



import math

import tensorflow as tf
from packaging import version






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	tf.convert_to_tensor(_UpperCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0     )		,	x.dtype     )     ))

 return x * cdf






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    int       =	tf.convert_to_tensor(_UpperCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tf.cast(math.pi		,	x.dtype     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	tf.cast(0.044715		,	x.dtype     )
 __SCREAMING_SNAKE_CASE       :    Any       =	0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi     ) * (x + coeff * tf.pow(_UpperCamelCase		,	3     ))     ))

 return x * cdf






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Tuple     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tf.convert_to_tensor(_UpperCamelCase     )

 return x * tf.tanh(tf.math.softplus(_UpperCamelCase     )     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Any     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	tf.convert_to_tensor(_UpperCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tf.cast(0.044715		,	x.dtype     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	tf.cast(0.7978845608		,	x.dtype     )

 return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)     ))






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	tf.convert_to_tensor(_UpperCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Dict       =	tf.cast(1.702		,	x.dtype     )
 return x * tf.math.sigmoid(coeff * x     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 return tf.clip_by_value(_gelu(_UpperCamelCase     )		,	-10		,	10     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     List[str]=-1     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tf.split(_UpperCamelCase		,	2		,	axis=_UpperCamelCase     )
 return a * tf.math.sigmoid(_UpperCamelCase     )


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):






      def 	_lowerCAmelCase						(				__lowerCamelCase   :     Any     ):



       """simple docstring"""





       return tf.keras.activations.gelu(_UpperCamelCase		,	approximate=_UpperCamelCase     )

      _lowerCamelCase						    =  tf.keras.activations.gelu






      _lowerCamelCase						    =  approximate_gelu_wrap
else:
      _lowerCamelCase						    =  _gelu
      _lowerCamelCase						    =  _gelu_new


_lowerCamelCase						    =  {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[str]     ):



 """simple docstring"""





 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()     )}"""     )





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






