'''simple docstring'''

import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 assert isinstance(UpperCAmelCase		,     UpperCAmelCase     )
 assert dataset.num_rows == 4
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_1", "col_2", "col_3"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory'''		,     [False, True]     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Any  =					tmp_path / '''cache'''
 lowercase__				:    List[Any]  =					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  lowercase__				:    Union[str, Any]  =					SqlDatasetReader(
      '''dataset'''		,     '''sqlite:///''' + sqlite_path		,     cache_dir=UpperCAmelCase		,     keep_in_memory=UpperCAmelCase     ).read()
 _check_sql_dataset(UpperCAmelCase		,     UpperCAmelCase     )
@require_sqlalchemy
@pytest.mark.parametrize(
    '''features'''		,     [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]		,     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    int  =					tmp_path / '''cache'''
 lowercase__				:    Dict  =					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 lowercase__				:    List[str]  =					features.copy() if features else default_expected_features
 lowercase__				:    List[Any]  =					(
     Features({feature: Value(UpperCAmelCase     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 lowercase__				:    Optional[Any]  =					SqlDatasetReader('''dataset'''		,     '''sqlite:///''' + sqlite_path		,     features=UpperCAmelCase		,     cache_dir=UpperCAmelCase     ).read()
 _check_sql_dataset(UpperCAmelCase		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 with contextlib.closing(sqlitea.connect(UpperCAmelCase     )     ) as con:
  lowercase__				:    int  =					con.cursor()
  cur.execute('''SELECT * FROM dataset'''     )
  for row in cur:
   yield row
@require_sqlalchemy
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    str  =					tmp_path / '''cache'''
 lowercase__				:    List[str]  =					os.path.join(UpperCAmelCase		,     '''tmp.sql'''     )
 lowercase__				:    List[Any]  =					SqlDatasetReader('''dataset'''		,     '''sqlite:///''' + sqlite_path		,     cache_dir=UpperCAmelCase     ).read()
 SqlDatasetWriter(UpperCAmelCase		,     '''dataset'''		,     '''sqlite:///''' + output_sqlite_path		,     num_proc=1     ).write()

 lowercase__				:    Union[str, Any]  =					iter_sql_file(UpperCAmelCase     )
 lowercase__				:    Tuple  =					iter_sql_file(UpperCAmelCase     )

 for rowa, rowa in zip(UpperCAmelCase		,     UpperCAmelCase     ):
  assert rowa == rowa
@require_sqlalchemy
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Dict  =					tmp_path / '''cache'''
 lowercase__				:    Tuple  =					os.path.join(UpperCAmelCase		,     '''tmp.sql'''     )
 lowercase__				:    Union[str, Any]  =					SqlDatasetReader('''dataset'''		,     '''sqlite:///''' + sqlite_path		,     cache_dir=UpperCAmelCase     ).read()
 SqlDatasetWriter(UpperCAmelCase		,     '''dataset'''		,     '''sqlite:///''' + output_sqlite_path		,     num_proc=2     ).write()

 lowercase__				:    List[str]  =					iter_sql_file(UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					iter_sql_file(UpperCAmelCase     )

 for rowa, rowa in zip(UpperCAmelCase		,     UpperCAmelCase     ):
  assert rowa == rowa





@require_sqlalchemy
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Any  =					tmp_path / '''cache'''
 lowercase__				:    List[Any]  =					os.path.join(UpperCAmelCase		,     '''tmp.sql'''     )
 lowercase__				:    Tuple  =					SqlDatasetReader('''dataset'''		,     '''sqlite:///''' + sqlite_path		,     cache_dir=UpperCAmelCase     ).read()
 with pytest.raises(UpperCAmelCase     ):
  SqlDatasetWriter(UpperCAmelCase		,     '''dataset'''		,     '''sqlite:///''' + output_sqlite_path		,     num_proc=0     ).write()


'''simple docstring'''

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 			UpperCAmelCase  (						pl.LightningModule					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase )  ->  List[str]:
        super().__init__()
        lowercase__				:    List[str]  =					model
        lowercase__				:    Dict  =					2
        lowercase__				:    Any  =					nn.Linear(self.model.config.hidden_size	,   self.num_labels )


       def _lowerCAmelCase(						self )  ->  str:
        pass


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 # load longformer model from model identifier
 lowercase__				:    Dict  =					LongformerModel.from_pretrained(UpperCAmelCase     )
 lowercase__				:    List[str]  =					LightningModel(UpperCAmelCase     )

 lowercase__				:    List[Any]  =					torch.load(UpperCAmelCase		,     map_location=torch.device('''cpu'''     )     )
 lightning_model.load_state_dict(ckpt['''state_dict''']     )

 # init longformer question answering model
 lowercase__				:    Optional[int]  =					LongformerForQuestionAnswering.from_pretrained(UpperCAmelCase     )

 # 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(UpperCAmelCase     )

 print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""     )


if __name__ == "__main__":
  __a: List[Any]          =   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."""
  )
  __a: Tuple          =   parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

