



'''simple docstring'''






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						str  )      ->   List[Any]:
							UpperCAmelCase :       List[str] 							=		AutoConfig.from_pretrained(_lowerCAmelCase  )
							UpperCAmelCase :       Tuple 							=		FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCAmelCase  )
							UpperCAmelCase :       Union[str, Any] 							=		checkpoints.load_tax_checkpoint(_lowerCAmelCase  )

							UpperCAmelCase :       Dict 							=		'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

							if config.model_type == "t5":
														UpperCAmelCase :       str 							=		'''SelfAttention'''
							if config.model_type == "longt5" and config.encoder_attention_type == "local":
														UpperCAmelCase :       List[Any] 							=		'''LocalSelfAttention'''
							elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														UpperCAmelCase :       Optional[Any] 							=		'''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  ):
														UpperCAmelCase :       Dict 							=		f"""layers_{str(_lowerCAmelCase  )}"""

														# Self-Attention
														UpperCAmelCase :       Any 							=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
														UpperCAmelCase :       Optional[Any] 							=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
														UpperCAmelCase :       List[str] 							=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
														UpperCAmelCase :       Dict 							=		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":
																					UpperCAmelCase :       List[Any] 							=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

														# Layer Normalization
														UpperCAmelCase :       Optional[int] 							=		tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

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

														# Layer Normalization
														UpperCAmelCase :       Tuple 							=		tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

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

														UpperCAmelCase :       str 							=		tax_attention_layer_norm

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					UpperCAmelCase :       List[Any] 							=		tax_global_layer_norm

														if split_mlp_wi:
																					UpperCAmelCase :       str 							=		tax_mlp_wi_a
																					UpperCAmelCase :       int 							=		tax_mlp_wi_a
														else:
																					UpperCAmelCase :       List[str] 							=		tax_mlp_wi

														UpperCAmelCase :       Any 							=		tax_mlp_wo
														UpperCAmelCase :       int 							=		tax_mlp_layer_norm

														UpperCAmelCase :       List[Any] 							=		flax_model_encoder_layer_block

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

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

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

							# Decoder
							for layer_index in range(config.num_layers  ):
														UpperCAmelCase :       str 							=		f"""layers_{str(_lowerCAmelCase  )}"""

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

														# Layer Normalization
														UpperCAmelCase :       Dict 							=		tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
														    '''scale'''
														]

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

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

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

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

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

														# Assigning
														UpperCAmelCase :       List[Any] 							=		flax_model.params['''decoder''']['''block'''][str(_lowerCAmelCase  )]['''layer''']
														UpperCAmelCase :       Optional[int] 							=		tax_attention_key
														UpperCAmelCase :       Optional[int] 							=		tax_attention_out
														UpperCAmelCase :       Union[str, Any] 							=		tax_attention_query
														UpperCAmelCase :       List[str] 							=		tax_attention_value

														UpperCAmelCase :       List[str] 							=		tax_pre_attention_layer_norm

														UpperCAmelCase :       List[str] 							=		tax_enc_dec_attention_key
														UpperCAmelCase :       int 							=		tax_enc_dec_attention_out
														UpperCAmelCase :       Dict 							=		tax_enc_dec_attention_query
														UpperCAmelCase :       List[str] 							=		tax_enc_dec_attention_value

														UpperCAmelCase :       str 							=		tax_cross_layer_norm

														if split_mlp_wi:
																					UpperCAmelCase :       Tuple 							=		tax_mlp_wi_a
																					UpperCAmelCase :       int 							=		tax_mlp_wi_a
														else:
																					UpperCAmelCase :       List[str] 							=		tax_mlp_wi

														UpperCAmelCase :       Union[str, Any] 							=		tax_mlp_wo

														UpperCAmelCase :       str 							=		txa_mlp_layer_norm

														UpperCAmelCase :       str 							=		flax_model_decoder_layer_block

							# Decoder Normalization
							UpperCAmelCase :       List[Any] 							=		tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
							UpperCAmelCase :       Any 							=		txa_decoder_norm

							# Only for layer 0:
							UpperCAmelCase :       str 							=		tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
							UpperCAmelCase :       Optional[int] 							=		tax_decoder_rel_embedding

							# Token Embeddings
							UpperCAmelCase :       Union[str, Any] 							=		tax_model['''target''']['''token_embedder''']['''embedding''']
							UpperCAmelCase :       str 							=		txa_token_embeddings

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

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


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










'''simple docstring'''






import random

from .binary_exp_mod import bin_exp_mod


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Optional[Any]=1000  )      ->   int:
							if n < 2:
														return False

							if n % 2 == 0:
														return n == 2

							# this means n is odd
							UpperCAmelCase :       str 							=		n - 1
							UpperCAmelCase :       List[Any] 							=		0
							while d % 2 == 0:
														d /= 2
														exp += 1

							# n - 1=d*(2**exp)
							UpperCAmelCase :       List[str] 							=		0
							while count < prec:
														UpperCAmelCase :       int 							=		random.randint(2   ,      n - 1  )
														UpperCAmelCase :       List[str] 							=		bin_exp_mod(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														if b != 1:
																					UpperCAmelCase :       int 							=		True
																					for _ in range(_lowerCAmelCase  ):
																												if b == n - 1:
																																			UpperCAmelCase :       Dict 							=		False
																																			break
																												UpperCAmelCase :       str 							=		b * b
																												b %= n
																					if flag:
																												return False
																					count += 1
							return True


if __name__ == "__main__":
						UpperCamelCase__:   Optional[int]								=			abs(int(input("Enter bound : ").strip()))
						print("Here's the list of primes:")
						print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))





