





"""simple docstring"""


import os
from collections import deque

import torch
from torch.utils.data import Dataset



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  def __init__(		self :							Any       , __a :							List[str]=""       , __a :							List[Any]="train"						)		->					Optional[Any]:
        assert os.path.isdir(__a						)

        _UpperCamelCase   :						Union[str, Any]    =							[]
        _UpperCamelCase   :						Optional[int]    =							os.listdir(__a						)
        for story_filename in story_filenames_list:
              if "summary" in story_filename:
                    continue
              _UpperCamelCase   :						Tuple    =							os.path.join(__a       , __a						)
              if not os.path.isfile(__a						):
                    continue
              self.documents.append(__a						)

  def __len__(		self :							List[str]						)		->					List[Any]:
        return len(self.documents						)


  def __getitem__(		self :							str       , __a :							int						)		->					int:
        _UpperCamelCase   :						Optional[Any]    =							self.documents[idx]
        _UpperCamelCase   :						Tuple    =							document_path.split("/"						)[-1]
        with open(__a       , encoding="utf-8"						) as source:
              _UpperCamelCase   :						Tuple    =							source.read()
              _UpperCamelCase,	_UpperCamelCase   :						Optional[int]    =							process_story(__a						)
        return document_name, story_lines, summary_lines





def        lowercase__     (       lowercase_      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							list(filter(lambda lowercase_      : len(lowercase_      ) != 0		,[line.strip() for line in raw_story.split("\n"      )]      )      )

      # for some unknown reason some lines miss a period, add it
      _UpperCamelCase   :						Optional[int]    =							[_add_missing_period(lowercase_      ) for line in nonempty_lines]

      # gather article lines
      _UpperCamelCase   :						Optional[Any]    =							[]
      _UpperCamelCase   :						Dict    =							deque(lowercase_      )
      while True:
            try:
                  _UpperCamelCase   :						Optional[Any]    =							lines.popleft()
                  if element.startswith("@highlight"      ):
                        break
                  story_lines.append(lowercase_      )
            except IndexError:
                  # if "@highlight" is absent from the file we pop
                  # all elements until there is None, raising an exception.
                  return story_lines, []

    # gather summary lines
      _UpperCamelCase   :						Dict    =							list(filter(lambda lowercase_      : not t.startswith("@highlight"      )		,lowercase_      )      )

      return story_lines, summary_lines




def        lowercase__     (       lowercase_      )       ->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase   :						Union[str, Any]    =							[".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
      if line.startswith("@highlight"      ):
            return line
      if line[-1] in END_TOKENS:
            return line
      return line + "."




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		Dict:




      """simple docstring"""

      if len(lowercase_      ) > block_size:
            return sequence[:block_size]
      else:
            sequence.extend([pad_token_id] * (block_size - len(lowercase_      ))      )
            return sequence




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Dict:




      """simple docstring"""

      _UpperCamelCase   :						int    =							torch.ones_like(lowercase_      )
      _UpperCamelCase   :						int    =							sequence == pad_token_id
      _UpperCamelCase   :						Union[str, Any]    =							0
      return mask




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							[tokenizer.encode(lowercase_      ) for line in story_lines]
      _UpperCamelCase   :						List[str]    =							[token for sentence in story_lines_token_ids for token in sentence]
      _UpperCamelCase   :						Dict    =							[tokenizer.encode(lowercase_      ) for line in summary_lines]
      _UpperCamelCase   :						List[str]    =							[token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Optional[int]:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							[]
      for sequence in batch:
            _UpperCamelCase   :						Tuple    =							-1
            _UpperCamelCase   :						int    =							[]
            for s in sequence:
                  if s == separator_token_id:
                        sentence_num += 1
                  embeddings.append(sentence_num % 2      )
            batch_embeddings.append(lowercase_      )
      return torch.tensor(lowercase_      )







"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def        lowercase__     (       lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							emb.weight.shape
      _UpperCamelCase   :						str    =							nn.Linear(lowercase_		,lowercase_		,bias=lowercase_      )
      _UpperCamelCase   :						List[Any]    =							emb.weight.data
      return lin_layer




def        lowercase__     (       lowercase_		,lowercase_="facebook/mbart-large-en-ro"		,lowercase_=False		,lowercase_=False      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							torch.load(lowercase_		,map_location="cpu"      )["model"]
      remove_ignore_keys_(lowercase_      )
      _UpperCamelCase   :						str    =							state_dict["encoder.embed_tokens.weight"].shape[0]

      _UpperCamelCase   :						int    =							MBartConfig.from_pretrained(lowercase_		,vocab_size=lowercase_      )
      if mbart_aa and finetuned:
            _UpperCamelCase   :						Union[str, Any]    =							"relu"

      _UpperCamelCase   :						Optional[int]    =							state_dict["decoder.embed_tokens.weight"]
      _UpperCamelCase   :						List[str]    =							MBartForConditionalGeneration(lowercase_      )
      model.model.load_state_dict(lowercase_      )

      if finetuned:
            _UpperCamelCase   :						Optional[Any]    =							make_linear_from_emb(model.model.shared      )

      return model


if __name__ == "__main__":
  lowerCamelCase__						= 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")
  lowerCamelCase__						= parser.parse_args()
  lowerCamelCase__						= 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)
