
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Tuple		    =   AutoConfig.from_pretrained(_UpperCamelCase	)
	__lowerCAmelCase    :		int		    =   FlaxAutoModelForSeqaSeqLM.from_config(config=_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   checkpoints.load_tax_checkpoint(_UpperCamelCase	)

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

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

		# Self-Attention
		__lowerCAmelCase    :		int		    =   tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
		__lowerCAmelCase    :		List[Any]		    =   tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
		__lowerCAmelCase    :		int		    =   tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
		__lowerCAmelCase    :		Tuple		    =   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":
			__lowerCAmelCase    :		Tuple		    =   tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

		# Layer Normalization
		__lowerCAmelCase    :		Optional[Any]		    =   tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

		if split_mlp_wi:
			__lowerCAmelCase    :		Dict		    =   tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
			__lowerCAmelCase    :		Tuple		    =   tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
		else:
			__lowerCAmelCase    :		Any		    =   tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

		__lowerCAmelCase    :		List[str]		    =   tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

		# Layer Normalization
		__lowerCAmelCase    :		Optional[int]		    =   tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

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

		__lowerCAmelCase    :		List[Any]		    =   tax_attention_layer_norm

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

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

		__lowerCAmelCase    :		Tuple		    =   tax_mlp_wo
		__lowerCAmelCase    :		str		    =   tax_mlp_layer_norm

		__lowerCAmelCase    :		Tuple		    =   flax_model_encoder_layer_block

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

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

	# Assigning
	__lowerCAmelCase    :		int		    =   tax_model['target']['encoder']['encoder_norm']['scale']
	__lowerCAmelCase    :		Tuple		    =   tax_encoder_norm

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

		# Self-Attention
		__lowerCAmelCase    :		Union[str, Any]		    =   tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
		__lowerCAmelCase    :		Any		    =   tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
		__lowerCAmelCase    :		int		    =   tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
		__lowerCAmelCase    :		int		    =   tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

		# Layer Normalization
		__lowerCAmelCase    :		int		    =   tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
		    'scale'
		]

		# Encoder-Decoder-Attention
		__lowerCAmelCase    :		Any		    =   tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
		__lowerCAmelCase    :		Dict		    =   tax_enc_dec_attention_module['key']['kernel']
		__lowerCAmelCase    :		Optional[Any]		    =   tax_enc_dec_attention_module['out']['kernel']
		__lowerCAmelCase    :		List[str]		    =   tax_enc_dec_attention_module['query']['kernel']
		__lowerCAmelCase    :		Any		    =   tax_enc_dec_attention_module['value']['kernel']

		# Layer Normalization
		__lowerCAmelCase    :		str		    =   tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

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

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

		# Layer Normalization
		__lowerCAmelCase    :		Optional[int]		    =   tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

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

		__lowerCAmelCase    :		Any		    =   tax_pre_attention_layer_norm

		__lowerCAmelCase    :		Any		    =   tax_enc_dec_attention_key
		__lowerCAmelCase    :		Optional[Any]		    =   tax_enc_dec_attention_out
		__lowerCAmelCase    :		str		    =   tax_enc_dec_attention_query
		__lowerCAmelCase    :		Tuple		    =   tax_enc_dec_attention_value

		__lowerCAmelCase    :		Optional[Any]		    =   tax_cross_layer_norm

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

		__lowerCAmelCase    :		Union[str, Any]		    =   tax_mlp_wo

		__lowerCAmelCase    :		int		    =   txa_mlp_layer_norm

		__lowerCAmelCase    :		List[str]		    =   flax_model_decoder_layer_block

	# Decoder Normalization
	__lowerCAmelCase    :		Optional[Any]		    =   tax_model['target']['decoder']['decoder_norm']['scale']
	__lowerCAmelCase    :		Optional[int]		    =   txa_decoder_norm

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

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

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

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase = False	):

	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		str		    =   F"Expected string as input, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)
	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   F"Expected boolean as use_pascal parameter, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)

	__lowerCAmelCase    :		Tuple		    =   input_str.split('_'	)

	__lowerCAmelCase    :		int		    =   0 if use_pascal else 1

	__lowerCAmelCase    :		Any		    =   words[start_index:]

	__lowerCAmelCase    :		Any		    =   [word[0].upper() + word[1:] for word in words_to_capitalize]

	__lowerCAmelCase    :		Any		    =   '' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()