





import math

import tensorflow as tf
from packaging import version






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=tf.convert_to_tensor(A     )
   UpperCAmelCase__						=0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0     )				,							x.dtype     )     ))

   return x * cdf






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=tf.convert_to_tensor(A     )
   UpperCAmelCase__						=tf.cast(math.pi				,							x.dtype     )
   UpperCAmelCase__						=tf.cast(0.04_47_15				,							x.dtype     )
   UpperCAmelCase__						=0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi     ) * (x + coeff * tf.pow(A				,							3     ))     ))

   return x * cdf






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=tf.convert_to_tensor(A     )

   return x * tf.tanh(tf.math.softplus(A     )     )






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=tf.convert_to_tensor(A     )
   UpperCAmelCase__						=tf.cast(0.04_47_15				,							x.dtype     )
   UpperCAmelCase__						=tf.cast(0.79_78_84_56_08				,							x.dtype     )

   return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)     ))






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=tf.convert_to_tensor(A     )
   UpperCAmelCase__						=tf.cast(1.7_02				,							x.dtype     )
   return x * tf.math.sigmoid(coeff * x     )






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   return tf.clip_by_value(_gelu(A     )				,							-10				,							10     )






def 				_UpperCAmelCase			(    A				,							A=-1     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=tf.split(A				,							2				,							axis=A     )
   return a * tf.math.sigmoid(A     )


if version.parse(tf.version.VERSION) >= version.parse('2.4'):






    def 				_UpperCAmelCase			(    A     ):




       '''simple docstring'''

       return tf.keras.activations.gelu(A				,							approximate=A     )

    UpperCamelCase_												=	tf.keras.activations.gelu




    UpperCamelCase_												=	approximate_gelu_wrap
else:
    UpperCamelCase_												=	_gelu
    UpperCamelCase_												=	_gelu_new


UpperCamelCase_												=	{
    'gelu': gelu,
    'gelu_10': gelu_aa,
    'gelu_fast': gelu_fast,
    'gelu_new': gelu_new,
    'glu': glu,
    'mish': mish,
    'quick_gelu': quick_gelu,
    'relu': tf.keras.activations.relu,
    'sigmoid': tf.keras.activations.sigmoid,
    'silu': tf.keras.activations.swish,
    'swish': tf.keras.activations.swish,
    'tanh': tf.keras.activations.tanh,
}






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   if activation_string in ACTaFN:
      return ACTaFN[activation_string]
   else:
      raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()     )}"""     )










import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A=1024     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=[], []

   UpperCAmelCase__						=list(zip(A				,							A     )     )
   UpperCAmelCase__			,							UpperCAmelCase__						=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:]     ):
      UpperCAmelCase__						=new_src + " " + src
      UpperCAmelCase__						=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     )
         UpperCAmelCase__			,							UpperCAmelCase__						=src, tgt
      else:  # can fit, keep adding
         UpperCAmelCase__			,							UpperCAmelCase__						=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 				_UpperCAmelCase			(    A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=Path(A     )
   save_path.mkdir(exist_ok=A     )
   for split in ["train"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__			,							UpperCAmelCase__						=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"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=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 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=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     )
   UpperCAmelCase__						=parser.parse_args()
   UpperCAmelCase__						=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()



