






'''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			:Union[str, Any]       ,							lowerCAmelCase__			:List[Any]				)				->  Dict:


										'''simple docstring'''




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






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


										'''simple docstring'''




										pass

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Dict:



			"""simple docstring"""



			snake_case_				:					str   =       LongformerModel.from_pretrained(__magic_name__						)
			snake_case_				:					Union[str, Any]   =       LightningModel(__magic_name__						)

			snake_case_				:					str   =       torch.load(__magic_name__     ,map_location=torch.device("cpu"						)						)
			lightning_model.load_state_dict(ckpt["state_dict"]						)

			# init longformer question answering model
			snake_case_				:					int   =       LongformerForQuestionAnswering.from_pretrained(__magic_name__						)

			# 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(__magic_name__						)

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


if __name__ == "__main__":
		__lowerCamelCase   :					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.'''
		)
		__lowerCamelCase   :					Optional[int]   							=					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'''




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 	__UpperCAmelCase       ( __magic_name__=None						)->    List[str]:



			"""simple docstring"""



			if subparsers is not None:
						snake_case_				:					List[str]   =       subparsers.add_parser("test"						)
			else:
						snake_case_				:					List[Any]   =       argparse.ArgumentParser("Accelerate test command"						)

			parser.add_argument(
			    "--config_file"     ,default=__magic_name__     ,help=(
			        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
			        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
			        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
			        "with 'huggingface'."
			    )     ,)

			if subparsers is not None:
						parser.set_defaults(func=__magic_name__						)
			return parser

def 	__UpperCAmelCase       ( __magic_name__						)->    Tuple:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       os.path.sep.join(__file__.split(os.path.sep						)[:-2] + ["test_utils", "scripts", "test_script.py"]						)

			if args.config_file is None:
						snake_case_				:					str   =       script_name
			else:
						snake_case_				:					Any   =       F'''--config_file={args.config_file} {script_name}'''

			snake_case_				:					Union[str, Any]   =       ["accelerate-launch"] + test_args.split()
			snake_case_				:					Optional[int]   =       execute_subprocess_async(__magic_name__     ,env=os.environ.copy()						)
			if result.returncode == 0:
						print("Test is a success! You are ready for your distributed training!"						)

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Dict   =       test_command_parser()
			snake_case_				:					Dict   =       parser.parse_args()
			test_command(__magic_name__						)


if __name__ == "__main__":
		main()




