



"""simple docstring"""






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:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Any)       -> Optional[Any]:

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase   :						Optional[int]				= model
				__lowerCAmelCase   :						Optional[int]				= 2
				__lowerCAmelCase   :						List[Any]				= nn.Linear(self.model.config.hidden_size ,    self.num_labels)







	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Dict:

				"""simple docstring"""

				pass






def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						List[str]				= LongformerModel.from_pretrained(__snake_case			)
			__lowerCAmelCase   :						Union[str, Any]				= LightningModel(__snake_case			)

			__lowerCAmelCase   :						Optional[Any]				= torch.load(__snake_case      ,map_location=torch.device("cpu"			)			)
			lightning_model.load_state_dict(ckpt["state_dict"]			)

			# init longformer question answering model
			__lowerCAmelCase   :						Union[str, Any]				= LongformerForQuestionAnswering.from_pretrained(__snake_case			)

			# 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(__snake_case			)

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


if __name__ == "__main__":
							__snake_case							:     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.'
							)
							__snake_case							:     Tuple   =					parser.parse_args()
							convert_longformer_qa_checkpoint_to_pytorch(
							    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
							)




"""simple docstring"""






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__snake_case							:     str   =					pytest.mark.integration


@pytest.mark.parametrize("path"      ,["paws", "csv"]			)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
			inspect_dataset(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						str				= path + ".py"
			assert script_name in os.listdir(__snake_case			)
			assert "__pycache__" not in os.listdir(__snake_case			)


@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"			)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"			)
@pytest.mark.parametrize("path"      ,["accuracy"]			)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			inspect_metric(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						str				= path + ".py"
			assert script_name in os.listdir(__snake_case			)
			assert "__pycache__" not in os.listdir(__snake_case			)


@pytest.mark.parametrize(
    "path, config_name, expected_splits"      ,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[int]:
			__lowerCAmelCase   :						str				= get_dataset_config_info(__snake_case      ,config_name=__snake_case			)
			assert info.config_name == config_name
			assert list(info.splits.keys()			) == expected_splits


@pytest.mark.parametrize(
    "path, config_name, expected_exception"      ,[
        ("paws", None, ValueError),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     List[str]:
			with pytest.raises(__snake_case			):
						get_dataset_config_info(__snake_case      ,config_name=__snake_case			)


@pytest.mark.parametrize(
    "path, expected"      ,[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						Dict				= get_dataset_config_names(__snake_case			)
			assert expected in config_names


@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"      ,[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[Any]:
			__lowerCAmelCase   :						List[Any]				= get_dataset_infos(__snake_case			)
			assert list(infos.keys()			) == expected_configs
			__lowerCAmelCase   :						List[Any]				= expected_configs[0]
			assert expected_config in infos
			__lowerCAmelCase   :						Tuple				= infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()			) == expected_splits_in_first_config


@pytest.mark.parametrize(
    "path, expected_config, expected_splits"      ,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Union[str, Any]:
			__lowerCAmelCase   :						Tuple				= get_dataset_infos(__snake_case			)
			assert expected_config in infos
			__lowerCAmelCase   :						int				= infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception"      ,[
        ("paws", None, ValueError),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			with pytest.raises(__snake_case			):
						get_dataset_split_names(__snake_case      ,config_name=__snake_case			)