






"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	int		,				UpperCamelCase     :	Any		,				UpperCamelCase     :	str    ):
  A__											=							TaConfig.from_pretrained(lowerCamelCase__    )
  A__											=							FlaxTaForConditionalGeneration(config=lowerCamelCase__    )
  A__											=							checkpoints.load_tax_checkpoint(lowerCamelCase__    )

  A__											=							"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

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

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

    # Layer Normalization
    A__											=							tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

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

    A__											=							tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    A__											=							tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    A__											=							tax_attention_key
    A__											=							tax_attention_out
    A__											=							tax_attention_query
    A__											=							tax_attention_value

    A__											=							tax_attention_layer_norm

    if split_mlp_wi:
      A__											=							tax_mlp_wi_a
      A__											=							tax_mlp_wi_a
    else:
      A__											=							tax_mlp_wi

    A__											=							tax_mlp_wo
    A__											=							tax_mlp_layer_norm

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

  # Assigning
  A__											=							tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  A__											=							tax_encoder_norm

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

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

    # Layer Normalization
    A__											=							tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
        """scale"""
    ]

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

    # Layer Normalization
    A__											=							tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

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

    A__											=							tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    A__											=							tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    A__											=							tax_attention_key
    A__											=							tax_attention_out
    A__											=							tax_attention_query
    A__											=							tax_attention_value

    A__											=							tax_pre_attention_layer_norm

    A__											=							tax_enc_dec_attention_key
    A__											=							tax_enc_dec_attention_out
    A__											=							tax_enc_dec_attention_query
    A__											=							tax_enc_dec_attention_value

    A__											=							tax_cross_layer_norm

    if split_mlp_wi:
      A__											=							tax_mlp_wi_a
      A__											=							tax_mlp_wi_a
    else:
      A__											=							tax_mlp_wi

    A__											=							tax_mlp_wo

    A__											=							txa_mlp_layer_norm

  # Decoder Normalization
  A__											=							tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  A__											=							txa_decoder_norm

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

  # Token Embeddings
  A__											=							tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  A__											=							txa_token_embeddings

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

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


if __name__ == "__main__":
       lowerCamelCase__			=   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."
       )
       lowerCamelCase__			=   parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












"""simple docstring"""




from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime







class 					_UpperCamelCase		( __snake_case):






    def __init__(self						,      *lowerCamelCase__						,      **lowerCamelCase__   ):


      """simple docstring"""
      super().__init__(*lowerCamelCase__						,      **lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							(
          next_prime(value % self.size_table   )
          if not is_prime(value % self.size_table   )
          else value % self.size_table
      )  # gt = bigger than
      return next_prime_gt - (data % next_prime_gt)






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      return (increment * self.__hash_function_a(lowerCamelCase__						,      lowerCamelCase__   )) % self.size_table






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=None   ):


      """simple docstring"""
      A__											=							1
      A__											=							self.hash_function(lowerCamelCase__   )

      while self.values[new_key] is not None and self.values[new_key] != key:
        A__											=							(
            self.__hash_double_function(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   )
            if self.balanced_factor() >= self.lim_charge
            else None
        )
        if new_key is None:
          break
        else:
          i += 1

      return new_key




