'''simple docstring'''

import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class   snake_case					(					lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						CTRLTokenizer
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False






  def 						snake_case	(					self  ):







   """simple docstring"""
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   lowerCamelCase_  				=						["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
   lowerCamelCase_  				=						dict(zip(UpperCamelCase    ,						range(len(UpperCamelCase  )  )  )  )
   lowerCamelCase_  				=						["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
   lowerCamelCase_  				=						{"unk_token": "<unk>"}

   lowerCamelCase_  				=						os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["vocab_file"]  )
   lowerCamelCase_  				=						os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["merges_file"]  )
   with open(self.vocab_file    ,						"w"    ,						encoding="utf-8"  ) as fp:
    fp.write(json.dumps(UpperCamelCase  ) + "\n"  )
   with open(self.merges_file    ,						"w"    ,						encoding="utf-8"  ) as fp:
    fp.write("\n".join(UpperCamelCase  )  )






  def 						snake_case	(					self    ,						**UpperCamelCase  ):







   """simple docstring"""
   kwargs.update(self.special_tokens_map  )
   return CTRLTokenizer.from_pretrained(self.tmpdirname    ,						**UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						"adapt react readapt apt"
   lowerCamelCase_  				=						"adapt react readapt apt"
   return input_text, output_text







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						CTRLTokenizer(self.vocab_file    ,						self.merges_file    ,						**self.special_tokens_map  )
   lowerCamelCase_  				=						"adapt react readapt apt"
   lowerCamelCase_  				=						"adapt re@@ a@@ c@@ t re@@ adapt apt".split()
   lowerCamelCase_  				=						tokenizer.tokenize(UpperCamelCase  )
   self.assertListEqual(UpperCamelCase    ,						UpperCamelCase  )

   lowerCamelCase_  				=						tokens + [tokenizer.unk_token]

   lowerCamelCase_  				=						[0, 1, 2, 4, 5, 1, 0, 3, 6]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase  )    ,						UpperCamelCase  )







'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[int]=1024      ,       UpperCAmelCase_		:		int=1024      ,       UpperCAmelCase_		:		Optional[Any]=False      ,       **UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="train"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						tok.pad_token_id

 def get_lens(UpperCAmelCase_		:		str	):
  lowerCamelCase_  				=						tqdm(
      DataLoader(UpperCAmelCase_      ,       batch_size=512      ,       num_workers=8      ,       shuffle=UpperCAmelCase_      ,       collate_fn=ds.collate_fn	)      ,       desc=str(ds.len_file	)      ,       )
  lowerCamelCase_  				=						[]
  for batch in dl:
   lowerCamelCase_  				=						batch["input_ids"].ne(UpperCAmelCase_	).sum(1	).tolist()
   lowerCamelCase_  				=						batch["labels"].ne(UpperCAmelCase_	).sum(1	).tolist()
   if consider_target:
    for src, tgt in zip(UpperCAmelCase_      ,       UpperCAmelCase_	):
     max_lens.append(max(UpperCAmelCase_      ,       UpperCAmelCase_	)	)
   else:
    max_lens.extend(UpperCAmelCase_	)
  return max_lens

 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="val"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 pickle_save(UpperCAmelCase_      ,       train_ds.len_file	)
 pickle_save(UpperCAmelCase_      ,       val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)



