
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def 				a						(							snake_case__:					Optional[int]						,   snake_case__:					Optional[int]						,   snake_case__:					Union[str, Any]					):


				'''simple docstring'''
				lowercase_       =					AutoConfig.from_pretrained(snake_case__					)
				lowercase_       =					FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case__					)
				lowercase_       =					checkpoints.load_tax_checkpoint(snake_case__					)

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

				if config.model_type == "t5":
								lowercase_       =					'''SelfAttention'''
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								lowercase_       =					'''LocalSelfAttention'''
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								lowercase_       =					'''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					):
								lowercase_       =					F'''layers_{str(snake_case__					)}'''

								# 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''']

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												lowercase_       =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

								# 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_       =					flax_model.params['''encoder''']['''block'''][str(snake_case__					)]['''layer''']
								lowercase_       =					tax_attention_key
								lowercase_       =					tax_attention_out
								lowercase_       =					tax_attention_query
								lowercase_       =					tax_attention_value

								lowercase_       =					tax_attention_layer_norm

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												lowercase_       =					tax_global_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

								lowercase_       =					flax_model_encoder_layer_block

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

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

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

				# Decoder
				for layer_index in range(config.num_layers					):
								lowercase_       =					F'''layers_{str(snake_case__					)}'''

								# 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''']
								lowercase_       =					tax_enc_dec_attention_module['''key''']['''kernel''']
								lowercase_       =					tax_enc_dec_attention_module['''out''']['''kernel''']
								lowercase_       =					tax_enc_dec_attention_module['''query''']['''kernel''']
								lowercase_       =					tax_enc_dec_attention_module['''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_       =					flax_model.params['''decoder''']['''block'''][str(snake_case__					)]['''layer''']
								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

								lowercase_       =					flax_model_decoder_layer_block

				# 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 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								lowercase_       =					tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
						__a  			=					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.'
						)
						__a  			=					parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






import itertools
import math




def 				a						(							snake_case__:					int					):


				'''simple docstring'''

				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(snake_case__					) + 1					)						,   6					):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					2
				while True:
								if is_prime(snake_case__					):
												yield num
								num += 1




def 				a						(							snake_case__:					int = 10_001					):


				'''simple docstring'''
				return next(itertools.islice(prime_generator()						,   nth - 1						,   snake_case__					)					)


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




