



"""simple docstring"""






from copy import deepcopy

class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						List[str],						lowerCamelCase							:						list[int] | None = None,						lowerCamelCase							:						int | None = None  )->      None:

								if arr is None and size is not None:
											lowerCamelCase__	:	Any				=size
											lowerCamelCase__	:	Optional[int]				=[0] * size
								elif arr is not None:
											self.init(lowerCamelCase  )
								else:
											raise ValueError('''Either arr or size must be specified'''  )



					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						list[int]  )->      None:
								lowerCamelCase__	:	Tuple				=len(lowerCamelCase  )
								lowerCamelCase__	:	int				=deepcopy(lowerCamelCase  )
								for i in range(1,						self.size  ):
											lowerCamelCase__	:	Dict				=self.next_(lowerCamelCase  )
											if j < self.size:
														self.tree[j] += self.tree[i]



					def 	snake_case ( self							:						Optional[int]  )->      list[int]:
								lowerCamelCase__	:	List[str]				=self.tree[:]
								for i in range(self.size - 1,						0,						-1  ):
											lowerCamelCase__	:	Optional[Any]				=self.next_(lowerCamelCase  )
											if j < self.size:
														arr[j] -= arr[i]
								return arr



					@staticmethod
					def 	snake_case ( lowerCamelCase							:						int  )->      int:
								return index + (index & (-index))



					@staticmethod
					def 	snake_case ( lowerCamelCase							:						int  )->      int:
								return index - (index & (-index))



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						int,						lowerCamelCase							:						int  )->      None:
								if index == 0:
											self.tree[0] += value
											return
								while index < self.size:
											self.tree[index] += value
											lowerCamelCase__	:	List[str]				=self.next_(lowerCamelCase  )



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						int,						lowerCamelCase							:						int  )->      None:
								self.add(lowerCamelCase,						value - self.get(lowerCamelCase  )  )



					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						int  )->      int:
								if right == 0:
											return 0
								lowerCamelCase__	:	Dict				=self.tree[0]
								right -= 1  # make right inclusive
								while right > 0:
											result += self.tree[right]
											lowerCamelCase__	:	Union[str, Any]				=self.prev(lowerCamelCase  )
								return result



					def 	snake_case ( self							:						str,						lowerCamelCase							:						int,						lowerCamelCase							:						int  )->      int:
								return self.prefix(lowerCamelCase  ) - self.prefix(lowerCamelCase  )



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						int  )->      int:
								return self.query(lowerCamelCase,						index + 1  )




					def 	snake_case ( self							:						List[Any],						lowerCamelCase							:						int  )->      int:
								value -= self.tree[0]
								if value < 0:
											return -1

								lowerCamelCase__	:	Optional[int]				=1  # Largest power of 2 <= size
								while j * 2 < self.size:
											j *= 2

								lowerCamelCase__	:	Union[str, Any]				=0

								while j > 0:
											if i + j < self.size and self.tree[i + j] <= value:
														value -= self.tree[i + j]
														i += j
											j //= 2
								return i


if __name__ == "__main__":
		import doctest

		doctest.testmod()










"""simple docstring"""






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				=AutoConfig.from_pretrained(__lowerCamelCase			)
			lowerCamelCase__	:	Any				=FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCamelCase			)
			lowerCamelCase__	:	Union[str, Any]				=checkpoints.load_tax_checkpoint(__lowerCamelCase			)

			lowerCamelCase__	:	Union[str, Any]				='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

			if config.model_type == "t5":
						lowerCamelCase__	:	List[str]				='''SelfAttention'''
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						lowerCamelCase__	:	List[Any]				='''LocalSelfAttention'''
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCamelCase__	:	Optional[Any]				='''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			):
						lowerCamelCase__	:	List[Any]				=f'''layers_{str(__lowerCamelCase			)}'''

						# Self-Attention
						lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
						lowerCamelCase__	:	Optional[int]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
						lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
						lowerCamelCase__	:	List[Any]				=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":
									lowerCamelCase__	:	str				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

						# Layer Normalization
						lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

						lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	Tuple				=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						lowerCamelCase__	:	str				=flax_model.params['''encoder''']['''block'''][str(__lowerCamelCase			)]['''layer''']
						lowerCamelCase__	:	int				=tax_attention_key
						lowerCamelCase__	:	Optional[int]				=tax_attention_out
						lowerCamelCase__	:	List[Any]				=tax_attention_query
						lowerCamelCase__	:	Optional[Any]				=tax_attention_value

						lowerCamelCase__	:	List[str]				=tax_attention_layer_norm

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									lowerCamelCase__	:	Optional[int]				=tax_global_layer_norm

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

						lowerCamelCase__	:	str				=tax_mlp_wo
						lowerCamelCase__	:	Optional[Any]				=tax_mlp_layer_norm

						lowerCamelCase__	:	Optional[int]				=flax_model_encoder_layer_block

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

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

			# Assigning
			lowerCamelCase__	:	int				=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
			lowerCamelCase__	:	List[Any]				=tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers			):
						lowerCamelCase__	:	Dict				=f'''layers_{str(__lowerCamelCase			)}'''

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

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

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

						# Layer Normalization
						lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

						lowerCamelCase__	:	Optional[Any]				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

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

						# Assigning
						lowerCamelCase__	:	str				=flax_model.params['''decoder''']['''block'''][str(__lowerCamelCase			)]['''layer''']
						lowerCamelCase__	:	Union[str, Any]				=tax_attention_key
						lowerCamelCase__	:	str				=tax_attention_out
						lowerCamelCase__	:	Optional[int]				=tax_attention_query
						lowerCamelCase__	:	Dict				=tax_attention_value

						lowerCamelCase__	:	List[str]				=tax_pre_attention_layer_norm

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

						lowerCamelCase__	:	Dict				=tax_cross_layer_norm

						if split_mlp_wi:
									lowerCamelCase__	:	Tuple				=tax_mlp_wi_a
									lowerCamelCase__	:	int				=tax_mlp_wi_a
						else:
									lowerCamelCase__	:	List[Any]				=tax_mlp_wi

						lowerCamelCase__	:	Dict				=tax_mlp_wo

						lowerCamelCase__	:	Tuple				=txa_mlp_layer_norm

						lowerCamelCase__	:	Optional[Any]				=flax_model_decoder_layer_block

			# Decoder Normalization
			lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
			lowerCamelCase__	:	int				=txa_decoder_norm

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

			# Token Embeddings
			lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''token_embedder''']['''embedding''']
			lowerCamelCase__	:	Dict				=txa_token_embeddings

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

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


if __name__ == "__main__":
		_lowercase				:				Tuple   		=		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."
		)
		_lowercase				:				List[Any]   		=		parser.parse_args()
		convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




