
'''simple docstring'''





import numpy as np

class     a :




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					int=5,     SCREAMING_SNAKE_CASE_						:					Dict=5   ):
										snake_case							:  Optional[Any]			=     depth
										snake_case							:  int			=     0
										snake_case							:  List[str]			=     None
										snake_case							:  Tuple			=     None
										snake_case							:  List[Any]			=     min_leaf_size
										snake_case							:  List[Any]			=     None




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										if labels.ndim != 1:
																print('''Error: Input labels must be one dimensional'''   )

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




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										if x.ndim != 1:
																print('''Error: Input data set must be one dimensional'''   )
																return
										if len(SCREAMING_SNAKE_CASE_   ) != len(SCREAMING_SNAKE_CASE_   ):
																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(SCREAMING_SNAKE_CASE_   ) < 2 * self.min_leaf_size:
																snake_case							:  Tuple			=     np.mean(SCREAMING_SNAKE_CASE_   )
																return

										if self.depth == 1:
																snake_case							:  Dict			=     np.mean(SCREAMING_SNAKE_CASE_   )
																return

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

										if best_split != 0:
																snake_case							:  Union[str, Any]			=     x[:best_split]
																snake_case							:  Union[str, Any]			=     y[:best_split]
																snake_case							:  Any			=     x[best_split:]
																snake_case							:  List[str]			=     y[best_split:]

																snake_case							:  List[str]			=     x[best_split]
																snake_case							:  Optional[int]			=     DecisionTree(
																    depth=self.depth - 1,     min_leaf_size=self.min_leaf_size   )
																snake_case							:  str			=     DecisionTree(
																    depth=self.depth - 1,     min_leaf_size=self.min_leaf_size   )
																self.left.train(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
																self.right.train(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										else:
																snake_case							:  Union[str, Any]			=     np.mean(SCREAMING_SNAKE_CASE_   )

										return




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										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(SCREAMING_SNAKE_CASE_   )
																else:
																						return self.left.predict(SCREAMING_SNAKE_CASE_   )
										else:
																print('''Error: Decision tree not yet trained'''   )
																return None




class     a :




				@staticmethod
				def 						__snake_case				(				SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int   ):
										snake_case							:  Optional[Any]			=     float(0   )
										for label in labels:
																squared_error_sum += (label - prediction) ** 2

										return float(squared_error_sum / labels.size   )




def   A			(							):
						snake_case							:  Union[str, Any]			=     np.arange(-1.0     ,				1.0     ,				0.005							)
						snake_case							:  Optional[Any]			=     np.sin(A_							)

						snake_case							:  List[Any]			=     DecisionTree(depth=10     ,				min_leaf_size=10							)
						tree.train(A_     ,				A_							)

						snake_case							:  Dict			=     (np.random.rand(10							) * 2) - 1
						snake_case							:  Optional[Any]			=     np.array([tree.predict(A_							) for x in test_cases]							)
						snake_case							:  Union[str, Any]			=     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)






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       RoFormerConfig.from_json_file(_UpperCAmelCase				)
					print(f"""Building PyTorch model from configuration: {config}"""				)
					lowercase						   =       RoFormerForMaskedLM(_UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tf_weights_in_roformer(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)

					# Save pytorch-model
					print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
					torch.save(model.state_dict()       ,			_UpperCAmelCase       ,			_use_new_zipfile_serialization=_UpperCAmelCase				)


if __name__ == "__main__":
		__magic_name__       =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__magic_name__       =     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






