




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser







def        lowerCAmelCase_			(  snake_case_=None  ):
      _A							:  List[str]    =		argparse.ArgumentParser(add_help=snake_case_,allow_abbrev=snake_case_  )
      # The main config parser
      _A							:  Union[str, Any]    =		config_command_parser(snake_case_  )
      # The subparser to add commands to
      _A							:  str    =		config_parser.add_subparsers(title="""subcommands""",dest="""subcommand"""  )

      # Then add other parsers with the parent parser
      default_command_parser(snake_case_,parents=[parent_parser]  )
      update_command_parser(snake_case_,parents=[parent_parser]  )

      return config_parser







def        lowerCAmelCase_			(  ):
      _A							:  Optional[int]    =		get_config_parser()
      _A							:  Union[str, Any]    =		config_parser.parse_args()

      if not hasattr(snake_case_,"""func"""  ):
            config_parser.print_help()
            exit(1  )

      # Run
      args.func(snake_case_  )


if __name__ == "__main__":
  main()







import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
  from transformers.models.rag.configuration_rag import RagConfig
  from transformers.models.rag.tokenization_rag import RagTokenizer







@require_faiss
@require_torch
class 		lowercase							(     UpperCamelCase__	):

     def       a__			(						self       )		->							int:
           _A							:  int    =		tempfile.mkdtemp()
           _A							:  Union[str, Any]    =		8

           # DPR tok
           _A							:  List[str]    =		[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           _A							:  List[str]    =		os.path.join(self.tmpdirname	,  """dpr_tokenizer"""       )
           os.makedirs(_a	,  exist_ok=_a       )
           _A							:  str    =		os.path.join(_a	,  DPR_VOCAB_FILES_NAMES["""vocab_file"""]       )
           with open(self.vocab_file	,  """w"""	,  encoding="""utf-8"""       ) as vocab_writer:
                 vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )

           # BART tok
           _A							:  Dict    =		[
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           _A							:  Optional[Any]    =		dict(zip(_a	,  range(len(_a       )       )       )       )
           _A							:  Tuple    =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           _A							:  Dict    =		{"""unk_token""": """<unk>"""}

           _A							:  Optional[Any]    =		os.path.join(self.tmpdirname	,  """bart_tokenizer"""       )
           os.makedirs(_a	,  exist_ok=_a       )
           _A							:  str    =		os.path.join(_a	,  BART_VOCAB_FILES_NAMES["""vocab_file"""]       )
           _A							:  List[Any]    =		os.path.join(_a	,  BART_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       a__			(						self       )		->							DPRQuestionEncoderTokenizer:
           return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,  """dpr_tokenizer"""       )       )

     def       a__			(						self       )		->							BartTokenizer:
           return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,  """bart_tokenizer"""       )       )

     def       a__			(						self       )		->							Optional[Any]:
           shutil.rmtree(self.tmpdirname       )

     @require_tokenizers
     def       a__			(						self       )		->							str:
           _A							:  Optional[Any]    =		os.path.join(self.tmpdirname	,  """rag_tokenizer"""       )
           _A							:  int    =		RagConfig(question_encoder=DPRConfig().to_dict()	,  generator=BartConfig().to_dict()       )
           _A							:  Any    =		RagTokenizer(question_encoder=self.get_dpr_tokenizer()	,  generator=self.get_bart_tokenizer()       )
           rag_config.save_pretrained(_a       )
           rag_tokenizer.save_pretrained(_a       )
           _A							:  Optional[Any]    =		RagTokenizer.from_pretrained(_a	,  config=_a       )
           self.assertIsInstance(new_rag_tokenizer.question_encoder	,  _a       )
           self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()	,  rag_tokenizer.question_encoder.get_vocab()       )
           self.assertIsInstance(new_rag_tokenizer.generator	,  _a       )
           self.assertEqual(new_rag_tokenizer.generator.get_vocab()	,  rag_tokenizer.generator.get_vocab()       )

     @slow
     def       a__			(						self       )		->							str:
           _A							:  Union[str, Any]    =		RagTokenizer.from_pretrained("""facebook/rag-token-nq"""       )
           _A							:  Tuple    =		[
               """who got the first nobel prize in physics""",
               """when is the next deadpool movie being released""",
               """which mode is used for short wave broadcast service""",
               """who is the owner of reading football club""",
               """when is the next scandal episode coming out""",
               """when is the last time the philadelphia won the superbowl""",
               """what is the most current adobe flash player version""",
               """how many episodes are there in dragon ball z""",
               """what is the first step in the evolution of the eye""",
               """where is gall bladder situated in human body""",
               """what is the main mineral in lithium batteries""",
               """who is the president of usa right now""",
               """where do the greasers live in the outsiders""",
               """panda is a national animal of which country""",
               """what is the name of manchester united stadium""",
           ]
           _A							:  Tuple    =		tokenizer(_a       )
           self.assertIsNotNone(_a       )



     @slow
     def       a__			(						self       )		->							Dict:
           _A							:  Dict    =		RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""       )
           _A							:  str    =		[
               """who got the first nobel prize in physics""",
               """when is the next deadpool movie being released""",
               """which mode is used for short wave broadcast service""",
               """who is the owner of reading football club""",
               """when is the next scandal episode coming out""",
               """when is the last time the philadelphia won the superbowl""",
               """what is the most current adobe flash player version""",
               """how many episodes are there in dragon ball z""",
               """what is the first step in the evolution of the eye""",
               """where is gall bladder situated in human body""",
               """what is the main mineral in lithium batteries""",
               """who is the president of usa right now""",
               """where do the greasers live in the outsiders""",
               """panda is a national animal of which country""",
               """what is the name of manchester united stadium""",
           ]
           _A							:  Optional[Any]    =		tokenizer(_a       )
           self.assertIsNotNone(_a       )

