





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[Any]      =       TaConfig.from_pretrained(_lowercase  )
						_UpperCamelCase     :   Dict      =       FlaxTaForConditionalGeneration(config=_lowercase  )
						_UpperCamelCase     :   List[str]      =       checkpoints.load_tax_checkpoint(_lowercase  )

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

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

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

												# Layer Normalization
												_UpperCamelCase     :   Optional[int]      =       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     :   int      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		_UpperCamelCase     :   Optional[Any]      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

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

												# Layer Normalization
												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												_UpperCamelCase     :   Dict      =       tax_attention_key
												_UpperCamelCase     :   Dict      =       tax_attention_out
												_UpperCamelCase     :   List[str]      =       tax_attention_query
												_UpperCamelCase     :   Dict      =       tax_attention_value

												_UpperCamelCase     :   Dict      =       tax_attention_layer_norm

												if split_mlp_wi:
																		_UpperCamelCase     :   str      =       tax_mlp_wi_a
																		_UpperCamelCase     :   Union[str, Any]      =       tax_mlp_wi_a
												else:
																		_UpperCamelCase     :   int      =       tax_mlp_wi

												_UpperCamelCase     :   str      =       tax_mlp_wo
												_UpperCamelCase     :   int      =       tax_mlp_layer_norm

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

						# Assigning
						_UpperCamelCase     :   int      =       tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
						_UpperCamelCase     :   Tuple      =       tax_encoder_norm

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

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

												# Layer Normalization
												_UpperCamelCase     :   int      =       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     :   List[Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
												    '''kernel'''
												]
												_UpperCamelCase     :   List[Any]      =       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     :   int      =       tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

												# MLP
												if split_mlp_wi:
																		_UpperCamelCase     :   str      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		_UpperCamelCase     :   Union[str, Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		_UpperCamelCase     :   Optional[int]      =       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     :   Any      =       tax_attention_key
												_UpperCamelCase     :   str      =       tax_attention_out
												_UpperCamelCase     :   int      =       tax_attention_query
												_UpperCamelCase     :   Tuple      =       tax_attention_value

												_UpperCamelCase     :   Optional[int]      =       tax_pre_attention_layer_norm

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

												_UpperCamelCase     :   str      =       tax_cross_layer_norm

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

												_UpperCamelCase     :   str      =       tax_mlp_wo

												_UpperCamelCase     :   Dict      =       txa_mlp_layer_norm

						# Decoder Normalization
						_UpperCamelCase     :   int      =       tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
						_UpperCamelCase     :   Any      =       txa_decoder_norm

						# Only for layer 0:
						_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
						_UpperCamelCase     :   Dict      =       tax_decoder_rel_embedding

						# Token Embeddings
						_UpperCamelCase     :   List[str]      =       tax_model['''target''']['''token_embedder''']['''embedding''']
						_UpperCamelCase     :   Tuple      =       txa_token_embeddings

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

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


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











'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A      :     Union[str, Any]       	=logging.get_logger(__name__)

_A      :     Tuple       	={
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class _lowercase      (							_lowercase						):
							a							 =			"""splinter"""




							def __init__(				self:							List[str]     ,				UpperCamelCase__:							List[Any]=30_522     ,				UpperCamelCase__:							Union[str, Any]=768     ,				UpperCamelCase__:							str=12     ,				UpperCamelCase__:							Dict=12     ,				UpperCamelCase__:							Tuple=3_072     ,				UpperCamelCase__:							Optional[int]="gelu"     ,				UpperCamelCase__:							Optional[int]=0.1     ,				UpperCamelCase__:							str=0.1     ,				UpperCamelCase__:							List[Any]=512     ,				UpperCamelCase__:							List[str]=2     ,				UpperCamelCase__:							Any=0.02     ,				UpperCamelCase__:							Optional[Any]=1e-12     ,				UpperCamelCase__:							Dict=True     ,				UpperCamelCase__:							str=0     ,				UpperCamelCase__:							int=104     ,				**UpperCamelCase__:							Any     ,				):
														super().__init__(pad_token_id=UpperCamelCase__     ,				**UpperCamelCase__	)

														lowerCamelCase__   :      Union[str, Any]			    =			vocab_size
														lowerCamelCase__   :      str			    =			max_position_embeddings
														lowerCamelCase__   :      Tuple			    =			hidden_size
														lowerCamelCase__   :      Dict			    =			num_hidden_layers
														lowerCamelCase__   :      Tuple			    =			num_attention_heads
														lowerCamelCase__   :      Tuple			    =			intermediate_size
														lowerCamelCase__   :      Any			    =			hidden_act
														lowerCamelCase__   :      int			    =			hidden_dropout_prob
														lowerCamelCase__   :      List[str]			    =			attention_probs_dropout_prob
														lowerCamelCase__   :      Dict			    =			initializer_range
														lowerCamelCase__   :      List[Any]			    =			type_vocab_size
														lowerCamelCase__   :      Tuple			    =			layer_norm_eps
														lowerCamelCase__   :      Optional[Any]			    =			use_cache
														lowerCamelCase__   :      Optional[Any]			    =			question_token_id

