
'''simple docstring'''





import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict







@pytest.mark.parametrize(
    "files"   ,       [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]   ,       )
def snake_case_     (					lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					List[Any]				       =							tmp_path_factory.mktemp("dset_infos_dir"		)
  if "full:README.md" in files:
    with open(dataset_infos_dir / "README.md"   ,       "w"		) as f:
      f.write("---\ndataset_info:\n  dataset_size: 42\n---"		)
  if "empty:README.md" in files:
    with open(dataset_infos_dir / "README.md"   ,       "w"		) as f:
      f.write(""		)
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / "dataset_infos.json"   ,       "w"		) as f:
      f.write("{\"default\": {\"dataset_size\": 42}}"		)
  UpperCAmelCase__      :					Dict				       =							DatasetInfosDict.from_directory(__A		)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 4_2







@pytest.mark.parametrize(
    "dataset_info"   ,       [
        DatasetInfo(),
        DatasetInfo(
            description="foo"   ,       features=Features({"a": Value("int32"		)}		)   ,       builder_name="builder"   ,       config_name="config"   ,       version="1.0.0"   ,       splits=[{"name": "train"}]   ,       download_size=4_2   ,       ),
    ]   ,       )
def snake_case_     (					lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					Tuple				       =							str(__A		)
  dataset_info.write_to_directory(__A		)
  UpperCAmelCase__      :					Optional[Any]				       =							DatasetInfo.from_directory(__A		)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(__A   ,       "dataset_info.json"		)		)







def snake_case_     (					):
  UpperCAmelCase__      :					str				       =							DatasetInfo(
      description="foo"   ,       citation="bar"   ,       homepage="https://foo.bar"   ,       license="CC0"   ,       features=Features({"a": Value("int32"		)}		)   ,       post_processed={}   ,       supervised_keys=()   ,       task_templates=[]   ,       builder_name="builder"   ,       config_name="config"   ,       version="1.0.0"   ,       splits=[{"name": "train", "num_examples": 4_2}]   ,       download_checksums={}   ,       download_size=1_3_3_7   ,       post_processing_size=4_4_2   ,       dataset_size=1_2_3_4   ,       size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4   ,       )
  UpperCAmelCase__      :					Tuple				       =							dataset_info._to_yaml_dict()
  assert sorted(__A		) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML		)
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key]   ,       (list, dict, int, str)		)
  UpperCAmelCase__      :					str				       =							yaml.safe_dump(__A		)
  UpperCAmelCase__      :					int				       =							yaml.safe_load(__A		)
  assert dataset_info_yaml_dict == reloaded







def snake_case_     (					):
  UpperCAmelCase__      :					Any				       =							DatasetInfo()
  UpperCAmelCase__      :					List[str]				       =							dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    "dataset_infos_dict"   ,       [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}		),
        DatasetInfosDict({"my_config_name": DatasetInfo()}		),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"   ,       features=Features({"a": Value("int32"		)}		)   ,       builder_name="builder"   ,       config_name="config"   ,       version="1.0.0"   ,       splits=[{"name": "train"}]   ,       download_size=4_2   ,       )
            }		),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=4_2		),
                "v2": DatasetInfo(dataset_size=1_3_3_7		),
            }		),
    ]   ,       )
def snake_case_     (					lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					Union[str, Any]				       =							str(__A		)
  dataset_infos_dict.write_to_directory(__A		)
  UpperCAmelCase__      :					Union[str, Any]				       =							DatasetInfosDict.from_directory(__A		)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    UpperCAmelCase__      :					str				       =							config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    UpperCAmelCase__      :					Optional[Any]				       =							DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()		)
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(__A   ,       "README.md"		)		)






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,
   )


