




import argparse
from pathlib import Path

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

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,)	-> List[str]:
       if config_name_or_path is None:
              lowerCamelCase   :      Any			 =					"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

       if generator_tokenizer_name_or_path is None:
              lowerCamelCase   :      Dict			 =					generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              lowerCamelCase   :      Any			 =					question_encoder_name_or_path

       lowerCamelCase   :      str			 =					RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

       # Save model.
       lowerCamelCase   :      List[Any]			 =					RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[Any]			 =					gen_config
       lowerCamelCase   :      Optional[Any]			 =					question_encoder_config

       lowerCamelCase   :      List[Any]			 =					model_class.from_pretrained_question_encoder_generator(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )
       rag_model.save_pretrained(_SCREAMING_SNAKE_CASE    )

       # Sanity check.
       model_class.from_pretrained(_SCREAMING_SNAKE_CASE    )

       # Save tokenizers.
       lowerCamelCase   :      List[str]			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"    )
       lowerCamelCase   :      int			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Any  =   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``'
           ),
       )

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   parser.parse_args()

       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   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,
       )














"""simple docstring"""



import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_UpperCamelCase		:  Tuple						=		logging.getLogger()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Path		,					__snake_case			:  list    ):





  '''simple docstring'''





  lowercase							    =       '\n'.join(__snake_case    )
  Path(__snake_case    ).open('w'    ).writelines(__snake_case    )


_UpperCamelCase		:  Union[str, Any]						=		'patrickvonplaten/t5-tiny-random'
_UpperCamelCase		:  Union[str, Any]						=		'sshleifer/bart-tiny-random'
_UpperCamelCase		:  Tuple						=		'sshleifer/tiny-mbart'

_UpperCamelCase		:  Union[str, Any]						=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



class        a     (   a_	):







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       ) / 'utest_input.source'
      lowercase							    =       input_file_name.parent / 'utest_output.txt'
      assert not output_file_name.exists()
      lowercase							    =       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
      _dump_articles(_lowerCamelCase							,		_lowerCamelCase       )

      lowercase							    =       str(Path(self.get_auto_remove_tmp_dir()       ) / 'scores.json'       )
      lowercase							    =       'translation_en_to_de' if model == T5_TINY else 'summarization'
      lowercase							    =       F'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

      with patch.object(_lowerCamelCase							,		'argv'							,		_lowerCamelCase       ):
        run_generate()
        assert Path(_lowerCamelCase       ).exists()
        # os.remove(Path(output_file_name))







    def 			UpperCamelCase_      (			self       ):
      self.run_eval_tester(_lowerCamelCase       )







    @parameterized.expand([BART_TINY, MBART_TINY]       )
    @slow
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      self.run_eval_tester(_lowerCamelCase       )







    @parameterized.expand([T5_TINY, MBART_TINY]       )
    @slow
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       ) / 'utest_input.source'
      lowercase							    =       input_file_name.parent / 'utest_output.txt'
      assert not output_file_name.exists()

      lowercase							    =       {
          'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
          'de': [
              'Maschinelles Lernen ist großartig, oder?',
              'Ich esse gerne Bananen',
              'Morgen ist wieder ein toller Tag!',
          ],
      }

      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       )
      lowercase							    =       str(tmp_dir / 'scores.json'       )
      lowercase							    =       str(tmp_dir / 'val.target'       )
      _dump_articles(_lowerCamelCase							,		text['en']       )
      _dump_articles(_lowerCamelCase							,		text['de']       )
      lowercase							    =       'translation_en_to_de' if model == T5_TINY else 'summarization'
      lowercase							    =       F'\n            run_eval_search.py\n            {model}\n            {str(_lowerCamelCase       )}\n            {str(_lowerCamelCase       )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
      testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']       )

      with patch.object(_lowerCamelCase							,		'argv'							,		_lowerCamelCase       ):
        with CaptureStdout() as cs:
          run_search()
        lowercase							    =       [' num_beams | length_penalty', model, 'Best score args']
        lowercase							    =       ['Info']
        if "translation" in task:
          expected_strings.append('bleu'       )
        else:
          expected_strings.extend(_lowerCamelCase       )
        for w in expected_strings:
          assert w in cs.out
        for w in un_expected_strings:
          assert w not in cs.out
        assert Path(_lowerCamelCase       ).exists()
        os.remove(Path(_lowerCamelCase       )       )


