



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Any				)  ->     Tuple:
 _snake_case    :  Tuple					=					AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  str					=					FlaxAutoModelForSeqaSeqLM.from_config(config=SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Dict					=					"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

 if config.model_type == "t5":
  _snake_case    :  Optional[int]					=					"""SelfAttention"""
 if config.model_type == "longt5" and config.encoder_attention_type == "local":
  _snake_case    :  int					=					"""LocalSelfAttention"""
 elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
  _snake_case    :  Tuple					=					"""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				):
  _snake_case    :  Any					=					F'''layers_{str(SCREAMING_SNAKE_CASE__				)}'''

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

  # Layer Normalization
  _snake_case    :  Dict					=					tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

  if split_mlp_wi:
   _snake_case    :  int					=					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
   _snake_case    :  Optional[int]					=					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  else:
   _snake_case    :  Dict					=					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

  _snake_case    :  Dict					=					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

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

  # Assigning
  _snake_case    :  Optional[int]					=					flax_model.params["""encoder"""]["""block"""][str(SCREAMING_SNAKE_CASE__				)]["""layer"""]
  _snake_case    :  Optional[Any]					=					tax_attention_key
  _snake_case    :  str					=					tax_attention_out
  _snake_case    :  Any					=					tax_attention_query
  _snake_case    :  Tuple					=					tax_attention_value

  _snake_case    :  List[Any]					=					tax_attention_layer_norm

  # Global input layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
   _snake_case    :  Optional[Any]					=					tax_global_layer_norm

  if split_mlp_wi:
   _snake_case    :  Optional[int]					=					tax_mlp_wi_a
   _snake_case    :  List[str]					=					tax_mlp_wi_a
  else:
   _snake_case    :  Optional[int]					=					tax_mlp_wi

  _snake_case    :  Union[str, Any]					=					tax_mlp_wo
  _snake_case    :  List[Any]					=					tax_mlp_layer_norm

  _snake_case    :  Union[str, Any]					=					flax_model_encoder_layer_block

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

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

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

 # Decoder
 for layer_index in range(config.num_layers				):
  _snake_case    :  Optional[int]					=					F'''layers_{str(SCREAMING_SNAKE_CASE__				)}'''

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

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

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

  # Layer Normalization
  _snake_case    :  List[Any]					=					tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

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

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

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

  # Assigning
  _snake_case    :  List[str]					=					flax_model.params["""decoder"""]["""block"""][str(SCREAMING_SNAKE_CASE__				)]["""layer"""]
  _snake_case    :  str					=					tax_attention_key
  _snake_case    :  Optional[Any]					=					tax_attention_out
  _snake_case    :  Optional[Any]					=					tax_attention_query
  _snake_case    :  List[Any]					=					tax_attention_value

  _snake_case    :  str					=					tax_pre_attention_layer_norm

  _snake_case    :  Dict					=					tax_enc_dec_attention_key
  _snake_case    :  Dict					=					tax_enc_dec_attention_out
  _snake_case    :  Optional[Any]					=					tax_enc_dec_attention_query
  _snake_case    :  Tuple					=					tax_enc_dec_attention_value

  _snake_case    :  List[str]					=					tax_cross_layer_norm

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

  _snake_case    :  Tuple					=					tax_mlp_wo

  _snake_case    :  List[Any]					=					txa_mlp_layer_norm

  _snake_case    :  List[str]					=					flax_model_decoder_layer_block

 # Decoder Normalization
 _snake_case    :  Tuple					=					tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
 _snake_case    :  Tuple					=					txa_decoder_norm

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

 # Token Embeddings
 _snake_case    :  Optional[Any]					=					tax_model["""target"""]["""token_embedder"""]["""embedding"""]
 _snake_case    :  int					=					txa_token_embeddings

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

 flax_model.save_pretrained(SCREAMING_SNAKE_CASE__				)
 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)





from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
  from .deepspeed import (
      DeepSpeedEngineWrapper,
      DeepSpeedOptimizerWrapper,
      DeepSpeedSchedulerWrapper,
      DummyOptim,
      DummyScheduler,
      HfDeepSpeedConfig,
  )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
