

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   ,	A__      ):
						super().__init__()
						A__							:   str					       =		model
						A__							:   List[str]					       =		2
						A__							:   Dict					       =		nn.Linear(self.model.config.hidden_size   ,	self.num_labels      )






					def   __A      (			self      ):
						pass



def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			str )					->				List[Any]:
	# load longformer model from model identifier
	A__							:   Dict					       =		LongformerModel.from_pretrained(lowercase_ )
	A__							:   str					       =		LightningModel(lowercase_ )

	A__							:   Tuple					       =		torch.load(lowercase_   ,				map_location=torch.device("""cpu""" ) )
	lightning_model.load_state_dict(ckpt["""state_dict"""] )

	# init longformer question answering model
	A__							:   int					       =		LongformerForQuestionAnswering.from_pretrained(lowercase_ )

	# 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(lowercase_ )

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


if __name__ == "__main__":
	A_	:			Dict  	=      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_	:			str  	=      parser.parse_args()
	convert_longformer_qa_checkpoint_to_pytorch(
	    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
	)






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	'''MCTCTFeatureExtractor'''
					UpperCAmelCase__:       Optional[int]  							=	'''AutoTokenizer'''






					def __init__(			self   ,	A__   ,	A__      ):
						super().__init__(A__   ,	A__      )
						A__							:   List[str]					       =		self.feature_extractor
						A__							:   Optional[int]					       =		False






					def __call__(			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor(*A__   ,	**A__      )

						if "raw_speech" in kwargs:
							warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""      )
							A__							:   Dict					       =		kwargs.pop("""raw_speech"""      )
						else:
							A__							:   Tuple					       =		kwargs.pop("""audio"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""sampling_rate"""   ,	A__      )
						A__							:   int					       =		kwargs.pop("""text"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   Optional[int]					       =		args[0]
							A__							:   Dict					       =		args[1:]

						if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process."""      )

						if audio is not None:
							A__							:   List[str]					       =		self.feature_extractor(A__   ,	*A__   ,	sampling_rate=A__   ,	**A__      )
						if text is not None:
							A__							:   Optional[Any]					       =		self.tokenizer(A__   ,	**A__      )

						if text is None:
							return inputs
						elif audio is None:
							return encodings
						else:
							A__							:   List[Any]					       =		encodings["""input_ids"""]
							return inputs






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.batch_decode(*A__   ,	**A__      )






					def   __A      (			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor.pad(*A__   ,	**A__      )

						A__							:   Optional[Any]					       =		kwargs.pop("""input_features"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""labels"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   List[Any]					       =		args[0]
							A__							:   Optional[int]					       =		args[1:]

						if input_features is not None:
							A__							:   Union[str, Any]					       =		self.feature_extractor.pad(A__   ,	*A__   ,	**A__      )
						if labels is not None:
							A__							:   List[Any]					       =		self.tokenizer.pad(A__   ,	**A__      )

						if labels is None:
							return input_features
						elif input_features is None:
							return labels
						else:
							A__							:   Dict					       =		labels["""input_ids"""]
							return input_features






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.decode(*A__   ,	**A__      )






					@contextmanager
					def   __A      (			self      ):
						warnings.warn(
						    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
						    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
						    """your audio inputs, or in a separate call."""      )
						A__							:   int					       =		True
						A__							:   List[Any]					       =		self.tokenizer
						yield
						A__							:   Tuple					       =		self.feature_extractor
						A__							:   Dict					       =		False



