

"""simple docstring"""





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_a       =							logging.get_logger(__name__)


_a       =							TypeVar('DatasetType', Dataset, IterableDataset)




def   _A  (  UpperCamelCase_     :       List[DatasetType],			UpperCamelCase_     :       Optional[List[float]] = None,			UpperCamelCase_     :       Optional[int] = None,			UpperCamelCase_     :       Optional[DatasetInfo] = None,			UpperCamelCase_     :       Optional[NamedSplit] = None,			UpperCamelCase_     :       Literal["first_exhausted", "all_exhausted"] = "first_exhausted",			)	->						DatasetType:
   '''simple docstring'''


   from .arrow_dataset import Dataset
   from .iterable_dataset import IterableDataset

   if not datasets:
      raise ValueError("Unable to interleave an empty list of datasets.")
   for i, dataset in enumerate(UpperCamelCase_):
      if not isinstance(UpperCamelCase_,			(Dataset, IterableDataset)):
         if isinstance(UpperCamelCase_,			(DatasetDict, IterableDatasetDict)):
            if not dataset:
               raise ValueError(
                   F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                   "is an empty dataset dictionary.")
            raise ValueError(
                F"""Dataset at position {i} has at least one split: {list(UpperCamelCase_)}\n"""
                F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase_))}']""")
         raise ValueError(
             F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_).__name__}.""")
      if i == 0:
         __lowercase		,__lowercase								=						(
             (Dataset, IterableDataset) if isinstance(UpperCamelCase_,			UpperCamelCase_) else (IterableDataset, Dataset)
         )
      elif not isinstance(UpperCamelCase_,			UpperCamelCase_):
         raise ValueError(
             F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""")
   if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
      raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy.""")
   if dataset_type is Dataset:
      return _interleave_map_style_datasets(
          UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_,			info=UpperCamelCase_,			split=UpperCamelCase_,			stopping_strategy=UpperCamelCase_)
   else:
      return _interleave_iterable_datasets(
          UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_,			info=UpperCamelCase_,			split=UpperCamelCase_,			stopping_strategy=UpperCamelCase_)




def   _A  (  UpperCamelCase_     :       List[DatasetType],			UpperCamelCase_     :       Optional[DatasetInfo] = None,			UpperCamelCase_     :       Optional[NamedSplit] = None,			UpperCamelCase_     :       int = 0,			)	->						DatasetType:
   '''simple docstring'''


   if not dsets:
      raise ValueError("Unable to concatenate an empty list of datasets.")
   for i, dataset in enumerate(UpperCamelCase_):
      if not isinstance(UpperCamelCase_,			(Dataset, IterableDataset)):
         if isinstance(UpperCamelCase_,			(DatasetDict, IterableDatasetDict)):
            if not dataset:
               raise ValueError(
                   F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                   "is an empty dataset dictionary.")
            raise ValueError(
                F"""Dataset at position {i} has at least one split: {list(UpperCamelCase_)}\n"""
                F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase_))}']""")
         raise ValueError(
             F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_).__name__}.""")
      if i == 0:
         __lowercase		,__lowercase								=						(
             (Dataset, IterableDataset) if isinstance(UpperCamelCase_,			UpperCamelCase_) else (IterableDataset, Dataset)
         )
      elif not isinstance(UpperCamelCase_,			UpperCamelCase_):
         raise ValueError(
             F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""")
   if dataset_type is Dataset:
      return _concatenate_map_style_datasets(UpperCamelCase_,			info=UpperCamelCase_,			split=UpperCamelCase_,			axis=UpperCamelCase_)
   else:
      return _concatenate_iterable_datasets(UpperCamelCase_,			info=UpperCamelCase_,			split=UpperCamelCase_,			axis=UpperCamelCase_)





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def    lowerCAmelCase_ (     __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  [
          "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    lowerCAmelCase_ (     __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__		,      UpperCAmelCase__								=  emb.weight.shape
      UpperCAmelCase__								=  nn.Linear(__A,  __A,  bias=__A							)
      UpperCAmelCase__								=  emb.weight.data
      return lin_layer




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







      '''simple docstring'''


      UpperCAmelCase__								=  torch.load(__A,  map_location="cpu"							)["model"]
      remove_ignore_keys_(__A							)
      UpperCAmelCase__								=  state_dict["encoder.embed_tokens.weight"].shape[0]

      UpperCAmelCase__								=  MBartConfig.from_pretrained(__A,  vocab_size=__A							)
      if mbart_aa and finetuned:
            UpperCAmelCase__								=  "relu"

      UpperCAmelCase__								=  state_dict["decoder.embed_tokens.weight"]
      UpperCAmelCase__								=  MBartForConditionalGeneration(__A							)
      model.model.load_state_dict(__A							)

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

      return model


if __name__ == "__main__":
      UpperCamelCase__											=					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')
      UpperCamelCase__											=					parser.parse_args()
      UpperCamelCase__											=					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)



