




"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def 				a (						__snake_case     :				Tuple,    __snake_case     :				Dict,    __snake_case     :				Union[str, Any]  ):



				'''simple docstring'''





				UpperCAmelCase_					:Any      			=      TaConfig.from_pretrained(__snake_case  )
				UpperCAmelCase_					:List[Any]      			=      FlaxTaForConditionalGeneration(config=__snake_case  )
				UpperCAmelCase_					:int      			=      checkpoints.load_tax_checkpoint(__snake_case  )

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

				# Encoder
				for layer_index in range(config.num_layers  ):
								UpperCAmelCase_					:List[str]      			=      f'layers_{str(__snake_case  )}'

								# Self-Attention
								UpperCAmelCase_					:str      			=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
								UpperCAmelCase_					:List[str]      			=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
								UpperCAmelCase_					:Dict      			=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
								UpperCAmelCase_					:Optional[Any]      			=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

								# Layer Normalization
								UpperCAmelCase_					:Dict      			=      tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

								if split_mlp_wi:
												UpperCAmelCase_					:List[str]      			=      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												UpperCAmelCase_					:str      			=      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												UpperCAmelCase_					:Optional[Any]      			=      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								UpperCAmelCase_					:Any      			=      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

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

								# Assigning
								UpperCAmelCase_					:List[Any]      			=      tax_attention_key
								UpperCAmelCase_					:Optional[int]      			=      tax_attention_out
								UpperCAmelCase_					:str      			=      tax_attention_query
								UpperCAmelCase_					:Optional[int]      			=      tax_attention_value

								UpperCAmelCase_					:Tuple      			=      tax_attention_layer_norm

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

								UpperCAmelCase_					:Union[str, Any]      			=      tax_mlp_wo
								UpperCAmelCase_					:Any      			=      tax_mlp_layer_norm

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

				# Assigning
				UpperCAmelCase_					:Optional[int]      			=      tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
				UpperCAmelCase_					:Union[str, Any]      			=      tax_encoder_norm

				# Decoder
				for layer_index in range(config.num_decoder_layers  ):
								UpperCAmelCase_					:Optional[int]      			=      f'layers_{str(__snake_case  )}'

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

								# Layer Normalization
								UpperCAmelCase_					:Any      			=      tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
								    '''scale'''
								]

								# Encoder-Decoder-Attention
								UpperCAmelCase_					:Optional[int]      			=      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
								    '''kernel'''
								]
								UpperCAmelCase_					:Optional[Any]      			=      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
								    '''kernel'''
								]
								UpperCAmelCase_					:int      			=      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
								    '''kernel'''
								]
								UpperCAmelCase_					:Tuple      			=      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
								    '''kernel'''
								]

								# Layer Normalization
								UpperCAmelCase_					:str      			=      tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

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

								# Layer Normalization
								UpperCAmelCase_					:Dict      			=      tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

								# Assigning
								UpperCAmelCase_					:Dict      			=      tax_attention_key
								UpperCAmelCase_					:List[Any]      			=      tax_attention_out
								UpperCAmelCase_					:Tuple      			=      tax_attention_query
								UpperCAmelCase_					:List[Any]      			=      tax_attention_value

								UpperCAmelCase_					:Dict      			=      tax_pre_attention_layer_norm

								UpperCAmelCase_					:Optional[int]      			=      tax_enc_dec_attention_key
								UpperCAmelCase_					:Union[str, Any]      			=      tax_enc_dec_attention_out
								UpperCAmelCase_					:str      			=      tax_enc_dec_attention_query
								UpperCAmelCase_					:Dict      			=      tax_enc_dec_attention_value

								UpperCAmelCase_					:List[str]      			=      tax_cross_layer_norm

								if split_mlp_wi:
												UpperCAmelCase_					:int      			=      tax_mlp_wi_a
												UpperCAmelCase_					:Optional[int]      			=      tax_mlp_wi_a
								else:
												UpperCAmelCase_					:Optional[Any]      			=      tax_mlp_wi

								UpperCAmelCase_					:List[str]      			=      tax_mlp_wo

								UpperCAmelCase_					:int      			=      txa_mlp_layer_norm

				# Decoder Normalization
				UpperCAmelCase_					:str      			=      tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
				UpperCAmelCase_					:Tuple      			=      txa_decoder_norm

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

				# Token Embeddings
				UpperCAmelCase_					:Any      			=      tax_model['''target''']['''token_embedder''']['''embedding''']
				UpperCAmelCase_					:Dict      			=      txa_token_embeddings

				# LM Head (only in v1.1 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								UpperCAmelCase_					:int      			=      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"""

def 				a (						__snake_case     :				list[list[int]]  ):



				'''simple docstring'''





				if len(__snake_case  ) == 0 or not all(len(__snake_case  ) == 2 for pair in coordinates  ):
								raise ValueError('''The program cannot work out a fitting polynomial.'''  )

				if len({tuple(__snake_case  ) for pair in coordinates}  ) != len(__snake_case  ):
								raise ValueError('''The program cannot work out a fitting polynomial.'''  )

				UpperCAmelCase_					:Optional[Any]      			=      {x for x, _ in coordinates}
				if len(__snake_case  ) == 1:
								return f'x={coordinates[0][0]}'

				if len(__snake_case  ) != len(__snake_case  ):
								raise ValueError('''The program cannot work out a fitting polynomial.'''  )

				UpperCAmelCase_					:List[Any]      			=      len(__snake_case  )

				UpperCAmelCase_					:str      			=      0
				UpperCAmelCase_					:list[list[float]]      			=      []
				# put the x and x to the power values in a matrix
				while count_of_line < x:
								UpperCAmelCase_					:str      			=      0
								UpperCAmelCase_					:Optional[int]      			=      coordinates[count_of_line][0]
								UpperCAmelCase_					:list[float]      			=      []
								while count_in_line < x:
												count_line.append(a ** (x - (count_in_line + 1))  )
												count_in_line += 1
								matrix.append(__snake_case  )
								count_of_line += 1

				UpperCAmelCase_					:Dict      			=      0
				# put the y values into a vector
				UpperCAmelCase_					:list[float]      			=      []
				while count_of_line < x:
								vector.append(coordinates[count_of_line][1]  )
								count_of_line += 1

				UpperCAmelCase_					:Dict      			=      0

				while count < x:
								UpperCAmelCase_					:Any      			=      0
								while zahlen < x:
												if count == zahlen:
																zahlen += 1
												if zahlen == x:
																break
												UpperCAmelCase_					:str      			=      matrix[zahlen][count] / matrix[count][count]
												for counting_columns, item in enumerate(matrix[count]  ):
																# manipulating all the values in the matrix
																matrix[zahlen][counting_columns] -= item * bruch
												# manipulating the values in the vector
												vector[zahlen] -= vector[count] * bruch
												zahlen += 1
								count += 1

				UpperCAmelCase_					:Tuple      			=      0
				# make solutions
				UpperCAmelCase_					:list[str]      			=      []
				while count < x:
								solution.append(str(vector[count] / matrix[count][count]  )  )
								count += 1

				UpperCAmelCase_					:Optional[int]      			=      0
				UpperCAmelCase_					:Optional[Any]      			=      '''f(x)='''

				while count < x:
								UpperCAmelCase_					:list[str]      			=      solution[count].split('''E'''  )
								if len(__snake_case  ) > 1:
												UpperCAmelCase_					:List[Any]      			=      f'{remove_e[0]}*10^{remove_e[1]}'
								solved += f'x^{x - (count + 1)}*{solution[count]}'
								if count + 1 != x:
												solved += "+"
								count += 1

				return solved


if __name__ == "__main__":
						print(points_to_polynomial([]))
						print(points_to_polynomial([[]]))
						print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
						print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
						print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
						print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
						print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))


