


"""simple docstring"""






import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__snake_case		=       datasets.utils.logging.get_logger(__name__)


@dataclass
class     __lowerCamelCase  (					datasets.BuilderConfig				):

     '''simple docstring'''
     A_       :	int    =		10_000
     A_       :	Optional[List[str]]    =		None
     A_       :	Optional[datasets.Features]    =		None







class     __lowerCamelCase  (					datasets.ArrowBasedBuilder				):

     '''simple docstring'''
     A_       :	Any    =		ParquetConfig


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          return datasets.DatasetInfo(features=self.config.features							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Tuple:
          if not self.config.data_files:
               raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'							)
          _a    	=				dl_manager.download_and_extract(self.config.data_files							)
          if isinstance(__UpperCAmelCase    ,				(str, list, tuple)							):
               _a    	=				data_files
               if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							):
                    _a    	=				[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               _a    	=				[dl_manager.iter_files(__UpperCAmelCase							) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,				gen_kwargs={'''files''': files}							)]
          _a    	=				[]
          for split_name, files in data_files.items():
               if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							):
                    _a    	=				[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               _a    	=				[dl_manager.iter_files(__UpperCAmelCase							) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                    for file in itertools.chain.from_iterable(__UpperCAmelCase							):
                         with open(__UpperCAmelCase    ,				'''rb'''							) as f:
                              _a    	=				datasets.Features.from_arrow_schema(pq.read_schema(__UpperCAmelCase							)							)
                         break
               splits.append(datasets.SplitGenerator(name=__UpperCAmelCase    ,				gen_kwargs={'''files''': files}							)							)
          return splits


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							pa.Table:
          if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               _a    	=				table_cast(__UpperCAmelCase    ,				self.info.features.arrow_schema							)
          return pa_table





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							str:
          _a    	=				self.info.features.arrow_schema if self.info.features is not None else None
          if self.info.features is not None and self.config.columns is not None:
               if sorted(field.name for field in schema							) != sorted(self.config.columns							):
                    raise ValueError(
                        F'Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''							)
          for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCAmelCase							)							):
               with open(__UpperCAmelCase    ,				'''rb'''							) as f:
                    _a    	=				pq.ParquetFile(__UpperCAmelCase							)
                    try:
                         for batch_idx, record_batch in enumerate(
                             parquet_file.iter_batches(batch_size=self.config.batch_size    ,				columns=self.config.columns							)							):
                              _a    	=				pa.Table.from_batches([record_batch]							)
                              # Uncomment for debugging (will print the Arrow table size and elements)
                              # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                              # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                              yield F'{file_idx}_{batch_idx}', self._cast_table(__UpperCAmelCase							)
                    except ValueError as e:
                         logger.error(F'Failed to read file \'{file}\' with error {type(__UpperCAmelCase							)}: {e}'							)
                         raise



"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     _a    	=				[
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''_float_tensor''',
         '''decoder.output_projection.weight''',
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     _a	,      _a    	=				emb.weight.shape
     _a    	=				nn.Linear(_lowerCAmelCase,     _lowerCAmelCase,     bias=_lowerCAmelCase       )
     _a    	=				emb.weight.data
     return lin_layer
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Union[str, Any]="facebook/mbart-large-en-ro",     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					int=False       ):






     """simple docstring"""

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     remove_ignore_keys_(_lowerCAmelCase       )
     _a    	=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

     _a    	=				MBartConfig.from_pretrained(_lowerCAmelCase,     vocab_size=_lowerCAmelCase       )
     if mbart_aa and finetuned:
          _a    	=				'''relu'''

     _a    	=				state_dict['''decoder.embed_tokens.weight''']
     _a    	=				MBartForConditionalGeneration(_lowerCAmelCase       )
     model.model.load_state_dict(_lowerCAmelCase       )

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

     return model


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