import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						[
        """encoder.version""",
        """decoder.version""",
        """model.encoder.version""",
        """model.decoder.version""",
        """_float_tensor""",
        """decoder.output_projection.weight""",
    ]
    for k in ignore_keys:
        state_dict.pop(__A   , __A	)







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase						,							_lowerCamelCase     :		Dict 						=						emb.weight.shape
    _lowerCamelCase     :		List[str] 						=						nn.Linear(__A   , __A   , bias=__A	)
    _lowerCamelCase     :		Optional[Any] 						=						emb.weight.data
    return lin_layer







def 						A__  (					__A   , __A="facebook/mbart-large-en-ro"   , __A=False   , __A=False	):







    '''simple docstring'''
    _lowerCamelCase     :		Union[str, Any] 						=						torch.load(__A   , map_location="""cpu"""	)["""model"""]
    remove_ignore_keys_(__A	)
    _lowerCamelCase     :		Optional[Any] 						=						state_dict["""encoder.embed_tokens.weight"""].shape[0]

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

    _lowerCamelCase     :		List[Any] 						=						state_dict["""decoder.embed_tokens.weight"""]
    _lowerCamelCase     :		Any 						=						MBartForConditionalGeneration(__A	)
    model.model.load_state_dict(__A	)

    if finetuned:
        _lowerCamelCase     :		Dict 						=						make_linear_from_emb(model.model.shared	)

    return model


if __name__ == "__main__":
    lowerCAmelCase			:							Optional[Any]     =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			:							Dict     =parser.parse_args()
    lowerCAmelCase			:							Any     =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)





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				Dict    ,  _UpperCamelCase     :				NestedDataStructureLike[PathLike]    ,  _UpperCamelCase     :				Optional[NamedSplit] = None    ,  _UpperCamelCase     :				Optional[Features] = None    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				Optional[int] = None    ,  **_UpperCamelCase     :				Tuple    ,  )				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              _UpperCamelCase    ,  split=_UpperCamelCase    ,  features=_UpperCamelCase    ,  cache_dir=_UpperCamelCase    ,  keep_in_memory=_UpperCamelCase    ,  streaming=_UpperCamelCase    ,  num_proc=_UpperCamelCase    ,  **_UpperCamelCase    ,  )
          _lowerCamelCase     :		List[Any] 						=						path_or_paths if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else {self.split: path_or_paths}
          _lowerCamelCase     :		Any 						=						Text(
              cache_dir=_UpperCamelCase    ,  data_files=_UpperCamelCase    ,  features=_UpperCamelCase    ,  **_UpperCamelCase    ,  )




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


          """simple docstring"""


          if self.streaming:
              _lowerCamelCase     :		Tuple 						=						self.builder.as_streaming_dataset(split=self.split)
          # Build regular (map-style) dataset
          else:
              _lowerCamelCase     :		List[Any] 						=						None
              _lowerCamelCase     :		Any 						=						None
              _lowerCamelCase     :		List[str] 						=						None
              _lowerCamelCase     :		Dict 						=						None

              self.builder.download_and_prepare(
                  download_config=_UpperCamelCase    ,  download_mode=_UpperCamelCase    ,  verification_mode=_UpperCamelCase    ,  base_path=_UpperCamelCase    ,  num_proc=self.num_proc    ,  )
              _lowerCamelCase     :		Optional[int] 						=						self.builder.as_dataset(
                  split=self.split    ,  verification_mode=_UpperCamelCase    ,  in_memory=self.keep_in_memory)
          return dataset




