


'''simple docstring'''




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def 			__UpperCamelCase    (    _A    :				Optional[int]					,   _A    :				Dict					,   _A    :				Any				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= AutoConfig.from_pretrained(_A				)
	lowerCAmelCase					:    int      							= FlaxAutoModelForSeqaSeqLM.from_config(config=_A				)
	lowerCAmelCase					:    int      							= checkpoints.load_tax_checkpoint(_A				)

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

	if config.model_type == "t5":
		lowerCAmelCase					:    Tuple      							= 'SelfAttention'
	if config.model_type == "longt5" and config.encoder_attention_type == "local":
		lowerCAmelCase					:    Dict      							= 'LocalSelfAttention'
	elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
		lowerCAmelCase					:    Tuple      							= '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					:    Optional[Any]      							= F"layers_{str(_A				)}"

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

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

		if split_mlp_wi:
			lowerCAmelCase					:    Any      							= tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
			lowerCAmelCase					:    Optional[int]      							= tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
		else:
			lowerCAmelCase					:    Optional[int]      							= tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

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

		# Layer Normalization
		lowerCAmelCase					:    Union[str, Any]      							= tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

		# Assigning
		lowerCAmelCase					:    str      							= flax_model.params['encoder']['block'][str(_A				)]['layer']
		lowerCAmelCase					:    str      							= tax_attention_key
		lowerCAmelCase					:    List[Any]      							= tax_attention_out
		lowerCAmelCase					:    Any      							= tax_attention_query
		lowerCAmelCase					:    str      							= 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					:    str      							= tax_mlp_wi_a
		else:
			lowerCAmelCase					:    Tuple      							= tax_mlp_wi

		lowerCAmelCase					:    Optional[int]      							= tax_mlp_wo
		lowerCAmelCase					:    List[str]      							= tax_mlp_layer_norm

		lowerCAmelCase					:    Tuple      							= flax_model_encoder_layer_block

	# Only for layer 0:
	lowerCAmelCase					:    Optional[int]      							= tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
	lowerCAmelCase					:    Optional[Any]      							= tax_encoder_rel_embedding

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

	# Assigning
	lowerCAmelCase					:    List[Any]      							= tax_model['target']['encoder']['encoder_norm']['scale']
	lowerCAmelCase					:    int      							= tax_encoder_norm

	# Decoder
	for layer_index in range(config.num_layers				):
		lowerCAmelCase					:    Dict      							= F"layers_{str(_A				)}"

		# Self-Attention
		lowerCAmelCase					:    Optional[int]      							= tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
		lowerCAmelCase					:    Optional[int]      							= tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
		lowerCAmelCase					:    Any      							= tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
		lowerCAmelCase					:    List[Any]      							= tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

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

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

		# Layer Normalization
		lowerCAmelCase					:    Dict      							= tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

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

		lowerCAmelCase					:    Union[str, Any]      							= tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

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

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

		lowerCAmelCase					:    List[str]      							= tax_pre_attention_layer_norm

		lowerCAmelCase					:    List[str]      							= tax_enc_dec_attention_key
		lowerCAmelCase					:    Any      							= tax_enc_dec_attention_out
		lowerCAmelCase					:    int      							= tax_enc_dec_attention_query
		lowerCAmelCase					:    Optional[int]      							= tax_enc_dec_attention_value

		lowerCAmelCase					:    List[Any]      							= tax_cross_layer_norm

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

		lowerCAmelCase					:    Tuple      							= tax_mlp_wo

		lowerCAmelCase					:    Tuple      							= txa_mlp_layer_norm

		lowerCAmelCase					:    int      							= flax_model_decoder_layer_block

	# Decoder Normalization
	lowerCAmelCase					:    Optional[Any]      							= tax_model['target']['decoder']['decoder_norm']['scale']
	lowerCAmelCase					:    Union[str, Any]      							= txa_decoder_norm

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

	# Token Embeddings
	lowerCAmelCase					:    Any      							= tax_model['target']['token_embedder']['embedding']
	lowerCAmelCase					:    str      							= txa_token_embeddings

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

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


if __name__ == "__main__":
				_lowerCAmelCase  :		Any		     =	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  :		Optional[Any]		     =	parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




'''simple docstring'''




import math

def 			__UpperCamelCase    (    _A    :				int = 1_00				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= sum(i * i for i in range(1					,   n + 1				)				)
	lowerCAmelCase					:    Optional[Any]      							= int(math.pow(sum(range(1					,   n + 1				)				)					,   2				)				)
	return square_of_sum - sum_of_squares


if __name__ == "__main__":
				print(f"""{solution() = }""")
