






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def    a_       (					_A					,      _A					,      _A     )	->	Optional[int]:







				"""simple docstring"""
				snake_case__													=   AutoConfig.from_pretrained(snake_case_     )
				snake_case__													=   FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case_     )
				snake_case__													=   checkpoints.load_tax_checkpoint(snake_case_     )

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

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

								# Self-Attention
								snake_case__													=   tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
								snake_case__													=   tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
								snake_case__													=   tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
								snake_case__													=   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":
												snake_case__													=   tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

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

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

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

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

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

								snake_case__													=   tax_attention_layer_norm

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

								if split_mlp_wi:
												snake_case__													=   tax_mlp_wi_a
												snake_case__													=   tax_mlp_wi_a
								else:
												snake_case__													=   tax_mlp_wi

								snake_case__													=   tax_mlp_wo
								snake_case__													=   tax_mlp_layer_norm

								snake_case__													=   flax_model_encoder_layer_block

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

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

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

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

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

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

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

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

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

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

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

								# Assigning
								snake_case__													=   flax_model.params['decoder']['block'][str(snake_case_     )]['layer']
								snake_case__													=   tax_attention_key
								snake_case__													=   tax_attention_out
								snake_case__													=   tax_attention_query
								snake_case__													=   tax_attention_value

								snake_case__													=   tax_pre_attention_layer_norm

								snake_case__													=   tax_enc_dec_attention_key
								snake_case__													=   tax_enc_dec_attention_out
								snake_case__													=   tax_enc_dec_attention_query
								snake_case__													=   tax_enc_dec_attention_value

								snake_case__													=   tax_cross_layer_norm

								if split_mlp_wi:
												snake_case__													=   tax_mlp_wi_a
												snake_case__													=   tax_mlp_wi_a
								else:
												snake_case__													=   tax_mlp_wi

								snake_case__													=   tax_mlp_wo

								snake_case__													=   txa_mlp_layer_norm

								snake_case__													=   flax_model_decoder_layer_block

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

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

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

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

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


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











def lowerCamelCase__    (							)      ->				int:
   return [
       a * b * (1000 - a - b)
       for a in range(1       ,      999       )
       for b in range(snake_case_       ,      999       )
       if (a * a + b * b == (1000 - a - b) ** 2)
   ][0]


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

