

import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class        lowerCAmelCase__  (					__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    BioGptTokenizer
       lowercase_						=    False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             __A    =[
                 '''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>''',
             ]
             __A    =dict(zip(lowercase__ ,   range(len(lowercase__     )     )     )     )
             __A    =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

             __A    =os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''vocab_file''']     )
             __A    =os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''merges_file''']     )
             with open(self.vocab_file ,   '''w'''     ) as fp:
                   fp.write(json.dumps(lowercase__     )     )
             with open(self.merges_file ,   '''w'''     ) as fp:
                   fp.write('''\n'''.join(lowercase__     )     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    ='''lower newer'''
             __A    ='''lower newer'''
             return input_text, output_text

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =BioGptTokenizer(self.vocab_file ,   self.merges_file     )

             __A    ='''lower'''
             __A    =['''low''', '''er</w>''']
             __A    =tokenizer.tokenize(lowercase__     )
             self.assertListEqual(lowercase__ ,   lowercase__     )

             __A    =tokens + ['''<unk>''']
             __A    =[1_4, 1_5, 2_0]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__     ) ,   lowercase__     )


       @slow
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =BioGptTokenizer.from_pretrained('''microsoft/biogpt'''     )

             __A    =tokenizer.encode('''sequence builders''' ,   add_special_tokens=lowercase__     )
             __A    =tokenizer.encode('''multi-sequence build''' ,   add_special_tokens=lowercase__     )

             __A    =tokenizer.build_inputs_with_special_tokens(lowercase__     )
             __A    =tokenizer.build_inputs_with_special_tokens(lowercase__ ,   lowercase__     )

             self.assertTrue(encoded_sentence == [2] + text     )
             self.assertTrue(encoded_pair == [2] + text + [2] + text_a     )





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration





def      A__					(			__A  :						Any     ,				__A  :						str     ,				__A  :						str     ,				__A  :						Path     ,				__A  :						str = None     ,				__A  :						str = None     ,				__A  :						str = None     ,				)					->Optional[Any]:
      if config_name_or_path is None:
            __A    ='''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

      if generator_tokenizer_name_or_path is None:
            __A    =generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            __A    =question_encoder_name_or_path

      __A    =RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

      # Save model.
      __A    =RagConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)

      __A    =gen_config
      __A    =question_encoder_config

      __A    =model_class.from_pretrained_question_encoder_generator(
          __A     ,				__A     ,				config=__A			)
      rag_model.save_pretrained(__A			)

      # Sanity check.
      model_class.from_pretrained(__A			)

      # Save tokenizers.
      __A    =AutoTokenizer.from_pretrained(__A			)
      gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''			)
      __A    =AutoTokenizer.from_pretrained(__A			)
      question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser()
   parser.add_argument(
       '''--model_type''',
       choices=['''rag_sequence''', '''rag_token'''],
       required=True,
       type=str,
       help='''RAG model type: rag_sequence, rag_token''',
   )
   parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
   parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
   parser.add_argument(
       '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
   )

   parser.add_argument(
       '''--generator_tokenizer_name_or_path''',
       type=str,
       help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
   )
   parser.add_argument(
       '''--question_encoder_tokenizer_name_or_path''',
       type=str,
       help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
   )
   parser.add_argument(
       '''--config_name_or_path''',
       type=str,
       help=(
           '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
           ''' ``model_type``'''
       ),
   )

   _lowerCamelCase      :					str					    =      parser.parse_args()

   _lowerCamelCase      :					Optional[int]					    =      Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )


