import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM






def  __lowerCamelCase						(		lowerCAmelCase__      ):






       if "model" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('model.'      ,    ''      )
       if "norm1" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('norm1'      ,    'attention.output.LayerNorm'      )
       if "norm2" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('norm2'      ,    'output.LayerNorm'      )
       if "norm" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('norm'      ,    'LayerNorm'      )
       if "transformer" in orig_key:
              lowerCAmelCase__	      =   orig_key.split('.'      )[0].split('_'      )[-1]
              lowerCAmelCase__	      =   orig_key.replace(F"""transformer_{layer_num}"""      ,    F"""encoder.layer.{layer_num}"""      )
       if "mha.attn" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('mha.attn'      ,    'attention.self'      )
       if "mha" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('mha'      ,    'attention'      )
       if "W_q" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('W_q'      ,    'self.query'      )
       if "W_k" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('W_k'      ,    'self.key'      )
       if "W_v" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('W_v'      ,    'self.value'      )
       if "ff1" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('ff1'      ,    'intermediate.dense'      )
       if "ff2" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('ff2'      ,    'output.dense'      )
       if "ff" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('ff'      ,    'output.dense'      )
       if "mlm_class" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('mlm.mlm_class'      ,    'cls.predictions.decoder'      )
       if "mlm" in orig_key:
              lowerCAmelCase__	      =   orig_key.replace('mlm'      ,    'cls.predictions.transform'      )
       if "cls" not in orig_key:
              lowerCAmelCase__	      =   "nystromformer." + orig_key

       return orig_key






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):






       for key in orig_state_dict.copy().keys():
              lowerCAmelCase__	      =   orig_state_dict.pop(_SCREAMING_SNAKE_CASE      )

              if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
                     continue
              else:
                     lowerCAmelCase__	      =   val

       lowerCAmelCase__	      =   orig_state_dict["cls.predictions.decoder.bias"]
       lowerCAmelCase__	      =   (
           torch.arange(config.max_position_embeddings      ).expand((1, -1)      ) + 2
       )

       return orig_state_dict






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   torch.load(_SCREAMING_SNAKE_CASE      ,    map_location='cpu'      )["model_state_dict"]
       lowerCAmelCase__	      =   NystromformerConfig.from_json_file(_SCREAMING_SNAKE_CASE      )
       lowerCAmelCase__	      =   NystromformerForMaskedLM(_SCREAMING_SNAKE_CASE      )

       lowerCAmelCase__	      =   convert_checkpoint_helper(_SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      )

       model.load_state_dict(_SCREAMING_SNAKE_CASE      )
       model.eval()
       model.save_pretrained(_SCREAMING_SNAKE_CASE      )

       print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""      )


if __name__ == "__main__":
    lowerCAmelCase__      		=					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to Nystromformer pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for Nystromformer model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    lowerCAmelCase__      		=					parser.parse_args()
    convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


import torch

from accelerate import Accelerator, DistributedType
class    _UpperCamelCase:

						def __enter__(					self		:			str			):
													if self.enabled:
																				_UpperCAmelCase  :			int	       =		self.model.no_sync()
																				self.model_sync_obj.__enter__()

													return self

						def __exit__(					self		:			str	,						_lowerCamelCase		:			Union[str, Any]	,						_lowerCamelCase		:			Tuple	,						_lowerCamelCase		:			Optional[Any]			):
													if self.enabled:
																				# Average all models on exit
																				self._sync_and_avg_model_params()
																				self.model_sync_obj.__exit__(_lowerCamelCase	,						_lowerCamelCase	,						_lowerCamelCase			)

						def __init__(					self		:			str	,						_lowerCamelCase		:			Accelerator	,						_lowerCamelCase		:			torch.nn.Module	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			bool = True			):
													if accelerator.distributed_type not in [
													    DistributedType.NO,
													    DistributedType.MULTI_CPU,
													    DistributedType.MULTI_GPU,
													]:
																				raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"			)
													_UpperCAmelCase  :			Any	       =		enabled and accelerator.distributed_type != DistributedType.NO
													_UpperCAmelCase  :			str	       =		0
													if self.enabled:
																				_UpperCAmelCase  :			Tuple	       =		accelerator
																				_UpperCAmelCase  :			List[str]	       =		model
																				_UpperCAmelCase  :			Dict	       =		local_sgd_steps

						def 						a__ (					self		:			Optional[int]			):
													self.num_steps += 1
													if not self.enabled:
																				return

													if self.num_steps % self.local_sgd_steps == 0:
																				self._sync_and_avg_model_params()






						def 						a__ (					self		:			Tuple			):
													self.accelerator.wait_for_everyone()
													with self.accelerator.autocast():
																				for param in self.model.parameters():
																											_UpperCAmelCase  :			Optional[Any]	       =		self.accelerator.reduce(param.data	,						reduction="mean"			)




