




import matplotlib.pyplot as plt
import numpy as np


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

				'''simple docstring'''


				_UpperCAmelCase													=					len(snake_case__						)  # Number of training samples
				_UpperCAmelCase													=					np.eye(snake_case__						)  # Initialize weights as identity matrix
				for j in range(snake_case__						):
								_UpperCAmelCase													=					point - x_train[j]
								_UpperCAmelCase													=					np.exp(diff @ diff.T / (-2.0 * tau**2)						)

				return weights


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_						):

				'''simple docstring'''


				_UpperCAmelCase													=					weight_matrix(snake_case__     ,   snake_case__     ,   snake_case__						)
				_UpperCAmelCase													=					np.linalg.inv(x_train.T @ weight_mat @ x_train						) @ (
				    x_train.T @ weight_mat @ y_train.T
				)

				return weight


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

				'''simple docstring'''


				_UpperCAmelCase													=					np.zeros(len(snake_case__						)						)  # Initialize array of predictions
				for i, item in enumerate(snake_case__						):
								_UpperCAmelCase													=					item @ local_weight(snake_case__     ,   snake_case__     ,   snake_case__     ,   snake_case__						)

				return y_pred


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

				'''simple docstring'''


				import seaborn as sns

				_UpperCAmelCase													=					sns.load_dataset(snake_case__						)
				_UpperCAmelCase													=					np.array(data[x_name]						)
				_UpperCAmelCase													=					np.array(data[y_name]						)

				_UpperCAmelCase													=					np.ones(len(snake_case__						)						)

				# pairing elements of one and x_data
				_UpperCAmelCase													=					np.column_stack((one, x_data)						)

				return x_train, x_data, y_data


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_     ,   ):

				'''simple docstring'''


				_UpperCAmelCase													=					np.sort(snake_case__     ,   axis=0						)
				plt.scatter(snake_case__     ,   snake_case__     ,   color="blue"						)
				plt.plot(
				    x_train_sorted[:, 1]     ,   preds[x_train[:, 1].argsort(0						)]     ,   color="yellow"     ,   linewidth=5     ,   )
				plt.title("Local Weighted Regression"						)
				plt.xlabel(snake_case__						)
				plt.ylabel(snake_case__						)
				plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Demo with a dataset from the seaborn module
				lowercase_    ,					lowercase_    ,					lowercase_     :    List[Any]      =			load_data('tips', 'total_bill', 'tip')
				lowercase_     :    List[str]      =			local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')










import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


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

						return orig_key


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												_UpperCAmelCase													=					orig_state_dict.pop(snake_case_						)

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

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

						return orig_state_dict


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

						'''simple docstring'''


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

						_UpperCAmelCase													=					convert_checkpoint_helper(snake_case_     ,   snake_case_						)

						model.load_state_dict(snake_case_						)
						model.eval()
						model.save_pretrained(snake_case_						)

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


if __name__ == "__main__":
				lowercase_     :    List[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.'
				)
				lowercase_     :    int      =			parser.parse_args()
				convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




