

"""simple docstring"""





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_a       =							logging.get_logger(__name__)

_a       =							{'vocab_file': 'vocab.json'}

_a       =							{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_a       =							{'mgp-str': 27}




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Dict   		=	VOCAB_FILES_NAMES
 __UpperCAmelCase	:					Union[str, Any]   		=	PRETRAINED_VOCAB_FILES_MAP
 __UpperCAmelCase	:					Any   		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(			self							:			Any,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Optional[int]="[GO]",					UpperCAmelCase__							:			str="[GO]",					UpperCAmelCase__							:			Dict="[s]",					UpperCAmelCase__							:			Union[str, Any]="[GO]",					**UpperCAmelCase__							:			Tuple	):
    super().__init__(
        unk_token=UpperCAmelCase__,					bos_token=UpperCAmelCase__,					eos_token=UpperCAmelCase__,					pad_token=UpperCAmelCase__,					**UpperCAmelCase__,					)

    with open(UpperCAmelCase__,					encoding="utf-8"	) as vocab_handle:
       __lowercase								=						json.load(UpperCAmelCase__	)
    __lowercase								=						{v: k for k, v in self.vocab.items()}


 @property
 def 					_lowercase (			self							:			List[Any]	):
    return len(self.vocab	)


 def 					_lowercase (			self							:			int	):
    return dict(self.vocab,					**self.added_tokens_encoder	)


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			Tuple	):
    __lowercase								=						[]
    for s in text:
       char_tokens.extend(UpperCAmelCase__	)
    return char_tokens


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Any	):
    return self.vocab.get(UpperCAmelCase__,					self.vocab.get(self.unk_token	)	)


 def 					_lowercase (			self							:			int,					UpperCAmelCase__							:			List[str]	):
    return self.decoder.get(UpperCAmelCase__	)



 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Optional[str] = None	):
    if not os.path.isdir(UpperCAmelCase__	):
       logger.error("Vocabulary path ({}) should be a directory".format(UpperCAmelCase__	)	)
       return
    __lowercase								=						os.path.join(
        UpperCAmelCase__,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

    with open(UpperCAmelCase__,					"w",					encoding="utf-8"	) as f:
       f.write(json.dumps(self.vocab,					indent=2,					sort_keys=UpperCAmelCase__,					ensure_ascii=UpperCAmelCase__	) + "\n"	)

    return (vocab_file,)







"""simple docstring"""





import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class 					_lowerCAmelCase  (	pl.LightningModule   ):







 """simple docstring"""







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



 def 					_lowercase (			self							:			Optional[int]	):
    pass







def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						str:
   '''simple docstring'''


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

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

   # init longformer question answering model
   __lowercase								=						LongformerForQuestionAnswering.from_pretrained(UpperCamelCase_)

   # 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(UpperCamelCase_)

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


if __name__ == "__main__":
   _a       =							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       =							parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )




