



'''simple docstring'''





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





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

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

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

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

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

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

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

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

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

            snake_case__  :    str    						=      tax_attention_layer_norm

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

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

            snake_case__  :    Any    						=      tax_mlp_wo
            snake_case__  :    int    						=      tax_mlp_layer_norm

            snake_case__  :    List[Any]    						=      flax_model_encoder_layer_block

      # Only for layer 0:
      snake_case__  :    Tuple    						=      tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
      snake_case__  :    Any    						=      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__  :    Tuple    						=      tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
            snake_case__  :    int    						=      tax_encoder_global_rel_embedding

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

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

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

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

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

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

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

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

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

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

            snake_case__  :    List[str]    						=      tax_pre_attention_layer_norm

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

            snake_case__  :    str    						=      tax_cross_layer_norm

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

            snake_case__  :    Union[str, Any]    						=      tax_mlp_wo

            snake_case__  :    str    						=      txa_mlp_layer_norm

            snake_case__  :    str    						=      flax_model_decoder_layer_block

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

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

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

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

      flax_model.save_pretrained(_lowerCAmelCase		)
      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)





'''simple docstring'''






from __future__ import annotations


def  snake_case_     (					_lowerCAmelCase  :						list[int | float]   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int  )      ->   int | float:
							if len(_lowerCAmelCase  ) == 0:
														raise ValueError('''find_max() arg is an empty sequence'''  )
							if (
							    left >= len(_lowerCAmelCase  )
							    or left < -len(_lowerCAmelCase  )
							    or right >= len(_lowerCAmelCase  )
							    or right < -len(_lowerCAmelCase  )
							):
														raise IndexError('''list index out of range'''  )
							if left == right:
														return nums[left]
							UpperCAmelCase :       List[Any] 							=		(left + right) >> 1  # the middle
							UpperCAmelCase :       Optional[Any] 							=		find_max(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )  # find max in range[left, mid]
							UpperCAmelCase :       Dict 							=		find_max(_lowerCAmelCase   ,      mid + 1   ,      _lowerCAmelCase  )  # find max in range[mid + 1, right]

							return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





