

"""simple docstring"""





import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate




@dataclass(frozen=lowercase   )
class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	field(default="image-classification"     ,metadata={"include_in_asdict_even_if_is_default": True}   )
 __UpperCAmelCase	:					ClassVar[Features]   		=	Features({"image": Image()}   )
 __UpperCAmelCase	:					ClassVar[Features]   		=	Features({"labels": ClassLabel}   )
 __UpperCAmelCase	:					str   		=	"image"
 __UpperCAmelCase	:					str   		=	"labels"


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Optional[int]	):
    if self.label_column not in features:
       raise ValueError(F"""Column {self.label_column} is not present in features."""	)
    if not isinstance(features[self.label_column],					UpperCAmelCase__	):
       raise ValueError(F"""Column {self.label_column} is not a ClassLabel."""	)
    __lowercase								=						copy.deepcopy(self	)
    __lowercase								=						self.label_schema.copy()
    __lowercase								=						features[self.label_column]
    __lowercase								=						label_schema
    return task_template



 @property
 def 					_lowercase (			self							:			Optional[Any]	):
    return {
        self.image_column: "image",
        self.label_column: "labels",
    }







"""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
   )




