

"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def   _A					(	_a	:						Any     ,   _a	:						Optional[Any]     ,   _a	:						Tuple				):



      """simple docstring"""






      A				  =		AutoConfig.from_pretrained(_a				)
      A				  =		FlaxAutoModelForSeqaSeqLM.from_config(config=_a				)
      A				  =		checkpoints.load_tax_checkpoint(_a				)

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

      if config.model_type == "t5":
            A				  =		"""SelfAttention"""
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            A				  =		"""LocalSelfAttention"""
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            A				  =		"""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				):
            A				  =		f'layers_{str(_a				)}'

            # 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"""]

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  A				  =		tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

            # 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				  =		flax_model.params["""encoder"""]["""block"""][str(_a				)]["""layer"""]
            A				  =		tax_attention_key
            A				  =		tax_attention_out
            A				  =		tax_attention_query
            A				  =		tax_attention_value

            A				  =		tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  A				  =		tax_global_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

            A				  =		flax_model_encoder_layer_block

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

      # Side/global relative position_bias + layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            A				  =		tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
            A				  =		tax_encoder_global_rel_embedding

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

      # Decoder
      for layer_index in range(config.num_layers				):
            A				  =		f'layers_{str(_a				)}'

            # 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"""]
            A				  =		tax_enc_dec_attention_module["""key"""]["""kernel"""]
            A				  =		tax_enc_dec_attention_module["""out"""]["""kernel"""]
            A				  =		tax_enc_dec_attention_module["""query"""]["""kernel"""]
            A				  =		tax_enc_dec_attention_module["""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				  =		flax_model.params["""decoder"""]["""block"""][str(_a				)]["""layer"""]
            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

            A				  =		flax_model_decoder_layer_block

      # 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 and LongT5 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
            A				  =		tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

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


if __name__ == "__main__":
  UpperCAmelCase													=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."
  )
  UpperCAmelCase													=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   _A					(	_a	:						list[float]     ,   _a	:						list[float]				):



      """simple docstring"""






      A				  =		sorted(numsa + numsa				)
      A  ,			A				  =		divmod(len(_a				)     ,   2				)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of first array: ").split()]
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of second array: ").split()]
  print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")




