





'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




def       _lowerCAmelCase  ( lowerCamelCase_	:     Union[str, Any]						,				lowerCamelCase_	:     str						,				lowerCamelCase_	:     Tuple ):
			__lowercase   =							TaConfig.from_pretrained(lowerCamelCase_ )
			__lowercase   =							FlaxTaForConditionalGeneration(config=lowerCamelCase_ )
			__lowercase   =							checkpoints.load_tax_checkpoint(lowerCamelCase_ )

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

			# Encoder
			for layer_index in range(config.num_layers ):
						__lowercase   =							f"layers_{str(lowerCamelCase_ )}"

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

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

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

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

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

						# Assigning
						__lowercase   =							tax_attention_key
						__lowercase   =							tax_attention_out
						__lowercase   =							tax_attention_query
						__lowercase   =							tax_attention_value

						__lowercase   =							tax_attention_layer_norm

						if split_mlp_wi:
									__lowercase   =							tax_mlp_wi_a
									__lowercase   =							tax_mlp_wi_a
						else:
									__lowercase   =							tax_mlp_wi

						__lowercase   =							tax_mlp_wo
						__lowercase   =							tax_mlp_layer_norm

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

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

			# Decoder
			for layer_index in range(config.num_decoder_layers ):
						__lowercase   =							f"layers_{str(lowerCamelCase_ )}"

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

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

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

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

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

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

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

						# Assigning
						__lowercase   =							tax_attention_key
						__lowercase   =							tax_attention_out
						__lowercase   =							tax_attention_query
						__lowercase   =							tax_attention_value

						__lowercase   =							tax_pre_attention_layer_norm

						__lowercase   =							tax_enc_dec_attention_key
						__lowercase   =							tax_enc_dec_attention_out
						__lowercase   =							tax_enc_dec_attention_query
						__lowercase   =							tax_enc_dec_attention_value

						__lowercase   =							tax_cross_layer_norm

						if split_mlp_wi:
									__lowercase   =							tax_mlp_wi_a
									__lowercase   =							tax_mlp_wi_a
						else:
									__lowercase   =							tax_mlp_wi

						__lowercase   =							tax_mlp_wo

						__lowercase   =							txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE    =			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.'''
	)
	_SCREAMING_SNAKE_CASE    =			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       _lowerCAmelCase  ( lowerCamelCase_	:     int ):

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5						,				int(math.sqrt(lowerCamelCase_ ) + 1 )						,				6 ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True




def       _lowerCAmelCase  ( lowerCamelCase_	:     int = 6_0_0_8_5_1_4_7_5_1_4_3 ):

			try:
						__lowercase   =							int(lowerCamelCase_ )
			except (TypeError, ValueError):
						raise TypeError('''Parameter n must be int or castable to int.''' )
			if n <= 0:
						raise ValueError('''Parameter n must be greater than or equal to one.''' )
			__lowercase   =							0
			if is_prime(lowerCamelCase_ ):
						return n
			while n % 2 == 0:
						n //= 2
			if is_prime(lowerCamelCase_ ):
						return n
			for i in range(3						,				int(math.sqrt(lowerCamelCase_ ) ) + 1						,				2 ):
						if n % i == 0:
									if is_prime(n // i ):
												__lowercase   =							n // i
												break
									elif is_prime(lowerCamelCase_ ):
												__lowercase   =							i
			return max_number


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





