list(zip(UpperCAmelCase						,	UpperCAmelCase      )      )
      __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						sorted_examples[0]

      def is_too_big(UpperCAmelCase      :			Optional[Any]      ):
            return tok(UpperCAmelCase						,	return_tensors="""pt"""      ).input_ids.shape[1] > max_tokens

      for src, tgt in tqdm(sorted_examples[1:]      ):
            __lowerCamelCase     :      Union[str, Any]       						=						new_src + """ """ + src
            __lowerCamelCase     :      str       						=						new_tgt + """ """ + tgt
            if is_too_big(UpperCAmelCase      ) or is_too_big(UpperCAmelCase      ):  # cant fit, finalize example
                  finished_src.append(UpperCAmelCase      )
                  finished_tgt.append(UpperCAmelCase      )
                  __lowerCamelCase						, __lowerCamelCase     :      str       						=						src, tgt
            else:  # can fit, keep adding
                  __lowerCamelCase						, __lowerCamelCase     :      int       						=						cand_src, cand_tgt

    # cleanup
      if new_src:
            assert new_tgt
            finished_src.append(UpperCAmelCase      )
            finished_tgt.append(UpperCAmelCase      )
      return finished_src, finished_tgt





def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Path						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						Path(UpperCAmelCase      )
      save_path.mkdir(exist_ok=UpperCAmelCase      )
      for split in ["train"]:
            __lowerCamelCase						, __lowerCamelCase     :      List[Any]       						=						data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
            __lowerCamelCase     :      Tuple       						=						[x.rstrip() for x in Path(UpperCAmelCase      ).open().readlines()]
            __lowerCamelCase     :      Tuple       						=						[x.rstrip() for x in Path(UpperCAmelCase      ).open().readlines()]
            __lowerCamelCase						, __lowerCamelCase     :      int       						=						pack_examples(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
            print(f"""packed {split} split from {len(UpperCAmelCase      )} examples -> {len(UpperCAmelCase      )}."""      )
            Path(save_path / f"""{split}.source"""      ).open("""w"""      ).write("""\n""".join(UpperCAmelCase      )      )
            Path(save_path / f"""{split}.target"""      ).open("""w"""      ).write("""\n""".join(UpperCAmelCase      )      )
      for split in ["val", "test"]:
            __lowerCamelCase						, __lowerCamelCase     :      Optional[Any]       						=						data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
            shutil.copyfile(UpperCAmelCase						,	save_path / f"""{split}.source"""      )
            shutil.copyfile(UpperCAmelCase						,	save_path / f"""{split}.target"""      )





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						argparse.ArgumentParser()
      parser.add_argument("""--tok_name"""						,	type=UpperCAmelCase						,	help="""like facebook/bart-large-cnn,t5-base, etc."""      )
      parser.add_argument("""--max_seq_len"""						,	type=UpperCAmelCase						,	default=128      )
      parser.add_argument("""--data_dir"""						,	type=UpperCAmelCase      )
      parser.add_argument("""--save_path"""						,	type=UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						parser.parse_args()
      __lowerCamelCase     :      Tuple       						=						AutoTokenizer.from_pretrained(args.tok_name      )
      return pack_data_dir(UpperCAmelCase						,	Path(args.data_dir      )						,	args.max_seq_len						,	args.save_path      )


if __name__ == "__main__":
    packer_cli()



