"""simple docstring"""




import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf
     from tensorflow.python.eager import context
     from tensorflow.python.framework import ops

     from transformers import GradientAccumulator, create_optimizer
@require_tf
class 						__A							(			unittest.TestCase		):






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       self.assertEqual(len(a__  )    ,					len(a__  )  )
       for a, b in zip(a__    ,					a__  ):
         self.assertAlmostEqual(a__    ,					a__    ,					delta=a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]  )]  )
       accumulator([tf.constant([-2.0, 1.0]  )]  )
       accumulator([tf.constant([-1.0, 2.0]  )]  )
       with self.assertRaises(a__  ):
         accumulator([tf.constant([1.0, 1.0]  ), tf.constant([2.0, 2.0]  )]  )
       self.assertEqual(accumulator.step    ,					3  )
       self.assertEqual(len(accumulator.gradients  )    ,					1  )
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,					[-2.0, 5.0]    ,					tol=1e-2  )
       accumulator.reset()
       self.assertEqual(accumulator.step    ,					0  )
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,					[0.0, 0.0]    ,					tol=1e-2  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					None
       ops.enable_eager_execution_internal()
       _lowerCAmelCase						:	List[str]        =					tf.config.list_physical_devices("""CPU"""  )
       if len(a__  ) == 1:
         tf.config.set_logical_device_configuration(
             physical_devices[0]    ,					[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]  )
       _lowerCAmelCase						:	Union[str, Any]        =					tf.config.list_logical_devices(device_type="""CPU"""  )
       _lowerCAmelCase						:	Union[str, Any]        =					tf.distribute.MirroredStrategy(devices=devices[:2]  )

       with strategy.scope():
         _lowerCAmelCase						:	List[str]        =					GradientAccumulator()
         _lowerCAmelCase						:	List[Any]        =					tf.Variable([4.0, 3.0]  )
         _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					create_optimizer(5e-5    ,					10    ,					5  )
         _lowerCAmelCase						:	Optional[Any]        =					tf.Variable([0.0, 0.0]    ,					trainable=a__  )

       def accumulate_on_replica(a__  ):
         accumulator([gradient]  )

       def apply_on_replica():
         optimizer.apply_gradients(list(zip(accumulator.gradients    ,					[variable]  )  )  )

       @tf.function
       def accumulate(a__    ,					a__  ):
         with strategy.scope():
           _lowerCAmelCase						:	List[Any]        =					strategy.experimental_local_results(a__  )
           local_variables[0].assign(a__  )
           local_variables[1].assign(a__  )
           strategy.run(a__    ,					args=(gradient_placeholder,)  )

       @tf.function
       def apply_grad():
         with strategy.scope():
           strategy.run(a__  )

       def _check_local_values(a__    ,					a__  ):
         _lowerCAmelCase						:	Dict        =					strategy.experimental_local_results(accumulator._gradients[0]  )
         self.assertListAlmostEqual(values[0].value()    ,					a__    ,					tol=1e-2  )
         self.assertListAlmostEqual(values[1].value()    ,					a__    ,					tol=1e-2  )

       accumulate([1.0, 2.0]    ,					[-1.0, 1.0]  )
       accumulate([3.0, -1.0]    ,					[-1.0, -1.0]  )
       accumulate([-2.0, 2.0]    ,					[3.0, -2.0]  )
       self.assertEqual(accumulator.step    ,					3  )
       _check_local_values([2.0, 3.0]    ,					[1.0, -2.0]  )
       apply_grad()
       self.assertListAlmostEqual(variable.value()    ,					[4.0, 3.0]    ,					tol=1e-2  )
       accumulator.reset()
       self.assertEqual(accumulator.step    ,					0  )
       _check_local_values([0.0, 0.0]    ,					[0.0, 0.0]  )





"""simple docstring"""




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




