
import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM

def    __snake_case       (  __magic_name__	):







				'''simple docstring'''


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

				return orig_key

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				for key in orig_state_dict.copy().keys():
								lowercase						    =			orig_state_dict.pop(__magic_name__	)

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

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

				return orig_state_dict

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			torch.load(__magic_name__		,						map_location="cpu"	)["model_state_dict"]
				lowercase						    =			NystromformerConfig.from_json_file(__magic_name__	)
				lowercase						    =			NystromformerForMaskedLM(__magic_name__	)

				lowercase						    =			convert_checkpoint_helper(__magic_name__		,						__magic_name__	)

				model.load_state_dict(__magic_name__	)
				model.eval()
				model.save_pretrained(__magic_name__	)

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


if __name__ == "__main__":
						_snake_case			:       str      =						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."
						)
						_snake_case			:       List[str]      =						parser.parse_args()
						convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if not inplace:
								lowercase						    =			ma + ma
				else:
								ma += ma

				return ma

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			-1 * len(__magic_name__	)
				lowercase						    =			list(range(len(tensor.shape[:zero_index]	)	)	)
				return tensor.permute(first_inds + [zero_index + i for i in inds]	)

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				return t.reshape(t.shape[:-no_dims] + (-1,)	)

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__ = 1e-4	):







				'''simple docstring'''


				lowercase						    =			mask.expand(*value.shape	)
				return torch.sum(mask * value		,						dim=__magic_name__	) / (eps + torch.sum(__magic_name__		,						dim=__magic_name__	))

def    __snake_case       (  __magic_name__		,						__magic_name__ = 2.3_1_2_5		,						__magic_name__ = 2_1.6_8_7_5		,						__magic_name__ = 64	):







				'''simple docstring'''


				lowercase						    =			torch.linspace(__magic_name__		,						__magic_name__		,						no_bins - 1		,						device=pts.device	)
				lowercase						    =			torch.sqrt(torch.sum((pts.unsqueeze(-2	) - pts.unsqueeze(-3	)) ** 2		,						dim=-1	)	)
				return torch.bucketize(__magic_name__		,						__magic_name__	)

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			dicts[0]
				lowercase						    =			{}
				for k, v in first.items():
								lowercase						    =			[d[k] for d in dicts]
								if isinstance(__magic_name__		,						__magic_name__	):
												lowercase						    =			dict_multimap(__magic_name__		,						__magic_name__	)
								else:
												lowercase						    =			fn(__magic_name__	)

				return new_dict

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			v_bins.view(((1,) * len(x.shape	)) + (len(__magic_name__	),)	)
				lowercase						    =			x[..., None] - reshaped_bins
				lowercase						    =			torch.argmin(torch.abs(__magic_name__	)		,						dim=-1	)
				return nn.functional.one_hot(__magic_name__		,						num_classes=len(__magic_name__	)	).float()

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__ = 0		,						__magic_name__ = 0	):







				'''simple docstring'''


				lowercase						    =			[]
				for i, s in enumerate(data.shape[:no_batch_dims]	):
								lowercase						    =			torch.arange(__magic_name__	)
								lowercase						    =			r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape	) - i - 1)))	)
								ranges.append(__magic_name__	)

				lowercase						    =			[slice(__magic_name__	) for _ in range(len(data.shape	) - no_batch_dims	)]
				lowercase						    =			inds
				ranges.extend(__magic_name__	)
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(__magic_name__	)]


_snake_case			:       List[Any]      =						TypeVar("T")

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			{}
				for k, v in dic.items():
								if isinstance(__magic_name__		,						__magic_name__	):
												lowercase						    =			dict_map(__magic_name__		,						__magic_name__		,						__magic_name__	)
								else:
												lowercase						    =			tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	)

				return new_dict

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if isinstance(__magic_name__		,						__magic_name__	):
								return dict_map(__magic_name__		,						__magic_name__		,						__magic_name__	)
				elif isinstance(__magic_name__		,						__magic_name__	):
								return [tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	) for x in tree]
				elif isinstance(__magic_name__		,						__magic_name__	):
								return tuple(tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	) for x in tree	)
				elif isinstance(__magic_name__		,						__magic_name__	):
								return fn(__magic_name__	)
				else:
								print(type(__magic_name__	)	)
								raise ValueError("Not supported"	)




_snake_case			:       Union[str, Any]      =						partial(tree_map, leaf_type=torch.Tensor)
