





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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       str:
  lowercase__           =  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}}'		)
  lowercase__           =  DatasetInfosDict.from_directory(_SCREAMING_SNAKE_CASE		)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 42




@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=42		,						),
    ]		,						)
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Optional[int]:
  lowercase__           =  str(_SCREAMING_SNAKE_CASE		)
  dataset_info.write_to_directory(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  DatasetInfo.from_directory(_SCREAMING_SNAKE_CASE		)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE		,						'dataset_info.json'		)		)




def       __UpperCamelCase  ()    ->       Dict:
  lowercase__           =  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': 42}]		,						download_checksums={}		,						download_size=1337		,						post_processing_size=442		,						dataset_size=1234		,						size_in_bytes=1337 + 442 + 1234		,						)
  lowercase__           =  dataset_info._to_yaml_dict()
  assert sorted(_SCREAMING_SNAKE_CASE		) == 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)		)
  lowercase__           =  yaml.safe_dump(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  yaml.safe_load(_SCREAMING_SNAKE_CASE		)
  assert dataset_info_yaml_dict == reloaded




def       __UpperCamelCase  ()    ->       str:
  lowercase__           =  DatasetInfo()
  lowercase__           =  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=42		,						)
            }		),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42		),
                'v2': DatasetInfo(dataset_size=1337		),
            }		),
    ]		,						)
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple:
  lowercase__           =  str(_SCREAMING_SNAKE_CASE		)
  dataset_infos_dict.write_to_directory(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  DatasetInfosDict.from_directory(_SCREAMING_SNAKE_CASE		)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    lowercase__           =  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
    lowercase__           =  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(_SCREAMING_SNAKE_CASE		,						'README.md'		)		)












import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  lowercase__           =  [
      'encoder.version',
      'decoder.version',
      'model.encoder.version',
      'model.decoder.version',
      '_float_tensor',
      'decoder.output_projection.weight',
  ]
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Dict:
  lowercase__      ,   lowercase__           =  emb.weight.shape
  lowercase__           =  nn.Linear(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						bias=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  emb.weight.data
  return lin_layer




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE="facebook/mbart-large-en-ro"		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		)    ->       str:
  lowercase__           =  torch.load(_SCREAMING_SNAKE_CASE		,						map_location='cpu'		)['model']
  remove_ignore_keys_(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  state_dict['encoder.embed_tokens.weight'].shape[0]

  lowercase__           =  MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE		,						vocab_size=_SCREAMING_SNAKE_CASE		)
  if mbart_aa and finetuned:
    lowercase__           =  'relu'

  lowercase__           =  state_dict['decoder.embed_tokens.weight']
  lowercase__           =  MBartForConditionalGeneration(_SCREAMING_SNAKE_CASE		)
  model.model.load_state_dict(_SCREAMING_SNAKE_CASE		)

  if finetuned:
    lowercase__           =  make_linear_from_emb(model.model.shared		)

  return model


if __name__ == "__main__":
      lowercase_          =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
      )
      parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument(
          """--hf_config""",
          default="""facebook/mbart-large-cc25""",
          type=str,
          help="""Which huggingface architecture to use: mbart-large""",
      )
      parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
      parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
      lowercase_          =		parser.parse_args()
      lowercase_          =		convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)


