

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def 				UpperCAmelCase_   (						_A      ,		_A      ,		_A				):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__    =    AutoConfig.from_pretrained(_A				)
     SCREAMING_SNAKE_CASE__    =    FlaxAutoModelForSeqaSeqLM.from_config(config=_A				)
     SCREAMING_SNAKE_CASE__    =    checkpoints.load_tax_checkpoint(_A				)

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

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

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

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

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

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

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

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

          SCREAMING_SNAKE_CASE__    =    tax_attention_layer_norm

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

          if split_mlp_wi:
               SCREAMING_SNAKE_CASE__    =    tax_mlp_wi_a
               SCREAMING_SNAKE_CASE__    =    tax_mlp_wi_a
          else:
               SCREAMING_SNAKE_CASE__    =    tax_mlp_wi

          SCREAMING_SNAKE_CASE__    =    tax_mlp_wo
          SCREAMING_SNAKE_CASE__    =    tax_mlp_layer_norm

          SCREAMING_SNAKE_CASE__    =    flax_model_encoder_layer_block

     # Only for layer 0:
     SCREAMING_SNAKE_CASE__    =    tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
     SCREAMING_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":
          SCREAMING_SNAKE_CASE__    =    tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
          SCREAMING_SNAKE_CASE__    =    tax_encoder_global_rel_embedding

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

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

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

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

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

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

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

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

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

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

          SCREAMING_SNAKE_CASE__    =    tax_pre_attention_layer_norm

          SCREAMING_SNAKE_CASE__    =    tax_enc_dec_attention_key
          SCREAMING_SNAKE_CASE__    =    tax_enc_dec_attention_out
          SCREAMING_SNAKE_CASE__    =    tax_enc_dec_attention_query
          SCREAMING_SNAKE_CASE__    =    tax_enc_dec_attention_value

          SCREAMING_SNAKE_CASE__    =    tax_cross_layer_norm

          if split_mlp_wi:
               SCREAMING_SNAKE_CASE__    =    tax_mlp_wi_a
               SCREAMING_SNAKE_CASE__    =    tax_mlp_wi_a
          else:
               SCREAMING_SNAKE_CASE__    =    tax_mlp_wi

          SCREAMING_SNAKE_CASE__    =    tax_mlp_wo

          SCREAMING_SNAKE_CASE__    =    txa_mlp_layer_norm

          SCREAMING_SNAKE_CASE__    =    flax_model_decoder_layer_block

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

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

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

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

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE     :  Dict   			=			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.'''
       )
       _SCREAMING_SNAKE_CASE     :  Optional[Any]   			=			parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      int						)	->	bool:
				_a								= n ** (1 / 3)
				return (val * val * val) == n


if __name__ == "__main__":
					print(perfect_cube(27))
					print(perfect_cube(4))
