






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def 					_lowerCamelCase(			lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Any      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[Any]													=   AutoConfig.from_pretrained(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   checkpoints.load_tax_checkpoint(lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	Dict													=   'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

				if config.model_type == "t5":
								SCREAMING_SNAKE_CASE_   :	List[str]													=   'SelfAttention'
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								SCREAMING_SNAKE_CASE_   :	int													=   'LocalSelfAttention'
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								SCREAMING_SNAKE_CASE_   :	Dict													=   '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      ):
								SCREAMING_SNAKE_CASE_   :	Tuple													=   F'''layers_{str(lowerCAmelCase__      )}'''

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

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

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

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

								# Layer Normalization
								SCREAMING_SNAKE_CASE_   :	List[str]													=   tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

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

								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   tax_attention_layer_norm

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

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

								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tax_mlp_wo
								SCREAMING_SNAKE_CASE_   :	Tuple													=   tax_mlp_layer_norm

								SCREAMING_SNAKE_CASE_   :	Any													=   flax_model_encoder_layer_block

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

				# Side/global relative position_bias + layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								SCREAMING_SNAKE_CASE_   :	List[str]													=   tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tax_encoder_global_rel_embedding

				# Assigning
				SCREAMING_SNAKE_CASE_   :	Dict													=   tax_model['target']['encoder']['encoder_norm']['scale']
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   tax_encoder_norm

				# Decoder
				for layer_index in range(config.num_layers      ):
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   F'''layers_{str(lowerCAmelCase__      )}'''

								# Self-Attention
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
								SCREAMING_SNAKE_CASE_   :	Any													=   tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
								SCREAMING_SNAKE_CASE_   :	Tuple													=   tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
								SCREAMING_SNAKE_CASE_   :	Dict													=   tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

								# Layer Normalization
								SCREAMING_SNAKE_CASE_   :	List[str]													=   tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
								    'scale'
								]

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

								# Layer Normalization
								SCREAMING_SNAKE_CASE_   :	int													=   tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

								# MLP
								if split_mlp_wi:
												SCREAMING_SNAKE_CASE_   :	Tuple													=   tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
												SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
								else:
												SCREAMING_SNAKE_CASE_   :	str													=   tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

								SCREAMING_SNAKE_CASE_   :	str													=   tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

								# Layer Normalization
								SCREAMING_SNAKE_CASE_   :	Tuple													=   tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

								# Assigning
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   flax_model.params['decoder']['block'][str(lowerCAmelCase__      )]['layer']
								SCREAMING_SNAKE_CASE_   :	int													=   tax_attention_key
								SCREAMING_SNAKE_CASE_   :	int													=   tax_attention_out
								SCREAMING_SNAKE_CASE_   :	Tuple													=   tax_attention_query
								SCREAMING_SNAKE_CASE_   :	List[str]													=   tax_attention_value

								SCREAMING_SNAKE_CASE_   :	Tuple													=   tax_pre_attention_layer_norm

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

								SCREAMING_SNAKE_CASE_   :	List[str]													=   tax_cross_layer_norm

								if split_mlp_wi:
												SCREAMING_SNAKE_CASE_   :	List[str]													=   tax_mlp_wi_a
												SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tax_mlp_wi_a
								else:
												SCREAMING_SNAKE_CASE_   :	Dict													=   tax_mlp_wi

								SCREAMING_SNAKE_CASE_   :	str													=   tax_mlp_wo

								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   txa_mlp_layer_norm

								SCREAMING_SNAKE_CASE_   :	List[Any]													=   flax_model_decoder_layer_block

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

				# Only for layer 0:
				SCREAMING_SNAKE_CASE_   :	Dict													=   tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tax_decoder_rel_embedding

				# Token Embeddings
				SCREAMING_SNAKE_CASE_   :	Any													=   tax_model['target']['token_embedder']['embedding']
				SCREAMING_SNAKE_CASE_   :	Tuple													=   txa_token_embeddings

				# LM Head (only in v1.1 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								SCREAMING_SNAKE_CASE_   :	List[str]													=   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 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.'
				)
				A        =			parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union


@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[Union[str, Path]]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				Optional[Dict]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						True
		UpperCAmelCase__      :				Optional[int]				    =						None
		UpperCAmelCase__      :				int				    =						1
		UpperCAmelCase__      :				Optional[Union[str, bool]]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				Optional[Dict]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None


		def   __snake_case			(						self       ):
						return self.__class__(**{k: copy.deepcopy(UpperCamelCase__       ) for k, v in self.__dict__.items()}       )