




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

def 	lowerCAmelCase_	(     lowerCamelCase:				Optional[Any]   , lowerCamelCase:				List[Any]   , lowerCamelCase:				Any  )    ->	Optional[Any]:


      '''simple docstring'''




      lowerCamelCase__					     = TaConfig.from_pretrained(lowerCamelCase  )
      lowerCamelCase__					     = FlaxTaForConditionalGeneration(config=lowerCamelCase  )
      lowerCamelCase__					     = checkpoints.load_tax_checkpoint(lowerCamelCase  )

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

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

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

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

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

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

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

            # Assigning
            lowerCamelCase__					     = tax_attention_key
            lowerCamelCase__					     = tax_attention_out
            lowerCamelCase__					     = tax_attention_query
            lowerCamelCase__					     = tax_attention_value

            lowerCamelCase__					     = tax_attention_layer_norm

            if split_mlp_wi:
                  lowerCamelCase__					     = tax_mlp_wi_a
                  lowerCamelCase__					     = tax_mlp_wi_a
            else:
                  lowerCamelCase__					     = tax_mlp_wi

            lowerCamelCase__					     = tax_mlp_wo
            lowerCamelCase__					     = tax_mlp_layer_norm

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

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

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

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

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

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

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

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

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

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

            # Assigning
            lowerCamelCase__					     = tax_attention_key
            lowerCamelCase__					     = tax_attention_out
            lowerCamelCase__					     = tax_attention_query
            lowerCamelCase__					     = tax_attention_value

            lowerCamelCase__					     = tax_pre_attention_layer_norm

            lowerCamelCase__					     = tax_enc_dec_attention_key
            lowerCamelCase__					     = tax_enc_dec_attention_out
            lowerCamelCase__					     = tax_enc_dec_attention_query
            lowerCamelCase__					     = tax_enc_dec_attention_value

            lowerCamelCase__					     = tax_cross_layer_norm

            if split_mlp_wi:
                  lowerCamelCase__					     = tax_mlp_wi_a
                  lowerCamelCase__					     = tax_mlp_wi_a
            else:
                  lowerCamelCase__					     = tax_mlp_wi

            lowerCamelCase__					     = tax_mlp_wo

            lowerCamelCase__					     = txa_mlp_layer_norm

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

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

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

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

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


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





def 	lowerCAmelCase_	(     lowerCamelCase:				int  )    ->	int:


      '''simple docstring'''




      return n * (2 * n - 1)

def 	lowerCAmelCase_	(     lowerCamelCase:				int  )    ->	bool:


      '''simple docstring'''




      lowerCamelCase__					     = (1 + 24 * n) ** 0.5
      return ((1 + root) / 6) % 1 == 0

def 	lowerCAmelCase_	(     lowerCamelCase:				int = 1_44  )    ->	int:


      '''simple docstring'''




      lowerCamelCase__					     = start
      lowerCamelCase__					     = hexagonal_num(lowerCamelCase  )
      while not is_pentagonal(lowerCamelCase  ):
            n += 1
            lowerCamelCase__					     = hexagonal_num(lowerCamelCase  )
      return num


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