






import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


A_						:						Union[str, Any]						      =					datasets.utils.logging.get_logger(__name__)

A_						:						Optional[Any]						      =					['names', 'prefix']
A_						:						List[str]						      =					['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
A_						:						List[Any]						      =					['encoding_errors', 'on_bad_lines']
A_						:						Optional[Any]						      =					['date_format']




@dataclass
class 							_lowerCAmelCase(			datasets.BuilderConfig   ):





       """simple docstring"""







       a							:						str										=","
       a							:						Optional[str]										=None
       a							:						Optional[Union[int, List[int], str]]										="infer"
       a							:						Optional[List[str]]										=None
       a							:						Optional[List[str]]										=None
       a							:						Optional[Union[int, str, List[int], List[str]]]										=None
       a							:						Optional[Union[List[int], List[str]]]										=None
       a							:						Optional[str]										=None
       a							:						bool										=True
       a							:						Optional[Literal["c", "python", "pyarrow"]]										=None
       a							:						Dict[Union[int, str], Callable[[Any], Any]]										=None
       a							:						Optional[list]										=None
       a							:						Optional[list]										=None
       a							:						bool										=False
       a							:						Optional[Union[int, List[int]]]										=None
       a							:						Optional[int]										=None
       a							:						Optional[Union[str, List[str]]]										=None
       a							:						bool										=True
       a							:						bool										=True
       a							:						bool										=False
       a							:						bool										=True
       a							:						Optional[str]										=None
       a							:						str										="."
       a							:						Optional[str]										=None
       a							:						str										='"'
       a							:						int										=0
       a							:						Optional[str]										=None
       a							:						Optional[str]										=None
       a							:						Optional[str]										=None
       a							:						Optional[str]										=None
       a							:						bool										=True
       a							:						bool										=True
       a							:						int										=0
       a							:						bool										=True
       a							:						bool										=False
       a							:						Optional[str]										=None
       a							:						int										=10000
       a							:						Optional[datasets.Features]										=None
       a							:						Optional[str]										="strict"
       a							:						Literal["error", "warn", "skip"]										="error"
       a							:						Optional[str]										=None





       def      _a       (   self				):
         if self.delimiter is not None:
           UpperCamelCase_:   Optional[Any]        =							self.delimiter
         if self.column_names is not None:
           UpperCamelCase_:   int        =							self.column_names





       @property
       def      _a       (   self				):
         UpperCamelCase_:   Any        =							{
             'sep': self.sep,
             'header': self.header,
             'names': self.names,
             'index_col': self.index_col,
             'usecols': self.usecols,
             'prefix': self.prefix,
             'mangle_dupe_cols': self.mangle_dupe_cols,
             'engine': self.engine,
             'converters': self.converters,
             'true_values': self.true_values,
             'false_values': self.false_values,
             'skipinitialspace': self.skipinitialspace,
             'skiprows': self.skiprows,
             'nrows': self.nrows,
             'na_values': self.na_values,
             'keep_default_na': self.keep_default_na,
             'na_filter': self.na_filter,
             'verbose': self.verbose,
             'skip_blank_lines': self.skip_blank_lines,
             'thousands': self.thousands,
             'decimal': self.decimal,
             'lineterminator': self.lineterminator,
             'quotechar': self.quotechar,
             'quoting': self.quoting,
             'escapechar': self.escapechar,
             'comment': self.comment,
             'encoding': self.encoding,
             'dialect': self.dialect,
             'error_bad_lines': self.error_bad_lines,
             'warn_bad_lines': self.warn_bad_lines,
             'skipfooter': self.skipfooter,
             'doublequote': self.doublequote,
             'memory_map': self.memory_map,
             'float_precision': self.float_precision,
             'chunksize': self.chunksize,
             'encoding_errors': self.encoding_errors,
             'on_bad_lines': self.on_bad_lines,
             'date_format': self.date_format,
         }

         # some kwargs must not be passed if they don't have a default value
         # some others are deprecated and we can also not pass them if they are the default value
         for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
           if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()    ,     _lowerCamelCase				):
             del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
         if not (datasets.config.PANDAS_VERSION.major >= 2):
           for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
             del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
         if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
           for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
             del pd_read_csv_kwargs[pd_read_csv_parameter]

         return pd_read_csv_kwargs







class 							_lowerCAmelCase(			datasets.ArrowBasedBuilder   ):





       """simple docstring"""







       a							:						Dict										=CsvConfig





       def      _a       (   self				):
         return datasets.DatasetInfo(features=self.config.features				)





       def      _a       (   self    ,     _lowerCamelCase				):
         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}'''				)
         UpperCamelCase_:   Tuple        =							dl_manager.download_and_extract(self.config.data_files				)
         if isinstance(_lowerCamelCase    ,     (str, list, tuple)				):
           UpperCamelCase_:   List[Any]        =							data_files
           if isinstance(_lowerCamelCase    ,     _lowerCamelCase				):
             UpperCamelCase_:   str        =							[files]
           UpperCamelCase_:   Tuple        =							[dl_manager.iter_files(_lowerCamelCase				) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,     gen_kwargs={'files': files}				)]
         UpperCamelCase_:   Tuple        =							[]
         for split_name, files in data_files.items():
           if isinstance(_lowerCamelCase    ,     _lowerCamelCase				):
             UpperCamelCase_:   Dict        =							[files]
           UpperCamelCase_:   int        =							[dl_manager.iter_files(_lowerCamelCase				) for file in files]
           splits.append(datasets.SplitGenerator(name=_lowerCamelCase    ,     gen_kwargs={'files': files}				)				)
         return splits





       def      _a       (   self    ,     _lowerCamelCase				):
         if self.config.features is not None:
           UpperCamelCase_:   List[Any]        =							self.config.features.arrow_schema
           if all(not require_storage_cast(_lowerCamelCase				) for feature in self.config.features.values()				):
             # cheaper cast
             UpperCamelCase_:   Optional[int]        =							pa.Table.from_arrays([pa_table[field.name] for field in schema]    ,     schema=_lowerCamelCase				)
           else:
             # more expensive cast; allows str <-> int/float or str to Audio for example
             UpperCamelCase_:   int        =							table_cast(_lowerCamelCase    ,     _lowerCamelCase				)
         return pa_table





       def      _a       (   self    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[str]        =							self.config.features.arrow_schema if self.config.features else None
         # dtype allows reading an int column as str
         UpperCamelCase_:   Dict        =							(
             {
                 name: dtype.to_pandas_dtype() if not require_storage_cast(_lowerCamelCase				) else object
                 for name, dtype, feature in zip(schema.names    ,     schema.types    ,     self.config.features.values()				)
             }
             if schema is not None
             else None
         )
         for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase				)				):
           UpperCamelCase_:   Optional[Any]        =							pd.read_csv(_lowerCamelCase    ,     iterator=_lowerCamelCase    ,     dtype=_lowerCamelCase    ,     **self.config.pd_read_csv_kwargs				)
           try:
             for batch_idx, df in enumerate(_lowerCamelCase				):
               UpperCamelCase_:   Union[str, Any]        =							pa.Table.from_pandas(_lowerCamelCase				)
               # 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 (file_idx, batch_idx), self._cast_table(_lowerCamelCase				)
           except ValueError as e:
             logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase				)}: {e}'''				)
             raise



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		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(__magic_name__       ,       __magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						emb.weight.shape
		lowercase__	     =						nn.Linear(__magic_name__       ,       __magic_name__       ,       bias=__magic_name__	)
		lowercase__	     =						emb.weight.data
		return lin_layer






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str="facebook/mbart-large-en-ro"       ,       __magic_name__	:				Any=False       ,       __magic_name__	:				int=False	)  ->    Optional[int]:
		"""simple docstring"""




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

		lowercase__	     =						MBartConfig.from_pretrained(__magic_name__       ,       vocab_size=__magic_name__	)
		if mbart_aa and finetuned:
				lowercase__	     =						"""relu"""

		lowercase__	     =						state_dict["""decoder.embed_tokens.weight"""]
		lowercase__	     =						MBartForConditionalGeneration(__magic_name__	)
		model.model.load_state_dict(__magic_name__	)

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

		return model


if __name__ == "__main__":
	A			:						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')
	A			:						Any        =  parser.parse_args()
	A			:						str        =  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)

