





'''simple docstring'''





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def   lowerCAmelCase_				( __a			,					__a			,					__a	)  ->	Any:







  """simple docstring"""





  lowerCamelCase__:   str    						=AutoConfig.from_pretrained(__a	)
  lowerCamelCase__:   Any    						=FlaxAutoModelForSeqaSeqLM.from_config(config=__a	)
  lowerCamelCase__:   Optional[Any]    						=checkpoints.load_tax_checkpoint(__a	)

  lowerCamelCase__:   str    						="wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

  if config.model_type == "t5":
    lowerCamelCase__:   Optional[int]    						="SelfAttention"
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    lowerCamelCase__:   List[Any]    						="LocalSelfAttention"
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    lowerCamelCase__:   str    						="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	):
    lowerCamelCase__:   Union[str, Any]    						=F"""layers_{str(__a	)}"""

    # Self-Attention
    lowerCamelCase__:   Union[str, Any]    						=tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
    lowerCamelCase__:   Union[str, Any]    						=tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
    lowerCamelCase__:   List[str]    						=tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
    lowerCamelCase__:   Tuple    						=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":
      lowerCamelCase__:   Any    						=tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

    # Layer Normalization
    lowerCamelCase__:   int    						=tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

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

    lowerCamelCase__:   Union[str, Any]    						=tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    lowerCamelCase__:   Union[str, Any]    						=tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    lowerCamelCase__:   int    						=flax_model.params["encoder"]["block"][str(__a	)]["layer"]
    lowerCamelCase__:   Union[str, Any]    						=tax_attention_key
    lowerCamelCase__:   str    						=tax_attention_out
    lowerCamelCase__:   Dict    						=tax_attention_query
    lowerCamelCase__:   Union[str, Any]    						=tax_attention_value

    lowerCamelCase__:   List[Any]    						=tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      lowerCamelCase__:   Dict    						=tax_global_layer_norm

    if split_mlp_wi:
      lowerCamelCase__:   Any    						=tax_mlp_wi_a
      lowerCamelCase__:   str    						=tax_mlp_wi_a
    else:
      lowerCamelCase__:   Dict    						=tax_mlp_wi

    lowerCamelCase__:   List[Any]    						=tax_mlp_wo
    lowerCamelCase__:   List[str]    						=tax_mlp_layer_norm

    lowerCamelCase__:   Tuple    						=flax_model_encoder_layer_block

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

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

  # Assigning
  lowerCamelCase__:   Union[str, Any]    						=tax_model["target"]["encoder"]["encoder_norm"]["scale"]
  lowerCamelCase__:   Optional[Any]    						=tax_encoder_norm

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

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

    # Layer Normalization
    lowerCamelCase__:   Optional[Any]    						=tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
        "scale"
    ]

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

    # Layer Normalization
    lowerCamelCase__:   Optional[Any]    						=tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

    lowerCamelCase__:   List[str]    						=tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    lowerCamelCase__:   int    						=tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    lowerCamelCase__:   Optional[int]    						=flax_model.params["decoder"]["block"][str(__a	)]["layer"]
    lowerCamelCase__:   List[Any]    						=tax_attention_key
    lowerCamelCase__:   Tuple    						=tax_attention_out
    lowerCamelCase__:   Dict    						=tax_attention_query
    lowerCamelCase__:   Tuple    						=tax_attention_value

    lowerCamelCase__:   Tuple    						=tax_pre_attention_layer_norm

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

    lowerCamelCase__:   Tuple    						=tax_cross_layer_norm

    if split_mlp_wi:
      lowerCamelCase__:   Union[str, Any]    						=tax_mlp_wi_a
      lowerCamelCase__:   int    						=tax_mlp_wi_a
    else:
      lowerCamelCase__:   int    						=tax_mlp_wi

    lowerCamelCase__:   Optional[Any]    						=tax_mlp_wo

    lowerCamelCase__:   List[Any]    						=txa_mlp_layer_norm

    lowerCamelCase__:   Union[str, Any]    						=flax_model_decoder_layer_block

  # Decoder Normalization
  lowerCamelCase__:   Any    						=tax_model["target"]["decoder"]["decoder_norm"]["scale"]
  lowerCamelCase__:   Dict    						=txa_decoder_norm

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

  # Token Embeddings
  lowerCamelCase__:   Union[str, Any]    						=tax_model["target"]["token_embedder"]["embedding"]
  lowerCamelCase__:   Dict    						=txa_token_embeddings

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

  flax_model.save_pretrained(__a	)
  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)








def   lowerCAmelCase_				( __a			,					__a	)  ->	float:







  """simple docstring"""





  if density <= 0:
    raise ValueError("Impossible fluid density"	)
  if bulk_modulus <= 0:
    raise ValueError("Impossible bulk modulus"	)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
     import doctest

     doctest.testmod()

