


import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification






def 	a__ (							A__							):
			SCREAMING_SNAKE_CASE_		:    Any 		=				[
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    '_float_tensor',
			    'decoder.output_projection.weight',
			]
			for k in ignore_keys:
						state_dict.pop(A__,			A__							)






def 	a__ (							A__							):
			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				emb.weight.shape
			SCREAMING_SNAKE_CASE_		:    int 		=				nn.Linear(A__,			A__,			bias=A__							)
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				emb.weight.data
			return lin_layer






def 	a__ (							A__,			A__="uclanlp/plbart-base",			A__=False,			A__=False							):
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				torch.load(A__,			map_location='cpu'							)['model']
			remove_ignore_keys_(A__							)
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				state_dict['encoder.embed_tokens.weight'].shape[0]

			SCREAMING_SNAKE_CASE_		:    Any 		=				PLBartConfig.from_pretrained(A__,			vocab_size=A__							)

			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				state_dict['decoder.embed_tokens.weight']
			if not classification:
						SCREAMING_SNAKE_CASE_		:    List[str] 		=				PLBartForConditionalGeneration(A__							)
						model.model.load_state_dict(A__							)
						if finetuned:
									SCREAMING_SNAKE_CASE_		:    List[Any] 		=				make_linear_from_emb(model.model.shared							)

			else:
						SCREAMING_SNAKE_CASE_		:    List[Any] 		=				{}
						for key, value in state_dict.copy().items():
									if key.startswith('classification_heads.sentence_classification_head'							):
												SCREAMING_SNAKE_CASE_		:    List[str] 		=				value
												state_dict.pop(A__							)
						SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				PLBartForSequenceClassification(A__							)
						model.model.load_state_dict(A__							)
						model.classification_head.load_state_dict(A__							)

			return model


if __name__ == "__main__":
				lowerCAmelCase__ :			Dict         =argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument(
				    '--hf_config',
				    default='uclanlp/plbart-base',
				    type=str,
				    help='Which huggingface architecture to use: plbart-base',
				)
				parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
				parser.add_argument(
				    '--classification', action='store_true', help='whether the model is a classification checkpoint'
				)
				lowerCAmelCase__ :			Union[str, Any]         =parser.parse_args()
				lowerCAmelCase__ :			int         =convert_fairseq_plbart_checkpoint_from_disk(
				    args.fairseq_path,
				    hf_config_path=args.hf_config,
				    finetuned=args.finetuned,
				    classification=args.classification,
				)
				model.save_pretrained(args.pytorch_dump_folder_path)









import numpy as np







class   __lowercase						:





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__=5 ,	lowerCAmelCase__=5					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				depth
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				0
										SCREAMING_SNAKE_CASE_		:    int 		=				None
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				None
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				min_leaf_size
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				None






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										if labels.ndim != 1:
													print('Error: Input labels must be one dimensional'					)

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






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										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:
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				np.mean(lowerCAmelCase__					)
													return

										if self.depth == 1:
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				np.mean(lowerCAmelCase__					)
													return

										SCREAMING_SNAKE_CASE_		:    int 		=				0
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				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:
																SCREAMING_SNAKE_CASE_		:    Dict 		=				self.mean_squared_error(x[:i] ,	np.mean(y[:i]					)					)
																SCREAMING_SNAKE_CASE_		:    int 		=				self.mean_squared_error(x[i:] ,	np.mean(y[i:]					)					)
																SCREAMING_SNAKE_CASE_		:    Any 		=				error_left + error_right
																if error < min_error:
																			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				i
																			SCREAMING_SNAKE_CASE_		:    int 		=				error

										if best_split != 0:
													SCREAMING_SNAKE_CASE_		:    int 		=				x[:best_split]
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				y[:best_split]
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				x[best_split:]
													SCREAMING_SNAKE_CASE_		:    str 		=				y[best_split:]

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

										return






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										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   __lowercase						:





							"""simple docstring"""







							@staticmethod
							def 				UpperCamelCase__    (			lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_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__ (							):
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				np.arange(-1.0,			1.0,			0.0_05							)
			SCREAMING_SNAKE_CASE_		:    Dict 		=				np.sin(A__							)

			SCREAMING_SNAKE_CASE_		:    str 		=				DecisionTree(depth=1_0,			min_leaf_size=1_0							)
			tree.train(A__,			A__							)

			SCREAMING_SNAKE_CASE_		:    Dict 		=				(np.random.rand(1_0							) * 2) - 1
			SCREAMING_SNAKE_CASE_		:    Any 		=				np.array([tree.predict(A__							) for x in test_cases]							)
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				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)





