




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> Optional[Any]:
	a		       =							TaConfig.from_pretrained(__lowerCamelCase					)
	a		       =							FlaxTaForConditionalGeneration(config=__lowerCamelCase					)
	a		       =							checkpoints.load_tax_checkpoint(__lowerCamelCase					)

	a		       =							"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

	# Encoder
	for layer_index in range(config.num_layers					):
		a		       =							f'layers_{str(__lowerCamelCase					)}'

		# Self-Attention
		a		       =							tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
		a		       =							tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
		a		       =							tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
		a		       =							tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

		# Layer Normalization
		a		       =							tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

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

		a		       =							tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

		# Layer Normalization
		a		       =							tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

		# Assigning
		a		       =							tax_attention_key
		a		       =							tax_attention_out
		a		       =							tax_attention_query
		a		       =							tax_attention_value

		a		       =							tax_attention_layer_norm

		if split_mlp_wi:
			a		       =							tax_mlp_wi_a
			a		       =							tax_mlp_wi_a
		else:
			a		       =							tax_mlp_wi

		a		       =							tax_mlp_wo
		a		       =							tax_mlp_layer_norm

	# Only for layer 0:
	a		       =							tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
	a		       =							tax_encoder_rel_embedding

	# Assigning
	a		       =							tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
	a		       =							tax_encoder_norm

	# Decoder
	for layer_index in range(config.num_decoder_layers					):
		a		       =							f'layers_{str(__lowerCamelCase					)}'

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

		# Layer Normalization
		a		       =							tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
		    """scale"""
		]

		# Encoder-Decoder-Attention
		a		       =							tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
		    """kernel"""
		]
		a		       =							tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
		    """kernel"""
		]
		a		       =							tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
		    """kernel"""
		]
		a		       =							tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
		    """kernel"""
		]

		# Layer Normalization
		a		       =							tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

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

		a		       =							tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

		# Layer Normalization
		a		       =							tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

		# Assigning
		a		       =							tax_attention_key
		a		       =							tax_attention_out
		a		       =							tax_attention_query
		a		       =							tax_attention_value

		a		       =							tax_pre_attention_layer_norm

		a		       =							tax_enc_dec_attention_key
		a		       =							tax_enc_dec_attention_out
		a		       =							tax_enc_dec_attention_query
		a		       =							tax_enc_dec_attention_value

		a		       =							tax_cross_layer_norm

		if split_mlp_wi:
			a		       =							tax_mlp_wi_a
			a		       =							tax_mlp_wi_a
		else:
			a		       =							tax_mlp_wi

		a		       =							tax_mlp_wo

		a		       =							txa_mlp_layer_norm

	# Decoder Normalization
	a		       =							tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
	a		       =							txa_decoder_norm

	# Only for layer 0:
	a		       =							tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
	a		       =							tax_decoder_rel_embedding

	# Token Embeddings
	a		       =							tax_model["""target"""]["""token_embedder"""]["""embedding"""]
	a		       =							txa_token_embeddings

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

	flax_model.save_pretrained(__lowerCamelCase					)
	print("""T5X Model was sucessfully converted!"""					)


if __name__ == "__main__":
		__UpperCamelCase      : Any					      =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
		)
		parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
		parser.add_argument(
		    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
		)
		__UpperCamelCase      : str					      =   parser.parse_args()
		convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







from itertools import combinations, product





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	a		       =							xa * xa + ya * ya
	a		       =							xa * xa + ya * ya
	a		       =							(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)





def 	__A	(			__lowerCamelCase = 50					)   -> int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1					)							,    repeat=2					)							,    2					)
	    if is_right(*__lowerCamelCase							,    *__lowerCamelCase					)					)


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

