



import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


UpperCAmelCase__			:   Tuple      			=    collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)


def 	_A	(   _UpperCamelCase			):
				print(F'''Reading saved model from {model_file}'''			)
				_UpperCAmelCase						:  Dict     					=	torch.load(__A      ,					map_location=lambda _UpperCamelCase      ,					_UpperCamelCase			: default_restore_location(__A      ,					'''cpu'''			)			)
				return CheckpointState(**__A			)





class 							lowerCAmelCase_		:







		def __init__(    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   Path					)       ->		List[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  int     					=	src_file







		def 					a_  (    self  :   str					)       ->		Optional[Any]:


						'''simple docstring'''







						raise NotImplementedError







		@staticmethod
		def 					a_  (    UpperCAmelCase_  :   str       ,		*UpperCAmelCase_  :   Dict       ,		**UpperCAmelCase_  :   str					)       ->		"DPRState":


						'''simple docstring'''







						if comp_type.startswith('''c'''					):
										return DPRContextEncoderState(*lowerCAmelCase__       ,		**lowerCAmelCase__					)
						if comp_type.startswith('''q'''					):
										return DPRQuestionEncoderState(*lowerCAmelCase__       ,		**lowerCAmelCase__					)
						if comp_type.startswith('''r'''					):
										return DPRReaderState(*lowerCAmelCase__       ,		**lowerCAmelCase__					)
						else:
										raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''					)





class 							lowerCAmelCase_		(		_snake_case     ):







		def 					a_  (    self  :   Optional[Any]					)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  List[str]     					=	DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''					)[0]					)					)
						print(F'''Loading DPR biencoder from {self.src_file}'''					)
						_UpperCAmelCase						:  str     					=	load_states_from_checkpoint(self.src_file					)
						_UpperCAmelCase					, _UpperCAmelCase						:  int     					=	model.ctx_encoder, '''ctx_model.'''
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						_UpperCAmelCase						:  Any     					=	{'''bert_model.embeddings.position_ids''': model.ctx_encoder.bert_model.embeddings.position_ids}
						for key, value in saved_state.model_dict.items():
										if key.startswith(lowerCAmelCase__					):
														_UpperCAmelCase						:  Tuple     					=	key[len(lowerCAmelCase__					) :]
														if not key.startswith('''encode_proj.'''					):
																		_UpperCAmelCase						:  int     					=	'''bert_model.''' + key
														_UpperCAmelCase						:  int     					=	value
						encoder.load_state_dict(lowerCAmelCase__					)
						return model





class 							lowerCAmelCase_		(		_snake_case     ):







		def 					a_  (    self  :   Any					)       ->		Any:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[Any]     					=	DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''					)[0]					)					)
						print(F'''Loading DPR biencoder from {self.src_file}'''					)
						_UpperCAmelCase						:  List[Any]     					=	load_states_from_checkpoint(self.src_file					)
						_UpperCAmelCase					, _UpperCAmelCase						:  List[Any]     					=	model.question_encoder, '''question_model.'''
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						_UpperCAmelCase						:  List[str]     					=	{'''bert_model.embeddings.position_ids''': model.question_encoder.bert_model.embeddings.position_ids}
						for key, value in saved_state.model_dict.items():
										if key.startswith(lowerCAmelCase__					):
														_UpperCAmelCase						:  Dict     					=	key[len(lowerCAmelCase__					) :]
														if not key.startswith('''encode_proj.'''					):
																		_UpperCAmelCase						:  Union[str, Any]     					=	'''bert_model.''' + key
														_UpperCAmelCase						:  Optional[Any]     					=	value
						encoder.load_state_dict(lowerCAmelCase__					)
						return model





class 							lowerCAmelCase_		(		_snake_case     ):







		def 					a_  (    self  :   Any					)       ->		Optional[int]:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[Any]     					=	DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''					)[0]					)					)
						print(F'''Loading DPR reader from {self.src_file}'''					)
						_UpperCAmelCase						:  Optional[Any]     					=	load_states_from_checkpoint(self.src_file					)
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						_UpperCAmelCase						:  Tuple     					=	{
						    '''encoder.bert_model.embeddings.position_ids''': model.span_predictor.encoder.bert_model.embeddings.position_ids
						}
						for key, value in saved_state.model_dict.items():
										if key.startswith('''encoder.'''					) and not key.startswith('''encoder.encode_proj'''					):
														_UpperCAmelCase						:  List[Any]     					=	'''encoder.bert_model.''' + key[len('''encoder.'''					) :]
										_UpperCAmelCase						:  Optional[Any]     					=	value
						model.span_predictor.load_state_dict(lowerCAmelCase__					)
						return model


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  List[Any]     					=	Path(__A			)
				dest_dir.mkdir(exist_ok=__A			)

				_UpperCAmelCase						:  Any     					=	DPRState.from_type(__A      ,					src_file=__A			)
				_UpperCAmelCase						:  Union[str, Any]     					=	dpr_state.load_dpr_model()
				model.save_pretrained(__A			)
				model.from_pretrained(__A			)  # sanity check


if __name__ == "__main__":
					UpperCAmelCase__			:   Optional[Any]      			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--type', type=str, help='Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'
					)
					parser.add_argument(
					    '--src',
					    type=str,
					    help=(
					        'Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'
					        ' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'
					        ' \'retriever\' checkpoints.'
					    ),
					)
					parser.add_argument('--dest', type=str, default=None, help='Path to the output PyTorch model directory.')
					UpperCAmelCase__			:   Optional[Any]      			=    parser.parse_args()

					UpperCAmelCase__			:   Tuple      			=    Path(args.src)
					UpperCAmelCase__			:   Optional[Any]      			=    F"""converted-{src_file.name}""" if args.dest is None else args.dest
					UpperCAmelCase__			:   List[str]      			=    Path(dest_dir)
					assert src_file.exists()
					assert (
					    args.type is not None
					), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
					convert(args.type, src_file, dest_dir)



import matplotlib.pyplot as plt
import numpy as np
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase          =	len(__A      )  # Number of training samples
  UpperCAmelCase          =	np.eye(__A      )  # Initialize weights as identity matrix
  for j in range(__A      ):
    UpperCAmelCase          =	point - x_train[j]
    UpperCAmelCase          =	np.exp(diff @ diff.T / (-2.0 * tau**2)      )

  return weights
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A      ):
  UpperCAmelCase          =	weight_matrix(__A ,							__A ,							__A      )
  UpperCAmelCase          =	np.linalg.inv(x_train.T @ weight_mat @ x_train      ) @ (
      x_train.T @ weight_mat @ y_train.T
  )

  return weight
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase          =	np.zeros(len(__A      )      )  # Initialize array of predictions
  for i, item in enumerate(__A      ):
    UpperCAmelCase          =	item @ local_weight(__A ,							__A ,							__A ,							__A      )

  return y_pred
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  import seaborn as sns

  UpperCAmelCase          =	sns.load_dataset(__A      )
  UpperCAmelCase          =	np.array(data[x_name]      )
  UpperCAmelCase          =	np.array(data[y_name]      )

  UpperCAmelCase          =	np.ones(len(__A      )      )

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

  return x_train, x_data, y_data
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A ,							__A ,							__A ,							):
  UpperCAmelCase          =	np.sort(__A ,							axis=0      )
  plt.scatter(__A ,							__A ,							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(__A      )
  plt.ylabel(__A      )
  plt.show()


if __name__ == "__main__":
      import doctest

      doctest.testmod()

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



