






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							snake_case_           =				AutoConfig.from_pretrained(a_)
							snake_case_           =				FlaxAutoModelForSeqaSeqLM.from_config(config=a_)
							snake_case_           =				checkpoints.load_tax_checkpoint(a_)

							snake_case_           =				'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

							if config.model_type == "t5":
														snake_case_           =				'SelfAttention'
							if config.model_type == "longt5" and config.encoder_attention_type == "local":
														snake_case_           =				'LocalSelfAttention'
							elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														snake_case_           =				'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):
														snake_case_           =				f'''layers_{str(a_)}'''

														# Self-Attention
														snake_case_           =				tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
														snake_case_           =				tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
														snake_case_           =				tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
														snake_case_           =				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":
																					snake_case_           =				tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

														# Layer Normalization
														snake_case_           =				tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

														if split_mlp_wi:
																					snake_case_           =				tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
																					snake_case_           =				tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
														else:
																					snake_case_           =				tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

														snake_case_           =				tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

														# Layer Normalization
														snake_case_           =				tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

														# Assigning
														snake_case_           =				flax_model.params['encoder']['block'][str(a_)]['layer']
														snake_case_           =				tax_attention_key
														snake_case_           =				tax_attention_out
														snake_case_           =				tax_attention_query
														snake_case_           =				tax_attention_value

														snake_case_           =				tax_attention_layer_norm

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					snake_case_           =				tax_global_layer_norm

														if split_mlp_wi:
																					snake_case_           =				tax_mlp_wi_a
																					snake_case_           =				tax_mlp_wi_a
														else:
																					snake_case_           =				tax_mlp_wi

														snake_case_           =				tax_mlp_wo
														snake_case_           =				tax_mlp_layer_norm

														snake_case_           =				flax_model_encoder_layer_block

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

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

							# Assigning
							snake_case_           =				tax_model['target']['encoder']['encoder_norm']['scale']
							snake_case_           =				tax_encoder_norm

							# Decoder
							for layer_index in range(config.num_layers):
														snake_case_           =				f'''layers_{str(a_)}'''

														# Self-Attention
														snake_case_           =				tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
														snake_case_           =				tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
														snake_case_           =				tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
														snake_case_           =				tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

														# Layer Normalization
														snake_case_           =				tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
														    'scale'
														]

														# Encoder-Decoder-Attention
														snake_case_           =				tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
														snake_case_           =				tax_enc_dec_attention_module['key']['kernel']
														snake_case_           =				tax_enc_dec_attention_module['out']['kernel']
														snake_case_           =				tax_enc_dec_attention_module['query']['kernel']
														snake_case_           =				tax_enc_dec_attention_module['value']['kernel']

														# Layer Normalization
														snake_case_           =				tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

														# MLP
														if split_mlp_wi:
																					snake_case_           =				tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
																					snake_case_           =				tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
														else:
																					snake_case_           =				tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

														snake_case_           =				tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

														# Layer Normalization
														snake_case_           =				tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

														# Assigning
														snake_case_           =				flax_model.params['decoder']['block'][str(a_)]['layer']
														snake_case_           =				tax_attention_key
														snake_case_           =				tax_attention_out
														snake_case_           =				tax_attention_query
														snake_case_           =				tax_attention_value

														snake_case_           =				tax_pre_attention_layer_norm

														snake_case_           =				tax_enc_dec_attention_key
														snake_case_           =				tax_enc_dec_attention_out
														snake_case_           =				tax_enc_dec_attention_query
														snake_case_           =				tax_enc_dec_attention_value

														snake_case_           =				tax_cross_layer_norm

														if split_mlp_wi:
																					snake_case_           =				tax_mlp_wi_a
																					snake_case_           =				tax_mlp_wi_a
														else:
																					snake_case_           =				tax_mlp_wi

														snake_case_           =				tax_mlp_wo

														snake_case_           =				txa_mlp_layer_norm

														snake_case_           =				flax_model_decoder_layer_block

							# Decoder Normalization
							snake_case_           =				tax_model['target']['decoder']['decoder_norm']['scale']
							snake_case_           =				txa_decoder_norm

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

							# Token Embeddings
							snake_case_           =				tax_model['target']['token_embedder']['embedding']
							snake_case_           =				txa_token_embeddings

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

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


if __name__ == "__main__":
	lowercase    				=   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    				=   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 					UpperCamelCase_ :



	'''simple docstring'''




	lowerCAmelCase						=					None
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					None
	lowerCAmelCase						=					None
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					True
	lowerCAmelCase						=					None
	lowerCAmelCase						=					1
	lowerCAmelCase						=					None
	lowerCAmelCase						=					False
	lowerCAmelCase						=					None
	lowerCAmelCase						=					None




	def 				_UpperCamelCase     (			self   )			->       "DownloadConfig":
								return self.__class__(**{k: copy.deepcopy(a   ) for k, v in self.__dict__.items()}   )





