





# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowercase_          =		get_logger(__name__)
class     SCREAMING_SNAKE_CASE       :
   _UpperCamelCase   :		Any			 =	'dummy_data'
   _UpperCamelCase   :		str			 =	'datasets'
   _UpperCamelCase   :		Any			 =	False


   def __init__(   self      :    Optional[int]      ,					a      :    str      ,					a      :    str      ,					a      :    Union[Version, str]      ,					a      :    Optional[str] = None      ,					a      :    bool = False      ,					a      :    bool = True      ,					a      :    Optional[List[Callable]] = None      ,					)->       int:





     """simple docstring"""







     lowercase__           =  0
     lowercase__           =  dataset_name
     lowercase__           =  cache_dir
     lowercase__           =  use_local_dummy_data
     lowercase__           =  config
     # download_callbacks take a single url as input
     lowercase__           =  download_callbacks or []
     # if False, it doesn't load existing files and it returns the paths of the dummy files relative
     # to the dummy_data zip file root
     lowercase__           =  load_existing_dummy_data

     # TODO(PVP, QL) might need to make this more general
     lowercase__           =  str(a	)
     # to be downloaded
     lowercase__           =  None
     lowercase__           =  None


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Optional[Any]:





     """simple docstring"""







     if self._dummy_file is None:
       lowercase__           =  self.download_dummy_data()
     return self._dummy_file


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       int:





     """simple docstring"""







     if self.config is not None:
       # structure is dummy / config_name / version_name
       return os.path.join('dummy'      ,					self.config.name      ,					self.version_name	)
     # structure is dummy / version_name
     return os.path.join('dummy'      ,					self.version_name	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Optional[int]:





     """simple docstring"""







     return os.path.join(self.dummy_data_folder      ,					'dummy_data.zip'	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  (
         self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
     )

     lowercase__           =  cached_path(
         a      ,					cache_dir=self.cache_dir      ,					extract_compressed_file=a      ,					force_extract=a	)

     return os.path.join(a      ,					self.dummy_file_name	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       List[Any]:





     """simple docstring"""







     return os.path.join(self.datasets_scripts_dir      ,					self.dataset_name      ,					self.dummy_zip_file	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Optional[Any]:





     """simple docstring"""







     if self._bucket_url is None:
       lowercase__           =  hf_github_url(self.dataset_name      ,					self.dummy_zip_file.replace(os.sep      ,					'/'	)	)
     return self._bucket_url


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Union[str, Any]:





     """simple docstring"""







     if os.path.isdir(self.dummy_file	):
       return self.dummy_file
     # else cut off path to file -> example `xsum`.
     return "/".join(self.dummy_file.replace(os.sep      ,					'/'	).split('/'	)[:-1]	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    Optional[int]      ,					*a      :    List[str]	)->       List[str]:





     """simple docstring"""







     if self.load_existing_dummy_data:
       # dummy data is downloaded and tested
       lowercase__           =  self.dummy_file
     else:
       # dummy data cannot be downloaded and only the path to dummy file is returned
       lowercase__           =  self.dummy_file_name

     # special case when data_url is a dict
     if isinstance(a      ,					a	):
       return self.create_dummy_data_dict(a      ,					a	)
     elif isinstance(a      ,					(list, tuple)	):
       return self.create_dummy_data_list(a      ,					a	)
     else:
       return self.create_dummy_data_single(a      ,					a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    Union[str, Any]      ,					*a      :    Optional[Any]	)->       Optional[int]:





     """simple docstring"""







     return self.download_and_extract(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    int      ,					a      :    List[str]	)->       List[Any]:





     """simple docstring"""







     return self.download_and_extract(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    Tuple      ,					*a      :    int      ,					**a      :    List[str]	)->       Tuple:





     """simple docstring"""







     return path


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Any:





     """simple docstring"""







     return {}


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    Optional[int]      ,					a      :    Union[str, Any]	)->       int:





     """simple docstring"""







     lowercase__           =  {}
     for key, single_urls in data_url.items():
       for download_callback in self.download_callbacks:
         if isinstance(a      ,					a	):
           for single_url in single_urls:
             download_callback(a	)
         else:
           lowercase__           =  single_urls
           download_callback(a	)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
       if isinstance(a      ,					a	):
         lowercase__           =  [os.path.join(a      ,					urllib.parse.quote_plus(Path(a	).name	)	) for x in single_urls]
       else:
         lowercase__           =  single_urls
         lowercase__           =  os.path.join(a      ,					urllib.parse.quote_plus(Path(a	).name	)	)
       lowercase__           =  value

     # make sure that values are unique
     if all(isinstance(a      ,					a	) for i in dummy_data_dict.values()	) and len(set(dummy_data_dict.values()	)	) < len(
         dummy_data_dict.values()	):
       # append key to value to make its name unique
       lowercase__           =  {key: value + key for key, value in dummy_data_dict.items()}

     return dummy_data_dict


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    List[Any]      ,					a      :    Dict	)->       Tuple:





     """simple docstring"""







     lowercase__           =  []
     # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
     lowercase__           =  all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'      ,					a	)	) for url in data_url	)
     lowercase__           =  all(
         url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'	) for url in data_url	)
     if data_url and (is_tf_records or is_pubmed_records):
       lowercase__           =  [data_url[0]] * len(a	)
     for single_url in data_url:
       for download_callback in self.download_callbacks:
         download_callback(a	)
       # we force the name of each key to be the last file / folder name of the url path
       # if the url has arguments, we need to encode them with urllib.parse.quote_plus
       lowercase__           =  os.path.join(a      ,					urllib.parse.quote_plus(single_url.split('/'	)[-1]	)	)
       dummy_data_list.append(a	)
     return dummy_data_list


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    Optional[int]      ,					a      :    Optional[int]	)->       Optional[Any]:





     """simple docstring"""







     for download_callback in self.download_callbacks:
       download_callback(a	)
     # we force the name of each key to be the last file / folder name of the url path
     # if the url has arguments, we need to encode them with urllib.parse.quote_plus
     lowercase__           =  os.path.join(a      ,					urllib.parse.quote_plus(data_url.split('/'	)[-1]	)	)
     if os.path.exists(a	) or not self.load_existing_dummy_data:
       return value
     else:
       # Backward compatibility, maybe deprecate at one point.
       # For many datasets with single url calls to dl_manager.download_and_extract,
       # the dummy_data.zip file is actually the zipped downloaded file
       # while now we expected the dummy_data.zip file to be a directory containing
       # the downloaded file.
       return path_to_dummy_data


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Tuple:





     """simple docstring"""







     pass


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       str:





     """simple docstring"""







     pass


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    List[str]	)->       int:





     """simple docstring"""







     def _iter_archive_members(a      :    Union[str, Any]	):
       # this preserves the order of the members inside the ZIP archive
       lowercase__           =  Path(self.dummy_file	).parent
       lowercase__           =  path.relative_to(a	)
       with ZipFile(self.local_path_to_dummy_data	) as zip_file:
         lowercase__           =  zip_file.namelist()
       for member in members:
         if member.startswith(relative_path.as_posix()	):
           yield dummy_parent_path.joinpath(a	)

     lowercase__           =  Path(a	)
     lowercase__           =  _iter_archive_members(a	) if self.use_local_dummy_data else path.rglob('*'	)
     for file_path in file_paths:
       if file_path.is_file() and not file_path.name.startswith(('.', '__')	):
         yield file_path.relative_to(a	).as_posix(), file_path.open('rb'	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    List[str]	)->       int:





     """simple docstring"""







     if not isinstance(a      ,					a	):
       lowercase__           =  [paths]
     for path in paths:
       if os.path.isfile(a	):
         if os.path.basename(a	).startswith(('.', '__')	):
           return
         yield path
       else:
         for dirpath, dirnames, filenames in os.walk(a	):
           if os.path.basename(a	).startswith(('.', '__')	):
             continue
           dirnames.sort()
           for filename in sorted(a	):
             if filename.startswith(('.', '__')	):
               continue
             yield os.path.join(a      ,					a	)









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)


