


'''simple docstring'''
import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







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

							return orig_key







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							for key in orig_state_dict.copy().keys():
														A_ 						=		orig_state_dict.pop(UpperCamelCase_   )

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

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

							return orig_state_dict







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		torch.load(UpperCamelCase_	,     map_location="cpu"   )["model_state_dict"]
							A_ 						=		NystromformerConfig.from_json_file(UpperCamelCase_   )
							A_ 						=		NystromformerForMaskedLM(UpperCamelCase_   )

							A_ 						=		convert_checkpoint_helper(UpperCamelCase_	,     UpperCamelCase_   )

							model.load_state_dict(UpperCamelCase_   )
							model.eval()
							model.save_pretrained(UpperCamelCase_   )

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


if __name__ == "__main__":
							__SCREAMING_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.'''
							)
							__SCREAMING_SNAKE_CASE				:							Optional[Any]		       =		parser.parse_args()
							convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''
def 			a_							(  UpperCamelCase_	,     UpperCamelCase_ = 1_0_0_0   ):
							if (not isinstance(UpperCamelCase_	,     UpperCamelCase_   )) or (digit_position <= 0):
														raise ValueError("Digit position must be a positive integer"   )
							elif (not isinstance(UpperCamelCase_	,     UpperCamelCase_   )) or (precision < 0):
														raise ValueError("Precision must be a nonnegative integer"   )

							# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
							# accurate
							A_ 						=		(
							    4 * _subsum(UpperCamelCase_	,     1	,     UpperCamelCase_   )
							    - 2 * _subsum(UpperCamelCase_	,     4	,     UpperCamelCase_   )
							    - _subsum(UpperCamelCase_	,     5	,     UpperCamelCase_   )
							    - _subsum(UpperCamelCase_	,     6	,     UpperCamelCase_   )
							)

							# return the first hex digit of the fractional part of the result
							return hex(int((sum_result % 1) * 1_6   )   )[2:]







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		0.0
							for sum_index in range(digit_pos_to_extract + precision   ):
														A_ 						=		8 * sum_index + denominator_addend
														if sum_index < digit_pos_to_extract:
																					# if the exponential term is an integer and we mod it by the denominator
																					# before dividing, only the integer part of the sum will change;
																					# the fractional part will not
																					A_ 						=		pow(
																					    1_6	,     digit_pos_to_extract - 1 - sum_index	,     UpperCamelCase_   )
														else:
																					A_ 						=		pow(1_6	,     digit_pos_to_extract - 1 - sum_index   )
														total += exponential_term / denominator
							return total


if __name__ == "__main__":
							import doctest

							doctest.testmod()





