

'''simple docstring'''




import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:int      	=					BlenderbotSmallTokenizer
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					False



 def 			snake_case__  (   self		:   Optional[int]      ):
      super().setUp()

      __magic_name__						       =    ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
      __magic_name__						       =    dict(zip(a__			,				range(len(a__      )      )      )      )

      __magic_name__						       =    ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
      __magic_name__						       =    {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

      __magic_name__						       =    os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']      )
      __magic_name__						       =    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(a__      ) + '''\n'''      )
      with open(self.merges_file			,				'''w'''			,				encoding='''utf-8'''      ) as fp:
           fp.write('''\n'''.join(a__      )      )



 def 			snake_case__  (   self		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      kwargs.update(self.special_tokens_map      )
      return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname			,				**a__      )



 def 			snake_case__  (   self		:   List[str]			,				a__		:   List[Any]      ):
      __magic_name__						       =    '''adapt act apte'''
      __magic_name__						       =    '''adapt act apte'''
      return input_text, output_text



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    BlenderbotSmallTokenizer(self.vocab_file			,				self.merges_file			,				**self.special_tokens_map      )
      __magic_name__						       =    '''adapt act apte'''
      __magic_name__						       =    ['''adapt''', '''act''', '''ap@@''', '''te''']
      __magic_name__						       =    tokenizer.tokenize(a__      )
      self.assertListEqual(a__			,				a__      )

      __magic_name__						       =    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

      __magic_name__						       =    [0, 1, 2, 3, 4, 5]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )			,				a__      )



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''      )
      assert tok('''sam'''      ).input_ids == [1384]
      __magic_name__						       =    '''I am a small frog.'''
      __magic_name__						       =    tok([src_text]			,				padding=a__			,				truncation=a__      )['''input_ids''']
      __magic_name__						       =    tok.batch_decode(a__			,				skip_special_tokens=a__			,				clean_up_tokenization_spaces=a__      )[0]
      assert src_text != decoded  # I wish it did!
      assert decoded == "i am a small frog ."



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''      )
      __magic_name__						       =    '''I am a small frog .'''
      __magic_name__						       =    '''.'''
      __magic_name__						       =    tok(a__      )['''input_ids''']
      __magic_name__						       =    tok(a__      )['''input_ids''']

      assert encoded[-1] == encoded_dot[0]









'''simple docstring'''




from pathlib import Path

import fire
from tqdm import tqdm




def 						UpperCamelCase	(			a="ro"		,						a="en"		,						a="wmt16"		,						a=None			)				->     None:






     '''simple docstring'''
     try:
          import datasets
     except (ModuleNotFoundError, ImportError):
          raise ImportError('''run pip install datasets'''			)
     __magic_name__						       =    F'''{src_lang}-{tgt_lang}'''
     print(F'''Converting {dataset}-{pair}'''			)
     __magic_name__						       =    datasets.load_dataset(a		,						a			)
     if save_dir is None:
          __magic_name__						       =    F'''{dataset}-{pair}'''
     __magic_name__						       =    Path(a			)
     save_dir.mkdir(exist_ok=a			)

     for split in ds.keys():
          print(F'''Splitting {split} with {ds[split].num_rows} records'''			)

          # to save to val.source, val.target like summary datasets
          __magic_name__						       =    '''val''' if split == '''validation''' else split
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.source'''			)
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.target'''			)
          __magic_name__						       =    src_path.open('''w+'''			)
          __magic_name__						       =    tgt_path.open('''w+'''			)

          # reader is the bottleneck so writing one record at a time doesn't slow things down
          for x in tqdm(ds[split]			):
               __magic_name__						       =    x['''translation''']
               src_fp.write(ex[src_lang] + '''\n'''			)
               tgt_fp.write(ex[tgt_lang] + '''\n'''			)

     print(F'''Saved {dataset} dataset to {save_dir}'''			)


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






