





import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a=1024		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__  , lowerCamelCase__:				Optional[int]        =[], []

  lowerCamelCase__:				str        =list(zip(__a     ,   __a		)		)
  lowerCamelCase__  , lowerCamelCase__:				List[str]        =sorted_examples[0]

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

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

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





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a		)					->		List[str]:







  """simple docstring"""





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





def 					lowerCAmelCase_			(	)					->		str:







  """simple docstring"""





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


if __name__ == "__main__":
     packer_cli()








from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def __init__(self    :       int	,			UpperCAmelCase_    :       Distribution	,			UpperCAmelCase_    :       Dict=None	,			UpperCAmelCase_    :       Tuple=None	,			UpperCAmelCase_    :       Tuple=0)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =1.0 if scale is None else scale
   lowerCamelCase__:				List[Any]        =0.0 if loc is None else loc

   super().__init__(UpperCAmelCase_	,			[AffineTransform(loc=self.loc	,			scale=self.scale	,			event_dim=UpperCAmelCase_)])

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->int:



   '''simple docstring'''



   return self.base_dist.mean * self.scale + self.loc

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Dict:



   '''simple docstring'''



   return self.base_dist.variance * self.scale**2







 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Optional[Any]:



   '''simple docstring'''



   return self.variance.sqrt()



class        _SCREAMING_SNAKE_CASE						(     nn.Module   ):







 '''simple docstring'''

 def __init__(self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Dict[str, int]	,			UpperCAmelCase_    :       Callable[..., Tuple[torch.Tensor]]	,			**UpperCAmelCase_    :       Dict)	->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =args_dim
   lowerCamelCase__:				Any        =nn.ModuleList([nn.Linear(UpperCAmelCase_	,			UpperCAmelCase_) for dim in args_dim.values()])
   lowerCamelCase__:				Any        =domain_map







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       torch.Tensor)	->Tuple[torch.Tensor]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =[proj(UpperCAmelCase_) for proj in self.proj]

   return self.domain_map(*UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						(     nn.Module   ):







 '''simple docstring'''

 def __init__(self    :       str	,			UpperCAmelCase_    :       Optional[Any])	->Optional[int]:



   '''simple docstring'''



   super().__init__()
   lowerCamelCase__:				Any        =function







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Optional[Any]	,			*UpperCAmelCase_    :       Union[str, Any])	->Union[str, Any]:



   '''simple docstring'''



   return self.function(UpperCAmelCase_	,			*UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      42

 def __init__(self    :       str	,			UpperCAmelCase_    :       int = 1)	->None:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =dim
   lowerCamelCase__:				int        ={k: dim * self.args_dim[k] for k in self.args_dim}

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       str)	->Optional[Any]:



   '''simple docstring'''



   if self.dim == 1:
     return self.distribution_class(*UpperCAmelCase_)
   else:
     return Independent(self.distribution_class(*UpperCAmelCase_)	,			1)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self._base_distribution(UpperCAmelCase_)
   if loc is None and scale is None:
     return distr
   else:
     return AffineTransformed(UpperCAmelCase_	,			loc=UpperCAmelCase_	,			scale=UpperCAmelCase_	,			event_dim=self.event_dim)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Tuple:



   '''simple docstring'''



   return () if self.dim == 1 else (self.dim,)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->int:



   '''simple docstring'''



   return len(self.event_shape)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->float:



   '''simple docstring'''



   return 0.0

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       int)	->nn.Module:



   '''simple docstring'''



   return ParameterProjection(
       in_features=UpperCAmelCase_	,			args_dim=self.args_dim	,			domain_map=LambdaLayer(self.domain_map)	,			)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			*UpperCAmelCase_    :       torch.Tensor)	->Any:



   '''simple docstring'''



   raise NotImplementedError()







 @staticmethod
 def 							SCREAMING_SNAKE_CASE_ (UpperCAmelCase_    :       torch.Tensor)	->torch.Tensor:



   '''simple docstring'''



   return (x + torch.sqrt(torch.square(UpperCAmelCase_) + 4.0)) / 2.0



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"df": 1, "loc": 1, "scale": 1}
 lowercase_		=      StudentT

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[int]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				str        =cls.squareplus(UpperCAmelCase_).clamp_min(torch.finfo(scale.dtype).eps)
   lowerCamelCase__:				Any        =2.0 + cls.squareplus(UpperCAmelCase_)
   return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"loc": 1, "scale": 1}
 lowercase_		=      Normal

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[Any]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =cls.squareplus(UpperCAmelCase_).clamp_min(torch.finfo(scale.dtype).eps)
   return loc.squeeze(-1), scale.squeeze(-1)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"total_count": 1, "logits": 1}
 lowercase_		=      NegativeBinomial

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[int]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =cls.squareplus(UpperCAmelCase_)
   return total_count.squeeze(-1), logits.squeeze(-1)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       Tuple)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Any        =distr_args
   if self.dim == 1:
     return self.distribution_class(total_count=UpperCAmelCase_	,			logits=UpperCAmelCase_)
   else:
     return Independent(self.distribution_class(total_count=UpperCAmelCase_	,			logits=UpperCAmelCase_)	,			1)







 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =distr_args

   if scale is not None:
     # See scaling property of Gamma.
     logits += scale.log()

   return self._base_distribution((total_count, logits))

