





'''simple docstring'''
import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class      snake_case     (    pl.LightningModule       ):




 """simple docstring"""



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






 def        _lowerCamelCase					(       self       :							Tuple	):
        pass






def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  str				,  __lowercase						:  str     ) ->      List[Any]:



       """simple docstring"""






       __UpperCamelCase       =  LongformerModel.from_pretrained(__lowercase     )
       __UpperCamelCase       =  LightningModel(__lowercase     )

       __UpperCamelCase       =  torch.load(__lowercase				,  map_location=torch.device('cpu'     )     )
       lightning_model.load_state_dict(ckpt['state_dict']     )

       # init longformer question answering model
       __UpperCamelCase       =  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__							:							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.'''
    )
    a__							:							Union[str, Any] =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 typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=["image_processor", "tokenizer"]
 SCREAMING_SNAKE_CASE_	:					List[Any]					="BlipImageProcessor"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=("BertTokenizer", "BertTokenizerFast")






 def __init__(       self       :							Dict							,	__A       :							Optional[int]							,	__A       :							List[Any]	):
        __UpperCamelCase       =  False
        super().__init__(__A							,	__A	)
        __UpperCamelCase       =  self.image_processor






 def __call__(       self       :							List[Any]							,	__A       :							ImageInput = None							,	__A       :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	__A       :							bool = True							,	__A       :							Union[bool, str, PaddingStrategy] = False							,	__A       :							Union[bool, str, TruncationStrategy] = None							,	__A       :							Optional[int] = None							,	__A       :							int = 0							,	__A       :							Optional[int] = None							,	__A       :							Optional[bool] = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = True							,	__A       :							Optional[Union[str, TensorType]] = None							,	**__A       :							List[Any]							,	):
        if images is None and text is None:
               raise ValueError('You have to specify either images or text.'	)

        # Get only text
        if images is None:
               __UpperCamelCase       =  self.tokenizer
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
               return text_encoding

        # add pixel_values
        __UpperCamelCase       =  self.image_processor(__A							,	return_tensors=__A	)

        if text is not None:
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
        else:
               __UpperCamelCase       =  None

        if text_encoding is not None:
               encoding_image_processor.update(__A	)

        return encoding_image_processor






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							Dict							,	**__A       :							Optional[int]	):
        return self.tokenizer.batch_decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							List[str]							,	**__A       :							Dict	):
        return self.tokenizer.decode(*__A							,	**__A	)






 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.tokenizer.model_input_names
        __UpperCamelCase       =  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
