






'''simple docstring'''
import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class 							_UpperCAmelCase    (		snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Union[str, Any]									=     PhobertTokenizer
    __lowerCamelCase: Dict									=     False




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       lowercase_						:     Tuple							     =  ["T@@", "i", "I", "R@@", "r", "e@@"]
       lowercase_						:     Any							     =  dict(zip(a	,  range(len(a      )      )      )      )
       lowercase_						:     Optional[Any]							     =  ["#version: 0.2", "l à</w>"]
       lowercase_						:     int							     =  {"unk_token": "<unk>"}

       lowercase_						:     List[str]							     =  os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]      )
       lowercase_						:     Any							     =  os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["merges_file"]      )

       with open(self.vocab_file	,  "w"	,  encoding="utf-8"      ) as fp:
          for token in vocab_tokens:
             fp.write(f"""{token} {vocab_tokens[token]}\n"""      )
       with open(self.merges_file	,  "w"	,  encoding="utf-8"      ) as fp:
          fp.write("\n".join(a      )      )




    def 					lowerCAmelCase__					(      self	:							Dict	,  **a	:							Union[str, Any]      ):






       '''simple docstring'''






       kwargs.update(self.special_tokens_map      )
       return PhobertTokenizer.from_pretrained(self.tmpdirname	,  **a      )




    def 					lowerCAmelCase__					(      self	:							str	,  a	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  "Tôi là VinAI Research"
       lowercase_						:     List[str]							     =  "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
       return input_text, output_text





    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  PhobertTokenizer(self.vocab_file	,  self.merges_file	,  **self.special_tokens_map      )
       lowercase_						:     Tuple							     =  "Tôi là VinAI Research"
       lowercase_						:     int							     =  "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
       lowercase_						:     Optional[Any]							     =  tokenizer.tokenize(a      )
       print(a      )
       self.assertListEqual(a	,  a      )

       lowercase_						:     List[str]							     =  tokens + [tokenizer.unk_token]

       lowercase_						:     List[str]							     =  [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a      )	,  a      )












'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase__			=      ['small', 'medium', 'large']

UpperCamelCase__			=      'lm_head.decoder.weight'
UpperCamelCase__			=      'lm_head.weight'




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  torch.load(_UpperCamelCase				)
   lowercase_						:     List[Any]							     =  d.pop(_UpperCamelCase				)
   os.makedirs(_UpperCamelCase    ,		exist_ok=_UpperCamelCase				)
   torch.save(_UpperCamelCase    ,		os.path.join(_UpperCamelCase    ,		_UpperCamelCase				)				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser()
 parser.add_argument('--dialogpt_path', default='.', type=str)
 UpperCamelCase__			=      parser.parse_args()
 for MODEL in DIALOGPT_MODELS:
  UpperCamelCase__			=      os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
  UpperCamelCase__			=      f"""./DialoGPT-{MODEL}"""
  convert_dialogpt_checkpoint(
      checkpoint_path,
      pytorch_dump_folder_path,
  )




