




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def 					UpperCamelCase__       (						lowercase__					:       Union[str, Any]    ,       lowercase__					:       Any    ,       lowercase__					:       Any				):
		snake_case						:  int        =			TaConfig.from_pretrained(lowercase__				)
		snake_case						:  Optional[Any]        =			FlaxTaForConditionalGeneration(config=lowercase__				)
		snake_case						:  Union[str, Any]        =			checkpoints.load_tax_checkpoint(lowercase__				)

		snake_case						:  Dict        =			"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

		# Encoder
		for layer_index in range(config.num_layers				):
				snake_case						:  List[str]        =			F'''layers_{str(lowercase__				)}'''

				# Self-Attention
				snake_case						:  Dict        =			tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
				snake_case						:  Optional[Any]        =			tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
				snake_case						:  Union[str, Any]        =			tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
				snake_case						:  Any        =			tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

				# Layer Normalization
				snake_case						:  List[Any]        =			tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

				if split_mlp_wi:
						snake_case						:  Tuple        =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
						snake_case						:  List[Any]        =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
				else:
						snake_case						:  List[str]        =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

				snake_case						:  List[str]        =			tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

				# Layer Normalization
				snake_case						:  Union[str, Any]        =			tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

				# Assigning
				snake_case						:  Tuple        =			tax_attention_key
				snake_case						:  Tuple        =			tax_attention_out
				snake_case						:  Optional[int]        =			tax_attention_query
				snake_case						:  str        =			tax_attention_value

				snake_case						:  Any        =			tax_attention_layer_norm

				if split_mlp_wi:
						snake_case						:  Any        =			tax_mlp_wi_a
						snake_case						:  List[str]        =			tax_mlp_wi_a
				else:
						snake_case						:  List[Any]        =			tax_mlp_wi

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

		# Only for layer 0:
		snake_case						:  Optional[Any]        =			tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
		snake_case						:  str        =			tax_encoder_rel_embedding

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

		# Decoder
		for layer_index in range(config.num_decoder_layers				):
				snake_case						:  Tuple        =			F'''layers_{str(lowercase__				)}'''

				# Self-Attention
				snake_case						:  List[str]        =			tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
				snake_case						:  Union[str, Any]        =			tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
				snake_case						:  Tuple        =			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						:  Tuple        =			tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
				    "scale"
				]

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

				# Layer Normalization
				snake_case						:  Dict        =			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						:  Optional[int]        =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
				else:
						snake_case						:  Dict        =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

				snake_case						:  str        =			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						:  int        =			tax_attention_key
				snake_case						:  Optional[Any]        =			tax_attention_out
				snake_case						:  List[str]        =			tax_attention_query
				snake_case						:  Union[str, Any]        =			tax_attention_value

				snake_case						:  Any        =			tax_pre_attention_layer_norm

				snake_case						:  Union[str, Any]        =			tax_enc_dec_attention_key
				snake_case						:  int        =			tax_enc_dec_attention_out
				snake_case						:  Tuple        =			tax_enc_dec_attention_query
				snake_case						:  Optional[int]        =			tax_enc_dec_attention_value

				snake_case						:  Dict        =			tax_cross_layer_norm

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

				snake_case						:  List[Any]        =			tax_mlp_wo

				snake_case						:  Optional[Any]        =			txa_mlp_layer_norm

		# Decoder Normalization
		snake_case						:  List[str]        =			tax_model["target"]["decoder"]["decoder_norm"]["scale"]
		snake_case						:  Union[str, Any]        =			txa_decoder_norm

		# Only for layer 0:
		snake_case						:  Any        =			tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
		snake_case						:  Any        =			tax_decoder_rel_embedding

		# Token Embeddings
		snake_case						:  Any        =			tax_model["target"]["token_embedder"]["embedding"]
		snake_case						:  Dict        =			txa_token_embeddings

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

		flax_model.save_pretrained(lowercase__				)
		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)







"""simple docstring"""

def 					UpperCamelCase__       (						lowercase__					:       int				):
		if not isinstance(lowercase__    ,       lowercase__				):
				raise ValueError("double_factorial() only accepts integral values"				)
		if n < 0:
				raise ValueError("double_factorial() not defined for negative values"				)
		return 1 if n <= 1 else n * double_factorial(n - 2				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

