


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig






def 	a__ (							A__,			A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				TaConfig.from_pretrained(__a							)
			SCREAMING_SNAKE_CASE_		:    Dict 		=				FlaxTaForConditionalGeneration(config=__a							)
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				checkpoints.load_tax_checkpoint(__a							)

			SCREAMING_SNAKE_CASE_		:    List[str] 		=				'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

			# Encoder
			for layer_index in range(config.num_layers							):
						SCREAMING_SNAKE_CASE_		:    str 		=				F'''layers_{str(__a							)}'''

						# Self-Attention
						SCREAMING_SNAKE_CASE_		:    Dict 		=				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
						SCREAMING_SNAKE_CASE_		:    int 		=				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
						SCREAMING_SNAKE_CASE_		:    int 		=				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
						SCREAMING_SNAKE_CASE_		:    Tuple 		=				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

						# Layer Normalization
						SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

						if split_mlp_wi:
									SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									SCREAMING_SNAKE_CASE_		:    Tuple 		=				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									SCREAMING_SNAKE_CASE_		:    int 		=				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						SCREAMING_SNAKE_CASE_		:    int 		=				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						SCREAMING_SNAKE_CASE_		:    Dict 		=				tax_attention_key
						SCREAMING_SNAKE_CASE_		:    str 		=				tax_attention_out
						SCREAMING_SNAKE_CASE_		:    Any 		=				tax_attention_query
						SCREAMING_SNAKE_CASE_		:    str 		=				tax_attention_value

						SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tax_attention_layer_norm

						if split_mlp_wi:
									SCREAMING_SNAKE_CASE_		:    int 		=				tax_mlp_wi_a
									SCREAMING_SNAKE_CASE_		:    Dict 		=				tax_mlp_wi_a
						else:
									SCREAMING_SNAKE_CASE_		:    Tuple 		=				tax_mlp_wi

						SCREAMING_SNAKE_CASE_		:    List[str] 		=				tax_mlp_wo
						SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				tax_mlp_layer_norm

			# Only for layer 0:
			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				tax_encoder_rel_embedding

			# Assigning
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_decoder_layers							):
						SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				F'''layers_{str(__a							)}'''

						# Self-Attention
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
						SCREAMING_SNAKE_CASE_		:    str 		=				tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
						SCREAMING_SNAKE_CASE_		:    Dict 		=				tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
						SCREAMING_SNAKE_CASE_		:    int 		=				tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

						# Layer Normalization
						SCREAMING_SNAKE_CASE_		:    Tuple 		=				tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
						    '''scale'''
						]

						# Encoder-Decoder-Attention
						SCREAMING_SNAKE_CASE_		:    List[str] 		=				tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
						    '''kernel'''
						]
						SCREAMING_SNAKE_CASE_		:    List[str] 		=				tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
						    '''kernel'''
						]
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
						    '''kernel'''
						]
						SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
						    '''kernel'''
						]

						# Layer Normalization
						SCREAMING_SNAKE_CASE_		:    Any 		=				tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

						# MLP
						if split_mlp_wi:
									SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									SCREAMING_SNAKE_CASE_		:    Any 		=				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						SCREAMING_SNAKE_CASE_		:    Dict 		=				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				tax_attention_key
						SCREAMING_SNAKE_CASE_		:    Tuple 		=				tax_attention_out
						SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				tax_attention_query
						SCREAMING_SNAKE_CASE_		:    Dict 		=				tax_attention_value

						SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tax_pre_attention_layer_norm

						SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				tax_enc_dec_attention_key
						SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				tax_enc_dec_attention_out
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tax_enc_dec_attention_query
						SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tax_enc_dec_attention_value

						SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tax_cross_layer_norm

						if split_mlp_wi:
									SCREAMING_SNAKE_CASE_		:    Tuple 		=				tax_mlp_wi_a
									SCREAMING_SNAKE_CASE_		:    Tuple 		=				tax_mlp_wi_a
						else:
									SCREAMING_SNAKE_CASE_		:    Any 		=				tax_mlp_wi

						SCREAMING_SNAKE_CASE_		:    int 		=				tax_mlp_wo

						SCREAMING_SNAKE_CASE_		:    Dict 		=				txa_mlp_layer_norm

			# Decoder Normalization
			SCREAMING_SNAKE_CASE_		:    int 		=				tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				txa_decoder_norm

			# Only for layer 0:
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				tax_decoder_rel_embedding

			# Token Embeddings
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				tax_model['''target''']['''token_embedder''']['''embedding''']
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				txa_token_embeddings

			# LM Head (only in v1.1 checkpoints)
			if "logits_dense" in tax_model["target"]["decoder"]:
						SCREAMING_SNAKE_CASE_		:    str 		=				tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

			flax_model.save_pretrained(__a							)
			print('T5X Model was sucessfully converted!'							)


if __name__ == "__main__":
				lowerCAmelCase__ :			Any         =argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
				)
				parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
				parser.add_argument(
				    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
				)
				lowerCAmelCase__ :			Any         =parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_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)





