






import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    Dict:





						"""simple docstring"""



						_UpperCAmelCase							   =     TaConfig.from_pretrained(__lowerCAmelCase      )
						_UpperCAmelCase							   =     FlaxTaForConditionalGeneration(config=__lowerCAmelCase      )
						_UpperCAmelCase							   =     checkpoints.load_tax_checkpoint(__lowerCAmelCase      )

						_UpperCAmelCase							   =     'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

						# Encoder
						for layer_index in range(config.num_layers      ):
												_UpperCAmelCase							   =     F"""layers_{str(__lowerCAmelCase      )}"""

												# Self-Attention
												_UpperCAmelCase							   =     tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
												_UpperCAmelCase							   =     tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
												_UpperCAmelCase							   =     tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
												_UpperCAmelCase							   =     tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

												# Layer Normalization
												_UpperCAmelCase							   =     tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

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

												_UpperCAmelCase							   =     tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

												# Layer Normalization
												_UpperCAmelCase							   =     tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

												# Assigning
												_UpperCAmelCase							   =     tax_attention_key
												_UpperCAmelCase							   =     tax_attention_out
												_UpperCAmelCase							   =     tax_attention_query
												_UpperCAmelCase							   =     tax_attention_value

												_UpperCAmelCase							   =     tax_attention_layer_norm

												if split_mlp_wi:
																		_UpperCAmelCase							   =     tax_mlp_wi_a
																		_UpperCAmelCase							   =     tax_mlp_wi_a
												else:
																		_UpperCAmelCase							   =     tax_mlp_wi

												_UpperCAmelCase							   =     tax_mlp_wo
												_UpperCAmelCase							   =     tax_mlp_layer_norm

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

						# Assigning
						_UpperCAmelCase							   =     tax_model['target']['encoder']['encoder_norm']['scale']
						_UpperCAmelCase							   =     tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_layers      ):
												_UpperCAmelCase							   =     F"""layers_{str(__lowerCAmelCase      )}"""

												# Self-Attention
												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

												# Layer Normalization
												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
												    'scale'
												]

												# Encoder-Decoder-Attention
												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['key'][
												    'kernel'
												]
												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
												    'kernel'
												]
												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
												    'kernel'
												]
												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['value'][
												    'kernel'
												]

												# Layer Normalization
												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

												# MLP
												if split_mlp_wi:
																		_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
																		_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
												else:
																		_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

												# Layer Normalization
												_UpperCAmelCase							   =     tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

												# Assigning
												_UpperCAmelCase							   =     tax_attention_key
												_UpperCAmelCase							   =     tax_attention_out
												_UpperCAmelCase							   =     tax_attention_query
												_UpperCAmelCase							   =     tax_attention_value

												_UpperCAmelCase							   =     tax_pre_attention_layer_norm

												_UpperCAmelCase							   =     tax_enc_dec_attention_key
												_UpperCAmelCase							   =     tax_enc_dec_attention_out
												_UpperCAmelCase							   =     tax_enc_dec_attention_query
												_UpperCAmelCase							   =     tax_enc_dec_attention_value

												_UpperCAmelCase							   =     tax_cross_layer_norm

												if split_mlp_wi:
																		_UpperCAmelCase							   =     tax_mlp_wi_a
																		_UpperCAmelCase							   =     tax_mlp_wi_a
												else:
																		_UpperCAmelCase							   =     tax_mlp_wi

												_UpperCAmelCase							   =     tax_mlp_wo

												_UpperCAmelCase							   =     txa_mlp_layer_norm

						# Decoder Normalization
						_UpperCAmelCase							   =     tax_model['target']['decoder']['decoder_norm']['scale']
						_UpperCAmelCase							   =     txa_decoder_norm

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

						# Token Embeddings
						_UpperCAmelCase							   =     tax_model['target']['token_embedder']['embedding']
						_UpperCAmelCase							   =     txa_token_embeddings

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

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


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








from __future__ import annotations

import re


def __A       ( __lowerCAmelCase      )->    list[str]:





						"""simple docstring"""



						def alphanum_key(__lowerCAmelCase      ):
												return [int(__lowerCAmelCase      ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'					,						__lowerCAmelCase      )]

						return sorted(__lowerCAmelCase					,						key=__lowerCAmelCase      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
