





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:    Union[str, Any]				=  AutoConfig.from_pretrained(_UpperCamelCase )
				snake_case_						:    Optional[int]				=  FlaxAutoModelForSeqaSeqLM.from_config(config=_UpperCamelCase )
				snake_case_						:    Union[str, Any]				=  checkpoints.load_tax_checkpoint(_UpperCamelCase )

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

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

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

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

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

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

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

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

								snake_case_						:    Optional[Any]				=  tax_attention_layer_norm

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

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

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

								snake_case_						:    int				=  flax_model_encoder_layer_block

				# Only for layer 0:
				snake_case_						:    Optional[int]				=  tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
				snake_case_						:    Any				=  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_						:    Any				=  tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
								snake_case_						:    Optional[int]				=  tax_encoder_global_rel_embedding

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

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

								# Self-Attention
								snake_case_						:    List[str]				=  tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
								snake_case_						:    List[Any]				=  tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
								snake_case_						:    Any				=  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_						:    Union[str, Any]				=  tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
								snake_case_						:    Optional[Any]				=  tax_enc_dec_attention_module['''key''']['''kernel''']
								snake_case_						:    Dict				=  tax_enc_dec_attention_module['''out''']['''kernel''']
								snake_case_						:    Tuple				=  tax_enc_dec_attention_module['''query''']['''kernel''']
								snake_case_						:    Dict				=  tax_enc_dec_attention_module['''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_						:    Any				=  tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												snake_case_						:    Optional[Any]				=  tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												snake_case_						:    Tuple				=  tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

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

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

								# Assigning
								snake_case_						:    str				=  flax_model.params['''decoder''']['''block'''][str(_UpperCamelCase )]['''layer''']
								snake_case_						:    List[str]				=  tax_attention_key
								snake_case_						:    Optional[Any]				=  tax_attention_out
								snake_case_						:    List[str]				=  tax_attention_query
								snake_case_						:    Tuple				=  tax_attention_value

								snake_case_						:    List[Any]				=  tax_pre_attention_layer_norm

								snake_case_						:    str				=  tax_enc_dec_attention_key
								snake_case_						:    Any				=  tax_enc_dec_attention_out
								snake_case_						:    Union[str, Any]				=  tax_enc_dec_attention_query
								snake_case_						:    Union[str, Any]				=  tax_enc_dec_attention_value

								snake_case_						:    int				=  tax_cross_layer_norm

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

								snake_case_						:    Optional[int]				=  tax_mlp_wo

								snake_case_						:    Optional[int]				=  txa_mlp_layer_norm

								snake_case_						:    List[str]				=  flax_model_decoder_layer_block

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

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

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

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

				flax_model.save_pretrained(_UpperCamelCase )
				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)







lowerCAmelCase_    =				{
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355_818,
}






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						float:






				"""simple docstring"""


				if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
								snake_case_						:    str				=  (
								    f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
								    f'''Valid values are: {", ".join(_UpperCamelCase )}'''
								)
								raise ValueError(_UpperCamelCase )
				return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
