

"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp




def 				a__	(	)  ->   List[Any]:
      UpperCAmelCase__ :       List[str]						=						ArgumentParser(
          description=(
              """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
          )						)

      # Optional arguments for the launch helper
      parser.add_argument("""--num_cores""" ,  type=lowerCAmelCase ,  default=1 ,  help="""Number of TPU cores to use (1 or 8)."""						)

      # positional
      parser.add_argument(
          """training_script""" ,  type=lowerCAmelCase ,  help=(
              """The full path to the single TPU training """
              """program/script to be launched in parallel, """
              """followed by all the arguments for the """
              """training script"""
          ) ,  )

      # rest from the training program
      parser.add_argument("""training_script_args""" ,  nargs=lowerCAmelCase						)

      return parser.parse_args()




def 				a__	(	)  ->   List[str]:
      UpperCAmelCase__ :       List[Any]						=						parse_args()

      # Import training_script as a module.
      UpperCAmelCase__ :       Optional[int]						=						Path(args.training_script						)
      sys.path.append(str(script_fpath.parent.resolve()						)						)
      UpperCAmelCase__ :       int						=						script_fpath.stem
      UpperCAmelCase__ :       int						=						importlib.import_module(lowerCAmelCase						)

      # Patch sys.argv
      UpperCAmelCase__ :       Any						=						[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores						)]

      xmp.spawn(mod._mp_fn ,  args=() ,  nprocs=args.num_cores						)


if __name__ == "__main__":
    main()




"""simple docstring"""
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
      UpperCAmelCase__ :       int						=						TaConfig.from_pretrained(lowerCAmelCase						)
      UpperCAmelCase__ :       Optional[int]						=						FlaxTaForConditionalGeneration(config=lowerCAmelCase						)
      UpperCAmelCase__ :       int						=						checkpoints.load_tax_checkpoint(lowerCAmelCase						)

      UpperCAmelCase__ :       Optional[Any]						=						"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

      # Encoder
      for layer_index in range(config.num_layers						):
            UpperCAmelCase__ :       Union[str, Any]						=						F"""layers_{str(lowerCAmelCase						)}"""

            # Self-Attention
            UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
            UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
            UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
            UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

            # Layer Normalization
            UpperCAmelCase__ :       str						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

            if split_mlp_wi:
                  UpperCAmelCase__ :       int						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                  UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
            else:
                  UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

            UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

            # Layer Normalization
            UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

            # Assigning
            UpperCAmelCase__ :       str						=						tax_attention_key
            UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_out
            UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_query
            UpperCAmelCase__ :       List[Any]						=						tax_attention_value

            UpperCAmelCase__ :       Any						=						tax_attention_layer_norm

            if split_mlp_wi:
                  UpperCAmelCase__ :       Any						=						tax_mlp_wi_a
                  UpperCAmelCase__ :       List[Any]						=						tax_mlp_wi_a
            else:
                  UpperCAmelCase__ :       Union[str, Any]						=						tax_mlp_wi

            UpperCAmelCase__ :       int						=						tax_mlp_wo
            UpperCAmelCase__ :       List[Any]						=						tax_mlp_layer_norm

      # Only for layer 0:
      UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
      UpperCAmelCase__ :       str						=						tax_encoder_rel_embedding

      # Assigning
      UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
      UpperCAmelCase__ :       Optional[Any]						=						tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_decoder_layers						):
            UpperCAmelCase__ :       str						=						F"""layers_{str(lowerCAmelCase						)}"""

            # Self-Attention
            UpperCAmelCase__ :       List[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
            UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
            UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
            UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

            # Layer Normalization
            UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
                """scale"""
            ]

            # Encoder-Decoder-Attention
            UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
                """kernel"""
            ]
            UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
                """kernel"""
            ]
            UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
                """kernel"""
            ]
            UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
                """kernel"""
            ]

            # Layer Normalization
            UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

            # MLP
            if split_mlp_wi:
                  UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                  UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
            else:
                  UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

            UpperCAmelCase__ :       Optional[int]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

            # Layer Normalization
            UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

            # Assigning
            UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_key
            UpperCAmelCase__ :       str						=						tax_attention_out
            UpperCAmelCase__ :       Tuple						=						tax_attention_query
            UpperCAmelCase__ :       int						=						tax_attention_value

            UpperCAmelCase__ :       List[Any]						=						tax_pre_attention_layer_norm

            UpperCAmelCase__ :       Union[str, Any]						=						tax_enc_dec_attention_key
            UpperCAmelCase__ :       Optional[int]						=						tax_enc_dec_attention_out
            UpperCAmelCase__ :       List[Any]						=						tax_enc_dec_attention_query
            UpperCAmelCase__ :       Any						=						tax_enc_dec_attention_value

            UpperCAmelCase__ :       List[Any]						=						tax_cross_layer_norm

            if split_mlp_wi:
                  UpperCAmelCase__ :       Tuple						=						tax_mlp_wi_a
                  UpperCAmelCase__ :       Dict						=						tax_mlp_wi_a
            else:
                  UpperCAmelCase__ :       str						=						tax_mlp_wi

            UpperCAmelCase__ :       Tuple						=						tax_mlp_wo

            UpperCAmelCase__ :       Any						=						txa_mlp_layer_norm

      # Decoder Normalization
      UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
      UpperCAmelCase__ :       Union[str, Any]						=						txa_decoder_norm

      # Only for layer 0:
      UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
      UpperCAmelCase__ :       Optional[int]						=						tax_decoder_rel_embedding

      # Token Embeddings
      UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""token_embedder"""]["""embedding"""]
      UpperCAmelCase__ :       int						=						txa_token_embeddings

      # LM Head (only in v1.1 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
            UpperCAmelCase__ :       str						=						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 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."""
    )
    _A						=     parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)

