




import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available




def 		lowerCAmelCase_ (						*lowerCamelCase ):
				if not isinstance(lowerCamelCase						,  lowerCamelCase ):
								__magic_name__						:						Union[str, Any]										=list(lowerCamelCase )
				for i in range(len(lowerCamelCase ) ):
								__magic_name__						:						Dict										=None
				gc.collect()
				if is_xpu_available():
								torch.xpu.empty_cache()
				elif is_npu_available():
								torch.npu.empty_cache()
				else:
								torch.cuda.empty_cache()
				return objects




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						List[str]										=[
				    """CUDA out of memory.""",  # CUDA OOM
				    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
				    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
				]
				if isinstance(lowerCamelCase						,  lowerCamelCase ) and len(exception.args ) == 1:
								return any(err in exception.args[0] for err in _statements )
				return False




def 		lowerCAmelCase_ (						lowerCamelCase = None						,  lowerCamelCase = 128 ):
				if function is None:
								return functools.partial(lowerCamelCase						,  starting_batch_size=lowerCamelCase )

				__magic_name__						:						List[Any]										=starting_batch_size

				def decorator(*lowerCamelCase						,  **lowerCamelCase ):
								nonlocal batch_size
								gc.collect()
								if is_xpu_available():
												torch.xpu.empty_cache()
								elif is_npu_available():
												torch.npu.empty_cache()
								else:
												torch.cuda.empty_cache()
								__magic_name__						:						Optional[Any]										=list(inspect.signature(lowerCamelCase ).parameters.keys() )
								# Guard against user error
								if len(lowerCamelCase ) < (len(lowerCamelCase ) + 1):
												__magic_name__						:						Optional[int]										=""", """.join([F"{arg}={value}" for arg, value in zip(params[1:]						,  args[1:] )] )
												raise TypeError(
												    F"Batch size was passed into `{function.__name__}` as the first argument when called."
												    F"Remove this as the decorator already does so: `{function.__name__}({arg_str})`" )
								while True:
												if batch_size == 0:
																raise RuntimeError("""No executable batch size found, reached zero.""" )
												try:
																return function(lowerCamelCase						,  *lowerCamelCase						,  **lowerCamelCase )
												except Exception as e:
																if should_reduce_batch_size(lowerCamelCase ):
																				gc.collect()
																				if is_xpu_available():
																								torch.xpu.empty_cache()
																				elif is_npu_available():
																								torch.npu.empty_cache()
																				else:
																								torch.cuda.empty_cache()
																				batch_size //= 2
																else:
																				raise

				return decorator











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       ,      snake_case   )					->	Dict:
      """simple docstring"""





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





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      pass







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load longformer model from model identifier
   a__						:    List[str]   		=	LongformerModel.from_pretrained(lowerCamelCase )
   a__						:    Dict   		=	LightningModel(lowerCamelCase )

   a__						:    str   		=	torch.load(lowerCamelCase				,			map_location=torch.device("cpu" ) )
   lightning_model.load_state_dict(ckpt["state_dict"] )

   # init longformer question answering model
   a__						:    Dict   		=	LongformerForQuestionAnswering.from_pretrained(lowerCamelCase )

   # 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(lowerCamelCase )

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      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."""
  )
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

