




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





				def        a_       (self			)	->       int:
							pass







def 			__lowerCAmelCase					(    snake_case__					,      snake_case__					,      snake_case__						):
			# load longformer model from model identifier
			__UpperCamelCase      :			Tuple									=   LongformerModel.from_pretrained(snake_case__						)
			__UpperCamelCase      :			Tuple									=   LightningModel(snake_case__						)

			__UpperCamelCase      :			List[Any]									=   torch.load(snake_case__					,      map_location=torch.device("cpu"						)						)
			lightning_model.load_state_dict(ckpt["state_dict"]						)

			# init longformer question answering model
			__UpperCamelCase      :			int									=   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__":
				_lowerCAmelCase      	=					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      	=					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'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_					:    int      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Tuple      	=			{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='big_bird'




				def __init__(  self     :					Optional[int]					,				__a     :					Dict=5_03_58					,				__a     :					str=7_68					,				__a     :					List[Any]=12					,				__a     :					List[str]=12					,				__a     :					Union[str, Any]=30_72					,				__a     :					str="gelu_new"					,				__a     :					Dict=0.1					,				__a     :					Union[str, Any]=0.1					,				__a     :					Any=40_96					,				__a     :					int=2					,				__a     :					Tuple=0.02					,				__a     :					List[Any]=1e-1_2					,				__a     :					int=True					,				__a     :					List[str]=0					,				__a     :					Tuple=1					,				__a     :					Optional[Any]=2					,				__a     :					Tuple=66					,				__a     :					str="block_sparse"					,				__a     :					Tuple=True					,				__a     :					Optional[int]=False					,				__a     :					str=64					,				__a     :					Tuple=3					,				__a     :					Any=None					,				**__a     :					Dict					,				):
								super().__init__(
								    pad_token_id=__a					,				bos_token_id=__a					,				eos_token_id=__a					,				sep_token_id=__a					,				**__a					,				)

								_a								= vocab_size
								_a								= max_position_embeddings
								_a								= hidden_size
								_a								= num_hidden_layers
								_a								= num_attention_heads
								_a								= intermediate_size
								_a								= hidden_act
								_a								= hidden_dropout_prob
								_a								= attention_probs_dropout_prob
								_a								= initializer_range
								_a								= type_vocab_size
								_a								= layer_norm_eps
								_a								= use_cache

								_a								= rescale_embeddings
								_a								= attention_type
								_a								= use_bias
								_a								= block_size
								_a								= num_random_blocks
								_a								= classifier_dropout


class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				@property
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								if self.task == "multiple-choice":
												_a								= {0: "batch", 1: "choice", 2: "sequence"}
								else:
												_a								= {0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								    ]					)
