

import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    List[str]:
      SCREAMING_SNAKE_CASE			:   Tuple	  = TaConfig.from_pretrained(_A      )
      SCREAMING_SNAKE_CASE			:   List[str]	  = FlaxTaForConditionalGeneration(config=_A      )
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = checkpoints.load_tax_checkpoint(_A      )

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

      # Encoder
      for layer_index in range(config.num_layers      ):
            SCREAMING_SNAKE_CASE			:   List[str]	  = F"layers_{str(_A      )}"

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

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

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE			:   List[str]	  = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                  SCREAMING_SNAKE_CASE			:   List[Any]	  = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
            else:
                  SCREAMING_SNAKE_CASE			:   Dict	  = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

            # Layer Normalization
            SCREAMING_SNAKE_CASE			:   List[Any]	  = tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

            # Assigning
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = tax_attention_key
            SCREAMING_SNAKE_CASE			:   Tuple	  = tax_attention_out
            SCREAMING_SNAKE_CASE			:   int	  = tax_attention_query
            SCREAMING_SNAKE_CASE			:   Tuple	  = tax_attention_value

            SCREAMING_SNAKE_CASE			:   str	  = tax_attention_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE			:   Tuple	  = tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE			:   Optional[int]	  = tax_mlp_wi

            SCREAMING_SNAKE_CASE			:   Optional[int]	  = tax_mlp_wo
            SCREAMING_SNAKE_CASE			:   List[str]	  = tax_mlp_layer_norm

      # Only for layer 0:
      SCREAMING_SNAKE_CASE			:   Any	  = tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = tax_encoder_rel_embedding

      # Assigning
      SCREAMING_SNAKE_CASE			:   Dict	  = tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
      SCREAMING_SNAKE_CASE			:   Dict	  = tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_decoder_layers      ):
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = F"layers_{str(_A      )}"

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

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

            # Encoder-Decoder-Attention
            SCREAMING_SNAKE_CASE			:   Tuple	  = tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
                """kernel"""
            ]
            SCREAMING_SNAKE_CASE			:   Any	  = tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
                """kernel"""
            ]
            SCREAMING_SNAKE_CASE			:   Dict	  = tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
                """kernel"""
            ]
            SCREAMING_SNAKE_CASE			:   Tuple	  = tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
                """kernel"""
            ]

            # Layer Normalization
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

            # MLP
            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE			:   List[str]	  = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                  SCREAMING_SNAKE_CASE			:   Optional[Any]	  = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
            else:
                  SCREAMING_SNAKE_CASE			:   Optional[Any]	  = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

            SCREAMING_SNAKE_CASE			:   Dict	  = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

            # Layer Normalization
            SCREAMING_SNAKE_CASE			:   str	  = tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

            # Assigning
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = tax_attention_key
            SCREAMING_SNAKE_CASE			:   Dict	  = tax_attention_out
            SCREAMING_SNAKE_CASE			:   Dict	  = tax_attention_query
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = tax_attention_value

            SCREAMING_SNAKE_CASE			:   str	  = tax_pre_attention_layer_norm

            SCREAMING_SNAKE_CASE			:   Optional[int]	  = tax_enc_dec_attention_key
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = tax_enc_dec_attention_out
            SCREAMING_SNAKE_CASE			:   Any	  = tax_enc_dec_attention_query
            SCREAMING_SNAKE_CASE			:   Any	  = tax_enc_dec_attention_value

            SCREAMING_SNAKE_CASE			:   Tuple	  = tax_cross_layer_norm

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

            SCREAMING_SNAKE_CASE			:   Optional[int]	  = tax_mlp_wo

            SCREAMING_SNAKE_CASE			:   List[str]	  = txa_mlp_layer_norm

      # Decoder Normalization
      SCREAMING_SNAKE_CASE			:   List[str]	  = tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
      SCREAMING_SNAKE_CASE			:   List[str]	  = txa_decoder_norm

      # Only for layer 0:
      SCREAMING_SNAKE_CASE			:   int	  = tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
      SCREAMING_SNAKE_CASE			:   Any	  = tax_decoder_rel_embedding

      # Token Embeddings
      SCREAMING_SNAKE_CASE			:   Dict	  = tax_model["""target"""]["""token_embedder"""]["""embedding"""]
      SCREAMING_SNAKE_CASE			:   Tuple	  = txa_token_embeddings

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

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


if __name__ == "__main__":
  UpperCAmelCase__			:  List[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."""
  )
  UpperCAmelCase__			:  Dict					     =     parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


lowerCAmelCase_:       List[str]			     =       logging.get_logger(__name__)






class  a__     ( _a						):
   snake_case_				=   "speech-encoder-decoder"
   snake_case_				=   True


   def __init__(  self,					**_UpperCAmelCase	):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase	)
        if "encoder" not in kwargs or "decoder" not in kwargs:
             raise ValueError(
                 F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                 F''' `decoder` sub-configurations are passed, but only {kwargs}'''	)

        lowercase__								=				kwargs.pop("encoder"	)
        lowercase__								=				encoder_config.pop("model_type"	)
        lowercase__								=				kwargs.pop("decoder"	)
        lowercase__								=				decoder_config.pop("model_type"	)

        lowercase__								=				AutoConfig.for_model(_UpperCAmelCase,					**_UpperCAmelCase	)
        lowercase__								=				AutoConfig.for_model(_UpperCAmelCase,					**_UpperCAmelCase	)
        lowercase__								=				True


   @classmethod
   def 						snake_case__						(  cls,					_UpperCAmelCase,					_UpperCAmelCase,					**_UpperCAmelCase	):
        '''simple docstring'''



        logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"	)
        lowercase__								=				True
        lowercase__								=				True

        return cls(encoder=encoder_config.to_dict(),					decoder=decoder_config.to_dict(),					**_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				copy.deepcopy(self.__dict__	)
        lowercase__								=				self.encoder.to_dict()
        lowercase__								=				self.decoder.to_dict()
        lowercase__								=				self.__class__.model_type
        return output




