


import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM






def 						_A						(						_lowercase				)			->				List[Any]:


     """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 						_A						(						_lowercase			,       _lowercase				)			->				List[str]:


     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          __UpperCamelCase				=	orig_state_dict.pop(_lowercase				)

          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 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				int:


     """simple docstring"""


     __UpperCamelCase				=	torch.load(_lowercase			,       map_location='cpu'				)['model_state_dict']
     __UpperCamelCase				=	NystromformerConfig.from_json_file(_lowercase				)
     __UpperCamelCase				=	NystromformerForMaskedLM(_lowercase				)

     __UpperCamelCase				=	convert_checkpoint_helper(_lowercase			,       _lowercase				)

     model.load_state_dict(_lowercase				)
     model.eval()
     model.save_pretrained(_lowercase				)

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


if __name__ == "__main__":
    __snake_case     =		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     =		parser.parse_args()
    convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


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

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


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

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

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

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	np.sort(_lowercase			,       axis=0				)
     plt.scatter(_lowercase			,       _lowercase			,       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(_lowercase				)
     plt.ylabel(_lowercase				)
     plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


