


import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Union[str, Any]:




									"""simple docstring"""





									super().__init__(
									    _a      ,   split=_a      ,   features=_a      ,   cache_dir=_a      ,   keep_in_memory=_a      ,   streaming=_a      ,   num_proc=_a      ,   **_a      ,   )
									_UpperCAmelCase					=      field
									_UpperCAmelCase					=      path_or_paths if isinstance(_a      ,   _a						) else {self.split: path_or_paths}
									_UpperCAmelCase					=      Json(
									    cache_dir=_a      ,   data_files=_a      ,   features=_a      ,   field=_a      ,   **_a      ,   )






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




									"""simple docstring"""





									if self.streaming:
																_UpperCAmelCase					=      self.builder.as_streaming_dataset(split=self.split						)
									# Build regular (map-style) dataset
									else:
																_UpperCAmelCase					=      None
																_UpperCAmelCase					=      None
																_UpperCAmelCase					=      None
																_UpperCAmelCase					=      None

																self.builder.download_and_prepare(
																    download_config=_a      ,   download_mode=_a      ,   verification_mode=_a      ,   base_path=_a      ,   num_proc=self.num_proc      ,   )
																_UpperCAmelCase					=      self.builder.as_dataset(
																    split=self.split      ,   verification_mode=_a      ,   in_memory=self.keep_in_memory						)
									return dataset



class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Tuple:




									"""simple docstring"""





									if num_proc is not None and num_proc <= 0:
																raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''						)

									_UpperCAmelCase					=      dataset
									_UpperCAmelCase					=      path_or_buf
									_UpperCAmelCase					=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_UpperCAmelCase					=      num_proc
									_UpperCAmelCase					=      'utf-8'
									_UpperCAmelCase					=      to_json_kwargs
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.to_json_kwargs.pop('path_or_buf'      ,   _a						)
									_UpperCAmelCase					=      self.to_json_kwargs.pop('orient'      ,   'records'						)
									_UpperCAmelCase					=      self.to_json_kwargs.pop('lines'      ,   True if orient == 'records' else False						)
									_UpperCAmelCase					=      self.to_json_kwargs.pop('index'      ,   False if orient in ['split', 'table'] else True						)
									_UpperCAmelCase					=      self.to_json_kwargs.pop('compression'      ,   _a						)

									if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																raise NotImplementedError(f'''`datasets` currently does not support {compression} compression'''						)

									if isinstance(self.path_or_buf      ,   (str, bytes, os.PathLike)						):
																with fsspec.open(self.path_or_buf      ,   'wb'      ,   compression=_a						) as buffer:
																							_UpperCAmelCase					=      self._write(file_obj=_a      ,   orient=_a      ,   lines=_a      ,   index=_a      ,   **self.to_json_kwargs						)
									else:
																if compression:
																							raise NotImplementedError(
																							    f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
																							    ' was passed. Please provide a local path instead.'						)
																_UpperCAmelCase					=      self._write(
																    file_obj=self.path_or_buf      ,   orient=_a      ,   lines=_a      ,   index=_a      ,   **self.to_json_kwargs						)
									return written
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      args

									_UpperCAmelCase					=      query_table(
									    table=self.dataset.data      ,   key=slice(_a      ,   offset + self.batch_size						)      ,   indices=self.dataset._indices      ,   )
									_UpperCAmelCase					=      batch.to_pandas().to_json(
									    path_or_buf=_a      ,   orient=_a      ,   lines=_a      ,   index=_a      ,   **_a						)
									if not json_str.endswith('\n'						):
																json_str += "\n"
									return json_str.encode(self.encoding						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      0

									if self.num_proc is None or self.num_proc == 1:
																for offset in logging.tqdm(
																    range(0      ,   len(self.dataset						)      ,   self.batch_size						)      ,   unit='ba'      ,   disable=not logging.is_progress_bar_enabled()      ,   desc='Creating json from Arrow format'      ,   ):
																							_UpperCAmelCase					=      self._batch_json((offset, orient, lines, index, to_json_kwargs)						)
																							written += file_obj.write(_a						)
									else:
																_UpperCAmelCase		,	_UpperCAmelCase					=      len(self.dataset						), self.batch_size
																with multiprocessing.Pool(self.num_proc						) as pool:
																							for json_str in logging.tqdm(
																							    pool.imap(
																							        self._batch_json      ,   [(offset, orient, lines, index, to_json_kwargs) for offset in range(0      ,   _a      ,   _a						)]      ,   )      ,   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size      ,   unit='ba'      ,   disable=not logging.is_progress_bar_enabled()      ,   desc='Creating json from Arrow format'      ,   ):
																														written += file_obj.write(_a						)

									return written




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		__a				(    pl.LightningModule      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      model
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      nn.Linear(self.model.config.hidden_size      ,   self.num_labels						)






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									pass



def 	lowerCAmelCase__			(      a__:		str      ,  a__:		str      ,  a__:		str	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      LongformerModel.from_pretrained(a__	)
							_UpperCAmelCase					=      LightningModel(a__	)

							_UpperCAmelCase					=      torch.load(a__      ,  map_location=torch.device('cpu'	)	)
							lightning_model.load_state_dict(ckpt['state_dict']	)

							# init longformer question answering model
							_UpperCAmelCase					=      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__":
		lowerCAmelCase__  :Optional[int]             =							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.'''
		)
		lowerCAmelCase__  :Dict             =							parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)
