





import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      XLMTokenizer
 lowercase_		=      False

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->int:



   '''simple docstring'''



   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   lowerCamelCase__:				int        =[
       "l",
       "o",
       "w",
       "e",
       "r",
       "s",
       "t",
       "i",
       "d",
       "n",
       "w</w>",
       "r</w>",
       "t</w>",
       "lo",
       "low",
       "er</w>",
       "low</w>",
       "lowest</w>",
       "newer</w>",
       "wider</w>",
       "<unk>",
   ]
   lowerCamelCase__:				List[Any]        =dict(zip(UpperCAmelCase_	,			range(len(UpperCAmelCase_))))
   lowerCamelCase__:				List[str]        =["l o 123", "lo w 1456", "e r</w> 1789", ""]

   lowerCamelCase__:				Dict        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["vocab_file"])
   lowerCamelCase__:				str        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["merges_file"])
   with open(self.vocab_file	,			"w") as fp:
     fp.write(json.dumps(UpperCAmelCase_))
   with open(self.merges_file	,			"w") as fp:
     fp.write("\n".join(UpperCAmelCase_))

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



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        ="lower newer"
   lowerCamelCase__:				Union[str, Any]        ="lower newer"
   return input_text, output_text

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =XLMTokenizer(self.vocab_file	,			self.merges_file)

   lowerCamelCase__:				List[Any]        ="lower"
   lowerCamelCase__:				Tuple        =["low", "er</w>"]
   lowerCamelCase__:				Dict        =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_	,			UpperCAmelCase_)

   lowerCamelCase__:				int        =tokens + ["<unk>"]
   lowerCamelCase__:				str        =[14, 15, 20]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)	,			UpperCAmelCase_)







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->int:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =XLMTokenizer.from_pretrained("xlm-mlm-en-2048")

   lowerCamelCase__:				Union[str, Any]        =tokenizer.encode("sequence builders"	,			add_special_tokens=UpperCAmelCase_)
   lowerCamelCase__:				Union[str, Any]        =tokenizer.encode("multi-sequence build"	,			add_special_tokens=UpperCAmelCase_)

   lowerCamelCase__:				Optional[Any]        =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_)
   lowerCamelCase__:				Optional[Any]        =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_	,			UpperCAmelCase_)

   assert encoded_sentence == [0] + text + [1]
   assert encoded_pair == [0] + text + [1] + text_a + [1]








import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__A     =    logging.getLogger(__name__)

if __name__ == "__main__":
     __A     =    argparse.ArgumentParser(
         description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
     )
     parser.add_argument(
         "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
     )
     parser.add_argument(
         "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
     )
     parser.add_argument("--vocab_size", default=3_0522, type=int)
     __A     =    parser.parse_args()

     logger.info(f'Loading data from {args.data_file}')
     with open(args.data_file, "rb") as fp:
          __A     =    pickle.load(fp)

     logger.info("Counting occurrences for MLM.")
     __A     =    Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     __A     =    [0] * args.vocab_size
     for k, v in counter.items():
          __A     =    v

     logger.info(f'Dump to {args.token_counts_dump}')
     with open(args.token_counts_dump, "wb") as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)

