






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


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

SCREAMING_SNAKE_CASE 							=						['names', 'prefix']
SCREAMING_SNAKE_CASE 							=						['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
SCREAMING_SNAKE_CASE 							=						['encoding_errors', 'on_bad_lines']
SCREAMING_SNAKE_CASE 							=						['date_format']
@dataclass
class 							A_    (						datasets.BuilderConfig			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			str =					","
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[Union[int, List[int], str]] =					"infer"
 _SCREAMING_SNAKE_CASE							:			Optional[List[str]] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[List[str]] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[Union[int, str, List[int], List[str]]] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[Union[List[int], List[str]]] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					None
 _SCREAMING_SNAKE_CASE							:			bool =					True
 _SCREAMING_SNAKE_CASE							:			Optional[Literal["c", "python", "pyarrow"]] =					None
 _SCREAMING_SNAKE_CASE							:			Dict[Union[int, str], Callable[[Any], Any]] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[list] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[list] =					None
 _SCREAMING_SNAKE_CASE							:			bool =					False
 _SCREAMING_SNAKE_CASE							:			Optional[Union[int, List[int]]] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[int] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[Union[str, List[str]]] =					None
 _SCREAMING_SNAKE_CASE							:			bool =					True
 _SCREAMING_SNAKE_CASE							:			bool =					True
 _SCREAMING_SNAKE_CASE							:			bool =					False
 _SCREAMING_SNAKE_CASE							:			bool =					True
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					None
 _SCREAMING_SNAKE_CASE							:			str =					"."
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					None
 _SCREAMING_SNAKE_CASE							:			str =					'"'
 _SCREAMING_SNAKE_CASE							:			int =					0
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					None
 _SCREAMING_SNAKE_CASE							:			bool =					True
 _SCREAMING_SNAKE_CASE							:			bool =					True
 _SCREAMING_SNAKE_CASE							:			int =					0
 _SCREAMING_SNAKE_CASE							:			bool =					True
 _SCREAMING_SNAKE_CASE							:			bool =					False
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					None
 _SCREAMING_SNAKE_CASE							:			int =					1_0_0_0_0
 _SCREAMING_SNAKE_CASE							:			Optional[datasets.Features] =					None
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					"strict"
 _SCREAMING_SNAKE_CASE							:			Literal["error", "warn", "skip"] =					"error"
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					None


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     if self.delimiter is not None:
         _UpperCAmelCase				:      str			  =				self.delimiter
     if self.column_names is not None:
         _UpperCAmelCase				:      str			  =				self.column_names


 @property
 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				{
         '''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()							,  _A):
             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 							A_    (						datasets.ArrowBasedBuilder			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					CsvConfig


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     return datasets.DatasetInfo(features=self.config.features)


 def  snake_case__				(	self							,  _A) ->		List[str]:

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A) ->		pa.Table:

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.config.features.arrow_schema if self.config.features else None
     # dtype allows reading an int column as str
     _UpperCAmelCase				:      str			  =				(
         {
             name: dtype.to_pandas_dtype() if not require_storage_cast(_A) 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(_A)):
         _UpperCAmelCase				:      Any			  =				pd.read_csv(_A							,  iterator=_A							,  dtype=_A							,  **self.config.pd_read_csv_kwargs)
         try:
             for batch_idx, df in enumerate(_A):
                 _UpperCAmelCase				:      List[str]			  =				pa.Table.from_pandas(_A)
                 # 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(_A)
         except ValueError as e:
             logger.error(f'''Failed to read file \'{file}\' with error {type(_A)}: {e}''')
             raise












import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class 							A_    (						pl.LightningModule			):






 '''simple docstring'''




 def __init__(	self							,  _A) ->		List[str]:

     """simple docstring"""



     super().__init__()
     _UpperCAmelCase				:      Dict			  =				model
     _UpperCAmelCase				:      Union[str, Any]			  =				2
     _UpperCAmelCase				:      List[Any]			  =				nn.Linear(self.model.config.hidden_size							,  self.num_labels)


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

     """simple docstring"""



     pass



def      _lowerCamelCase	(						__A    :							str		,		__A    :							str		,		__A    :							str )      ->					List[str]:
    # load longformer model from model identifier
    _UpperCAmelCase				:      Optional[Any]			  =				LongformerModel.from_pretrained(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				LightningModel(__A )

    _UpperCAmelCase				:      Optional[int]			  =				torch.load(__A		,		map_location=torch.device('''cpu''' ) )
    lightning_model.load_state_dict(ckpt['''state_dict'''] )

    # init longformer question answering model
    _UpperCAmelCase				:      int			  =				LongformerForQuestionAnswering.from_pretrained(__A )

    # transfer weights
    longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
    longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
    longformer_for_qa.eval()

    # save model
    longformer_for_qa.save_pretrained(__A )

    print(f'''Conversion successful. Model saved under {pytorch_dump_folder_path}''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--longformer_model',
      default=None,
      type=str,
      required=True,
      help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
  )
  parser.add_argument(
      '--longformer_question_answering_ckpt_path',
      default=None,
      type=str,
      required=True,
      help='Path the official PyTorch Lightning Checkpoint.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )




