





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				snake_case_						:   Optional[int]           =  AutoConfig.from_pretrained(_UpperCamelCase			)
				snake_case_						:   Dict           =  FlaxAutoModelForSeqaSeqLM.from_config(config=_UpperCamelCase			)
				snake_case_						:   List[str]           =  checkpoints.load_tax_checkpoint(_UpperCamelCase			)

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

				if config.model_type == "t5":
								snake_case_						:   Optional[Any]           =  '''SelfAttention'''
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								snake_case_						:   Union[str, Any]           =  '''LocalSelfAttention'''
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								snake_case_						:   str           =  '''TransientGlobalSelfAttention'''
				else:
								raise ValueError(
								    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
								    ''' attribute with a value from [\'local\', \'transient-global].'''			)

				# Encoder
				for layer_index in range(config.num_layers			):
								snake_case_						:   Optional[Any]           =  f'''layers_{str(_UpperCamelCase			)}'''

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

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												snake_case_						:   Optional[Any]           =  tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

								# Layer Normalization
								snake_case_						:   Union[str, Any]           =  tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

								if split_mlp_wi:
												snake_case_						:   Dict           =  tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												snake_case_						:   Any           =  tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												snake_case_						:   Union[str, Any]           =  tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

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

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

								# Assigning
								snake_case_						:   List[Any]           =  flax_model.params['''encoder''']['''block'''][str(_UpperCamelCase			)]['''layer''']
								snake_case_						:   Optional[int]           =  tax_attention_key
								snake_case_						:   Union[str, Any]           =  tax_attention_out
								snake_case_						:   Union[str, Any]           =  tax_attention_query
								snake_case_						:   Dict           =  tax_attention_value

								snake_case_						:   str           =  tax_attention_layer_norm

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												snake_case_						:   Tuple           =  tax_global_layer_norm

								if split_mlp_wi:
												snake_case_						:   List[Any]           =  tax_mlp_wi_a
												snake_case_						:   Optional[int]           =  tax_mlp_wi_a
								else:
												snake_case_						:   Dict           =  tax_mlp_wi

								snake_case_						:   Optional[int]           =  tax_mlp_wo
								snake_case_						:   List[Any]           =  tax_mlp_layer_norm

								snake_case_						:   List[str]           =  flax_model_encoder_layer_block

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

				# Side/global relative position_bias + layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								snake_case_						:   List[str]           =  tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
								snake_case_						:   int           =  tax_encoder_global_rel_embedding

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

				# Decoder
				for layer_index in range(config.num_layers			):
								snake_case_						:   str           =  f'''layers_{str(_UpperCamelCase			)}'''

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

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

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

								# Layer Normalization
								snake_case_						:   Optional[int]           =  tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

								# MLP
								if split_mlp_wi:
												snake_case_						:   Optional[Any]           =  tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												snake_case_						:   int           =  tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												snake_case_						:   Tuple           =  tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								snake_case_						:   Tuple           =  tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

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

								# Assigning
								snake_case_						:   List[str]           =  flax_model.params['''decoder''']['''block'''][str(_UpperCamelCase			)]['''layer''']
								snake_case_						:   Union[str, Any]           =  tax_attention_key
								snake_case_						:   Dict           =  tax_attention_out
								snake_case_						:   Union[str, Any]           =  tax_attention_query
								snake_case_						:   List[Any]           =  tax_attention_value

								snake_case_						:   Optional[Any]           =  tax_pre_attention_layer_norm

								snake_case_						:   Optional[Any]           =  tax_enc_dec_attention_key
								snake_case_						:   Dict           =  tax_enc_dec_attention_out
								snake_case_						:   Tuple           =  tax_enc_dec_attention_query
								snake_case_						:   Any           =  tax_enc_dec_attention_value

								snake_case_						:   Tuple           =  tax_cross_layer_norm

								if split_mlp_wi:
												snake_case_						:   List[Any]           =  tax_mlp_wi_a
												snake_case_						:   Union[str, Any]           =  tax_mlp_wi_a
								else:
												snake_case_						:   Optional[Any]           =  tax_mlp_wi

								snake_case_						:   Any           =  tax_mlp_wo

								snake_case_						:   Tuple           =  txa_mlp_layer_norm

								snake_case_						:   int           =  flax_model_decoder_layer_block

				# Decoder Normalization
				snake_case_						:   str           =  tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
				snake_case_						:   int           =  txa_decoder_norm

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

				# Token Embeddings
				snake_case_						:   List[str]           =  tax_model['''target''']['''token_embedder''']['''embedding''']
				snake_case_						:   Optional[int]           =  txa_token_embeddings

				# LM Head (only in v1.1 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								snake_case_						:   Tuple           =  tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

				flax_model.save_pretrained(_UpperCamelCase			)
				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 T5X checkpoint.'''
	)
	parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/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)







def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
								raise ValueError('''multiplicative_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''multiplicative_persistence() does not accept negative values'''			)

				snake_case_						:   List[Any]           =  0
				snake_case_						:   Tuple           =  str(_UpperCamelCase			)

				while len(_UpperCamelCase			) != 1:
								snake_case_						:   Tuple           =  [int(_UpperCamelCase			) for i in num_string]

								snake_case_						:   Dict           =  1
								for i in range(0			,    len(_UpperCamelCase			)			):
												total *= numbers[i]

								snake_case_						:   str           =  str(_UpperCamelCase			)

								steps += 1
				return steps






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
								raise ValueError('''additive_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''additive_persistence() does not accept negative values'''			)

				snake_case_						:   Any           =  0
				snake_case_						:   Tuple           =  str(_UpperCamelCase			)

				while len(_UpperCamelCase			) != 1:
								snake_case_						:   List[str]           =  [int(_UpperCamelCase			) for i in num_string]

								snake_case_						:   Optional[int]           =  0
								for i in range(0			,    len(_UpperCamelCase			)			):
												total += numbers[i]

								snake_case_						:   Tuple           =  str(_UpperCamelCase			)

								steps += 1
				return steps


if __name__ == "__main__":
	import doctest

	doctest.testmod()
