


'''simple docstring'''
import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class 			lowerCamelCase_   (	pl.LightningModule      ):






      def __init__(					self				:    List[str]						,						_A				:    Any ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          pass





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Optional[int]:
    UpperCAmelCase__			:		Tuple													=				LongformerModel.from_pretrained(__a						)
    UpperCAmelCase__			:		Union[str, Any]													=				LightningModel(__a						)

    UpperCAmelCase__			:		str													=				torch.load(__a  ,					map_location=torch.device('''cpu'''						)						)
    lightning_model.load_state_dict(ckpt['''state_dict''']						)

    # init longformer question answering model
    UpperCAmelCase__			:		str													=				LongformerForQuestionAnswering.from_pretrained(__a						)

    # 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(__a						)

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


if __name__ == "__main__":
  UpperCamelCase__    					=      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.'''
  )
  UpperCamelCase__    					=      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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Dict	    =    "ChineseCLIPImageProcessor"
							UpperCAmelCase__       :						List[str]	    =    ("BertTokenizer", "BertTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    Optional[Any]         =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       ,							_a       ,							)
																	_a	:    Tuple         =		kwargs.pop('''feature_extractor'''       )

												_a	:    Tuple         =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''       )
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''       )

												super().__init__(_a       ,							_a       )
												_a	:    List[str]         =		self.image_processor


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    int:

												if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

												if text is not None:
																	_a	:    List[str]         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    Union[str, Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Union[str, Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Any         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




							@property
							def 				__lowercase				(			self       )    ->    Dict:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							_a       ,							)
												return self.image_processor_class



