'''simple docstring'''

import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__a: List[Any]          =   logging.get_logger(__name__)
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Union[str, Any]  =					RobertaPreLayerNormConfig.from_pretrained(
     UpperCAmelCase		,     architectures=['''RobertaPreLayerNormForMaskedLM''']     )

 # convert state_dict
 lowercase__				:    Tuple  =					torch.load(hf_hub_download(repo_id=UpperCAmelCase		,     filename='''pytorch_model.bin'''     )     )
 lowercase__				:    List[str]  =					{}
 for tensor_key, tensor_value in original_state_dict.items():
  # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
  if tensor_key.startswith('''roberta.'''     ):
   lowercase__				:    Tuple  =					'''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''     ) :]

  # The original implementation contains weights which are not used, remove them from the state_dict
  if tensor_key.endswith('''.self.LayerNorm.weight'''     ) or tensor_key.endswith('''.self.LayerNorm.bias'''     ):
   continue

  lowercase__				:    List[Any]  =					tensor_value

 lowercase__				:    Tuple  =					RobertaPreLayerNormForMaskedLM.from_pretrained(
     pretrained_model_name_or_path=UpperCAmelCase		,     config=UpperCAmelCase		,     state_dict=UpperCAmelCase     )
 model.save_pretrained(UpperCAmelCase     )

 # convert tokenizer
 lowercase__				:    List[str]  =					AutoTokenizer.from_pretrained(UpperCAmelCase     )
 tokenizer.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
  __a: Union[str, Any]          =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint-repo""",
      default=None,
      type=str,
      required=True,
      help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __a: Dict          =   parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


'''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 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    CTRLTokenizer
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Dict:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase__				:    Optional[Any]  =					['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
        lowercase__				:    str  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )
        lowercase__				:    Tuple  =					['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
        lowercase__				:    Optional[Any]  =					{'''unk_token''': '''<unk>'''}

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

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  List[str]:
        kwargs.update(self.special_tokens_map )
        return CTRLTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  List[str]:
        lowercase__				:    List[str]  =					'''adapt react readapt apt'''
        lowercase__				:    Union[str, Any]  =					'''adapt react readapt apt'''
        return input_text, output_text


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Union[str, Any]  =					CTRLTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Optional[Any]  =					'''adapt react readapt apt'''
        lowercase__				:    Dict  =					'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    int  =					tokens + [tokenizer.unk_token]

        lowercase__				:    List[str]  =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

