



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







def       lowerCamelCase_  (    _a       :						str    ):



    '''simple docstring'''




    print(F'''Reading saved model from {model_file}'''    )
    UpperCAmelCase_     :       str  		=					torch.load(__a   ,		map_location=lambda _a   ,		_a    : default_restore_location(__a   ,		"""cpu"""    )    )
    return CheckpointState(**__a    )







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     str  ,lowerCamelCase_:     List[Any]							)	->      Optional[int]:
       UpperCAmelCase_     :       List[str]  		=					src_file


   def       A__    (    self:     Optional[int]							)	->      Tuple:
       raise NotImplementedError






   @staticmethod
   def       A__    (    lowerCamelCase_:     str  ,*lowerCamelCase_:     Any  ,**lowerCamelCase_:     Optional[int]							)	->      int:
       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       _snake_case				(     lowerCamelCase_ ):






   '''simple docstring'''






   def       A__    (    self:     str							)	->      Any:
       UpperCAmelCase_     :       Optional[Any]  		=					DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased"""							)[0]							)							)
       print(F'''Loading DPR biencoder from {self.src_file}'''							)
       UpperCAmelCase_     :       Dict  		=					load_states_from_checkpoint(self.src_file							)
       UpperCAmelCase_		, UpperCAmelCase_     :       List[Any]  		=					model.ctx_encoder, """ctx_model."""
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCAmelCase_     :       Optional[int]  		=					{"""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_     :       str  		=					key[len(lowerCAmelCase_							) :]
               if not key.startswith("""encode_proj."""							):
                   UpperCAmelCase_     :       Optional[Any]  		=					"""bert_model.""" + key
               UpperCAmelCase_     :       Optional[Any]  		=					value
       encoder.load_state_dict(lowerCAmelCase_							)
       return model







class       _snake_case				(     lowerCamelCase_ ):






   '''simple docstring'''






   def       A__    (    self:     Tuple							)	->      Optional[int]:
       UpperCAmelCase_     :       List[str]  		=					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_     :       Any  		=					model.question_encoder, """question_model."""
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCAmelCase_     :       Optional[int]  		=					{"""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_     :       Optional[Any]  		=					key[len(lowerCAmelCase_							) :]
               if not key.startswith("""encode_proj."""							):
                   UpperCAmelCase_     :       Optional[int]  		=					"""bert_model.""" + key
               UpperCAmelCase_     :       List[Any]  		=					value
       encoder.load_state_dict(lowerCAmelCase_							)
       return model







class       _snake_case				(     lowerCamelCase_ ):






   '''simple docstring'''






   def       A__    (    self:     str							)	->      List[Any]:
       UpperCAmelCase_     :       Optional[Any]  		=					DPRReader(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased"""							)[0]							)							)
       print(F'''Loading DPR reader from {self.src_file}'''							)
       UpperCAmelCase_     :       Dict  		=					load_states_from_checkpoint(self.src_file							)
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCAmelCase_     :       Dict  		=					{
           """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[str]  		=					"""encoder.bert_model.""" + key[len("""encoder."""							) :]
           UpperCAmelCase_     :       int  		=					value
       model.span_predictor.load_state_dict(lowerCAmelCase_							)
       return model







def       lowerCamelCase_  (    _a       :						str   ,		_a       :						Path   ,		_a       :						Path    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Tuple  		=					Path(__a    )
    dest_dir.mkdir(exist_ok=__a    )

    UpperCAmelCase_     :       int  		=					DPRState.from_type(__a   ,		src_file=__a    )
    UpperCAmelCase_     :       Tuple  		=					dpr_state.load_dpr_model()
    model.save_pretrained(__a    )
    model.from_pretrained(__a    )  # sanity check


if __name__ == "__main__":
      UpperCamelCase_									=   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_									=   parser.parse_args()

      UpperCamelCase_									=   Path(args.src)
      UpperCamelCase_									=   F"converted-{src_file.name}" if args.dest is None else args.dest
      UpperCamelCase_									=   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 numpy as np
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_=5       ,  lowerCAmelCase_=5       ):
		_lowercase   =depth
		_lowercase   =0
		_lowercase   =None
		_lowercase   =None
		_lowercase   =min_leaf_size
		_lowercase   =None


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if labels.ndim != 1:
			print("Error: Input labels must be one dimensional"       )

		return np.mean((labels - prediction) ** 2       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if x.ndim != 1:
			print("Error: Input data set must be one dimensional"       )
			return
		if len(lowerCAmelCase_       ) != len(lowerCAmelCase_       ):
			print("Error: X and y have different lengths"       )
			return
		if y.ndim != 1:
			print("Error: Data set labels must be one dimensional"       )
			return

		if len(lowerCAmelCase_       ) < 2 * self.min_leaf_size:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		if self.depth == 1:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		_lowercase   =0
		_lowercase   =self.mean_squared_error(lowerCAmelCase_       ,  np.mean(lowerCAmelCase_       )       ) * 2
		for i in range(len(lowerCAmelCase_       )       ):
			if len(x[:i]       ) < self.min_leaf_size:
				continue
			elif len(x[i:]       ) < self.min_leaf_size:
				continue
			else:
				_lowercase   =self.mean_squared_error(x[:i]       ,  np.mean(y[:i]       )       )
				_lowercase   =self.mean_squared_error(x[i:]       ,  np.mean(y[i:]       )       )
				_lowercase   =error_left + error_right
				if error < min_error:
					_lowercase   =i
					_lowercase   =error

		if best_split != 0:
			_lowercase   =x[:best_split]
			_lowercase   =y[:best_split]
			_lowercase   =x[best_split:]
			_lowercase   =y[best_split:]

			_lowercase   =x[best_split]
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			self.left.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
			self.right.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
		else:
			_lowercase   =np.mean(lowerCAmelCase_       )

		return


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		if self.prediction is not None:
			return self.prediction
		elif self.left or self.right is not None:
			if x >= self.decision_boundary:
				return self.right.predict(lowerCAmelCase_       )
			else:
				return self.left.predict(lowerCAmelCase_       )
		else:
			print("Error: Decision tree not yet trained"       )
			return None

class 	_a  :



	"""simple docstring"""



	@staticmethod
	def __lowerCAmelCase      (   lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =float(0       )
		for label in labels:
			squared_error_sum += (label - prediction) ** 2

		return float(squared_error_sum / labels.size       )

def 	__lowerCamelCase							(				)      ->       Union[str, Any]:
	_lowercase   =np.arange(-1.0      ,							1.0      ,							0.005		)
	_lowercase   =np.sin(__a		)

	_lowercase   =DecisionTree(depth=10      ,							min_leaf_size=10		)
	tree.train(__a      ,							__a		)

	_lowercase   =(np.random.rand(10		) * 2) - 1
	_lowercase   =np.array([tree.predict(__a		) for x in test_cases]		)
	_lowercase   =np.mean((predictions - test_cases) ** 2		)

	print("Test values: " + str(__a		)		)
	print("Predictions: " + str(__a		)		)
	print("Average error: " + str(__a		)		)


if __name__ == "__main__":
						main()
						import doctest

						doctest.testmod(name="mean_squarred_error", verbose=True)




