



'''simple docstring'''






import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Dict ,       snake_case_    :     Any ,       snake_case_    :     Optional[int]  )		->Union[str, Any]:






							'''simple docstring'''
							_lowercase							:		List[Any]				=   TaConfig.from_pretrained(snake_case_  )
							_lowercase							:		Optional[Any]				=   FlaxTaForConditionalGeneration(config=snake_case_  )
							_lowercase							:		List[str]				=   checkpoints.load_tax_checkpoint(snake_case_  )

							_lowercase							:		int				=   '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

							# Encoder
							for layer_index in range(config.num_layers  ):
														_lowercase							:		int				=   F"layers_{str(snake_case_  )}"

														# Self-Attention
														_lowercase							:		Union[str, Any]				=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
														_lowercase							:		Optional[Any]				=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
														_lowercase							:		Any				=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
														_lowercase							:		Tuple				=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

														# Layer Normalization
														_lowercase							:		List[str]				=   tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

														if split_mlp_wi:
																					_lowercase							:		Dict				=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																					_lowercase							:		str				=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
														else:
																					_lowercase							:		Dict				=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

														_lowercase							:		str				=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

														# Layer Normalization
														_lowercase							:		str				=   tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

														# Assigning
														_lowercase							:		int				=   tax_attention_key
														_lowercase							:		str				=   tax_attention_out
														_lowercase							:		Any				=   tax_attention_query
														_lowercase							:		Tuple				=   tax_attention_value

														_lowercase							:		Tuple				=   tax_attention_layer_norm

														if split_mlp_wi:
																					_lowercase							:		Optional[int]				=   tax_mlp_wi_a
																					_lowercase							:		List[str]				=   tax_mlp_wi_a
														else:
																					_lowercase							:		List[str]				=   tax_mlp_wi

														_lowercase							:		Dict				=   tax_mlp_wo
														_lowercase							:		int				=   tax_mlp_layer_norm

							# Only for layer 0:
							_lowercase							:		Any				=   tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
							_lowercase							:		List[str]				=   tax_encoder_rel_embedding

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

							# Decoder
							for layer_index in range(config.num_decoder_layers  ):
														_lowercase							:		int				=   F"layers_{str(snake_case_  )}"

														# Self-Attention
														_lowercase							:		int				=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
														_lowercase							:		int				=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
														_lowercase							:		List[str]				=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
														_lowercase							:		int				=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

														# Layer Normalization
														_lowercase							:		Optional[int]				=   tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
														    '''scale'''
														]

														# Encoder-Decoder-Attention
														_lowercase							:		str				=   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
														    '''kernel'''
														]
														_lowercase							:		int				=   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
														    '''kernel'''
														]
														_lowercase							:		Dict				=   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
														    '''kernel'''
														]
														_lowercase							:		Any				=   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
														    '''kernel'''
														]

														# Layer Normalization
														_lowercase							:		Tuple				=   tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

														# MLP
														if split_mlp_wi:
																					_lowercase							:		int				=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																					_lowercase							:		List[str]				=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
														else:
																					_lowercase							:		Tuple				=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

														_lowercase							:		List[Any]				=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

														# Layer Normalization
														_lowercase							:		List[str]				=   tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

														# Assigning
														_lowercase							:		Union[str, Any]				=   tax_attention_key
														_lowercase							:		List[Any]				=   tax_attention_out
														_lowercase							:		Any				=   tax_attention_query
														_lowercase							:		Union[str, Any]				=   tax_attention_value

														_lowercase							:		str				=   tax_pre_attention_layer_norm

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

														_lowercase							:		str				=   tax_cross_layer_norm

														if split_mlp_wi:
																					_lowercase							:		List[Any]				=   tax_mlp_wi_a
																					_lowercase							:		str				=   tax_mlp_wi_a
														else:
																					_lowercase							:		str				=   tax_mlp_wi

														_lowercase							:		Optional[int]				=   tax_mlp_wo

														_lowercase							:		str				=   txa_mlp_layer_norm

							# Decoder Normalization
							_lowercase							:		Optional[Any]				=   tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
							_lowercase							:		List[str]				=   txa_decoder_norm

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

							# Token Embeddings
							_lowercase							:		str				=   tax_model['''target''']['''token_embedder''']['''embedding''']
							_lowercase							:		int				=   txa_token_embeddings

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

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


if __name__ == "__main__":
						lowerCamelCase__	  =		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__	  =		parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








'''simple docstring'''






import math as m





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Union[str, Any] ,       snake_case_    :     int ,       snake_case_    :     Optional[Any]=0.0_0_1  )		->Tuple:






							'''simple docstring'''
							return (f(a + h  ) - f(a - h  )) / (2 * h)





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Dict ,       snake_case_    :     List[Any]=0 ,       snake_case_    :     Dict=1_00 ,       snake_case_    :     List[str]=0.0_0_0_1 ,       snake_case_    :     str=1E-6 ,       snake_case_    :     Tuple=False  )		->List[Any]:






							'''simple docstring'''
							_lowercase							:		Tuple				=   xa  # set the initial guess
							_lowercase							:		List[str]				=   [a]
							_lowercase							:		str				=   abs(f(snake_case_  )  )
							_lowercase							:		Dict				=   lambda snake_case_  : calc_derivative(snake_case_ ,       snake_case_ ,       h=snake_case_  )  # noqa: E731  Derivative of f(x)
							for _ in range(snake_case_  ):
														if fa(snake_case_  ) == 0:
																					raise ValueError('''No converging solution found'''  )
														_lowercase							:		Tuple				=   a - f(snake_case_  ) / fa(snake_case_  )  # Calculate the next estimate
														if logsteps:
																					steps.append(snake_case_  )
														if error < maxerror:
																					break
							else:
														raise ValueError('''Iteration limit reached, no converging solution found'''  )
							if logsteps:
														# If logstep is true, then log intermediate steps
														return a, error, steps
							return a, error


if __name__ == "__main__":
						from matplotlib import pyplot as plt

						lowerCamelCase__	  =		lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
						lowerCamelCase__  ,      lowerCamelCase__  ,      lowerCamelCase__	  =		newton_raphson(
						    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
						)
						plt.plot([abs(f(x)) for x in steps])
						plt.xlabel('step')
						plt.ylabel('error')
						plt.show()
						print(f'''solution = {{{solution:f}}}, error = {{{error:f}}}''')



