






"""simple docstring"""
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Optional[Any]     	=   AutoConfig.from_pretrained(snake_case__		)
			UpperCAmelCase_	:					Optional[int]     	=   FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case__		)
			UpperCAmelCase_	:					str     	=   checkpoints.load_tax_checkpoint(snake_case__		)

			UpperCAmelCase_	:					Union[str, Any]     	=   "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

			if config.model_type == "t5":
						UpperCAmelCase_	:					Any     	=   "SelfAttention"
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						UpperCAmelCase_	:					List[Any]     	=   "LocalSelfAttention"
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						UpperCAmelCase_	:					int     	=   "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		):
						UpperCAmelCase_	:					Optional[int]     	=   F"""layers_{str(snake_case__		)}"""

						# Self-Attention
						UpperCAmelCase_	:					Tuple     	=   tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
						UpperCAmelCase_	:					str     	=   tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
						UpperCAmelCase_	:					Union[str, Any]     	=   tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
						UpperCAmelCase_	:					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":
									UpperCAmelCase_	:					Optional[int]     	=   tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

						# Layer Normalization
						UpperCAmelCase_	:					List[str]     	=   tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

						if split_mlp_wi:
									UpperCAmelCase_	:					Any     	=   tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									UpperCAmelCase_	:					int     	=   tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									UpperCAmelCase_	:					Tuple     	=   tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

						UpperCAmelCase_	:					int     	=   tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						UpperCAmelCase_	:					str     	=   tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						UpperCAmelCase_	:					Tuple     	=   flax_model.params["encoder"]["block"][str(snake_case__		)]["layer"]
						UpperCAmelCase_	:					Any     	=   tax_attention_key
						UpperCAmelCase_	:					Tuple     	=   tax_attention_out
						UpperCAmelCase_	:					Dict     	=   tax_attention_query
						UpperCAmelCase_	:					Any     	=   tax_attention_value

						UpperCAmelCase_	:					int     	=   tax_attention_layer_norm

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

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

						UpperCAmelCase_	:					str     	=   tax_mlp_wo
						UpperCAmelCase_	:					Any     	=   tax_mlp_layer_norm

						UpperCAmelCase_	:					Optional[Any]     	=   flax_model_encoder_layer_block

			# Only for layer 0:
			UpperCAmelCase_	:					Union[str, Any]     	=   tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
			UpperCAmelCase_	:					List[str]     	=   tax_encoder_rel_embedding

			# Side/global relative position_bias + layer norm
			if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						UpperCAmelCase_	:					Tuple     	=   tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
						UpperCAmelCase_	:					Any     	=   tax_encoder_global_rel_embedding

			# Assigning
			UpperCAmelCase_	:					Union[str, Any]     	=   tax_model["target"]["encoder"]["encoder_norm"]["scale"]
			UpperCAmelCase_	:					List[Any]     	=   tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers		):
						UpperCAmelCase_	:					str     	=   F"""layers_{str(snake_case__		)}"""

						# Self-Attention
						UpperCAmelCase_	:					str     	=   tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
						UpperCAmelCase_	:					Union[str, Any]     	=   tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
						UpperCAmelCase_	:					Any     	=   tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
						UpperCAmelCase_	:					List[str]     	=   tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

						# Layer Normalization
						UpperCAmelCase_	:					Tuple     	=   tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
						    "scale"
						]

						# Encoder-Decoder-Attention
						UpperCAmelCase_	:					int     	=   tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
						UpperCAmelCase_	:					Tuple     	=   tax_enc_dec_attention_module["key"]["kernel"]
						UpperCAmelCase_	:					Any     	=   tax_enc_dec_attention_module["out"]["kernel"]
						UpperCAmelCase_	:					Optional[int]     	=   tax_enc_dec_attention_module["query"]["kernel"]
						UpperCAmelCase_	:					Tuple     	=   tax_enc_dec_attention_module["value"]["kernel"]

						# Layer Normalization
						UpperCAmelCase_	:					Tuple     	=   tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

						# MLP
						if split_mlp_wi:
									UpperCAmelCase_	:					Dict     	=   tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									UpperCAmelCase_	:					Tuple     	=   tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									UpperCAmelCase_	:					Any     	=   tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

						UpperCAmelCase_	:					List[Any]     	=   tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						UpperCAmelCase_	:					int     	=   tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						UpperCAmelCase_	:					int     	=   flax_model.params["decoder"]["block"][str(snake_case__		)]["layer"]
						UpperCAmelCase_	:					int     	=   tax_attention_key
						UpperCAmelCase_	:					Any     	=   tax_attention_out
						UpperCAmelCase_	:					Optional[int]     	=   tax_attention_query
						UpperCAmelCase_	:					List[Any]     	=   tax_attention_value

						UpperCAmelCase_	:					List[str]     	=   tax_pre_attention_layer_norm

						UpperCAmelCase_	:					Optional[Any]     	=   tax_enc_dec_attention_key
						UpperCAmelCase_	:					List[str]     	=   tax_enc_dec_attention_out
						UpperCAmelCase_	:					Optional[Any]     	=   tax_enc_dec_attention_query
						UpperCAmelCase_	:					Dict     	=   tax_enc_dec_attention_value

						UpperCAmelCase_	:					List[str]     	=   tax_cross_layer_norm

						if split_mlp_wi:
									UpperCAmelCase_	:					str     	=   tax_mlp_wi_a
									UpperCAmelCase_	:					Any     	=   tax_mlp_wi_a
						else:
									UpperCAmelCase_	:					Dict     	=   tax_mlp_wi

						UpperCAmelCase_	:					Optional[int]     	=   tax_mlp_wo

						UpperCAmelCase_	:					Union[str, Any]     	=   txa_mlp_layer_norm

						UpperCAmelCase_	:					str     	=   flax_model_decoder_layer_block

			# Decoder Normalization
			UpperCAmelCase_	:					Optional[int]     	=   tax_model["target"]["decoder"]["decoder_norm"]["scale"]
			UpperCAmelCase_	:					List[str]     	=   txa_decoder_norm

			# Only for layer 0:
			UpperCAmelCase_	:					List[Any]     	=   tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
			UpperCAmelCase_	:					Union[str, Any]     	=   tax_decoder_rel_embedding

			# Token Embeddings
			UpperCAmelCase_	:					int     	=   tax_model["target"]["token_embedder"]["embedding"]
			UpperCAmelCase_	:					List[str]     	=   txa_token_embeddings

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

			flax_model.save_pretrained(snake_case__		)
			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)





import copy

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


__a         =   logging.get_logger(__name__)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[str]       	=      'encoder-decoder'
	a     :Any       	=      True
	def __init__(			self    :     List[Any]			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Optional[int]:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					assert (
					    "encoder" in kwargs and "decoder" in kwargs
					), "Config has to be initialized with encoder and decoder config"
					lowercase_	    =   kwargs.pop('''encoder'''			)
					lowercase_	    =   encoder_config.pop('''model_type'''			)
					lowercase_	    =   kwargs.pop('''decoder'''			)
					lowercase_	    =   decoder_config.pop('''model_type'''			)

					from ..auto.configuration_auto import AutoConfig

					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   True
	@classmethod
	def 		_lowercase    (			cls    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	PretrainedConfig:
					logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''			)
					lowercase_	    =   True
					lowercase_	    =   True

					return cls(encoder=encoder_config.to_dict()			,				decoder=decoder_config.to_dict()			,				**SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     Tuple			)			->	List[Any]:
					lowercase_	    =   copy.deepcopy(self.__dict__			)
					lowercase_	    =   self.encoder.to_dict()
					lowercase_	    =   self.decoder.to_dict()
					lowercase_	    =   self.__class__.model_type
					return output




