"""simple docstring"""


import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__UpperCamelCase									=			logging.get_logger(__name__)




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "linear"
       SCREAMING_SNAKE_CASE_			=      "cosine"
       SCREAMING_SNAKE_CASE_			=      "cosine_with_restarts"
       SCREAMING_SNAKE_CASE_			=      "polynomial"
       SCREAMING_SNAKE_CASE_			=      "constant"
       SCREAMING_SNAKE_CASE_			=      "constant_with_warmup"
       SCREAMING_SNAKE_CASE_			=      "piecewise_constant"







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase = -1					)				->      Optional[Any]:
     return LambdaLR(UpperCAmelCase						,					lambda UpperCAmelCase					: 1						,					last_epoch=UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase = -1					)				->      Optional[Any]:

     def lr_lambda(UpperCAmelCase					):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase					) / float(max(1.0						,					UpperCAmelCase					)					)
          return 1.0

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					last_epoch=UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase = -1					)				->      List[Any]:
     snake_case_					       =					{}
     snake_case_					       =					step_rules.split(','					)
     for rule_str in rule_list[:-1]:
          snake_case_       ,	snake_case_					       =					rule_str.split(':'					)
          snake_case_					       =					int(UpperCAmelCase					)
          snake_case_					       =					float(UpperCAmelCase					)
          snake_case_					       =					value
     snake_case_					       =					float(rule_list[-1]					)

     def create_rules_function(UpperCAmelCase						,					UpperCAmelCase					):
          def rule_func(UpperCAmelCase					) -> float:
               snake_case_					       =					sorted(rules_dict.keys()					)
               for i, sorted_step in enumerate(UpperCAmelCase					):
                    if steps < sorted_step:
                         return rules_dict[sorted_steps[i]]
               return last_lr_multiple

          return rule_func

     snake_case_					       =					create_rules_function(UpperCAmelCase						,					UpperCAmelCase					)

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					last_epoch=UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=-1					)				->      Optional[int]:

     def lr_lambda(UpperCAmelCase					):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase					) / float(max(1						,					UpperCAmelCase					)					)
          return max(
              0.0						,					float(num_training_steps - current_step					) / float(max(1						,					num_training_steps - num_warmup_steps					)					)					)

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase = 0.5						,					UpperCAmelCase = -1					)				->      Optional[Any]:

     def lr_lambda(UpperCAmelCase					):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase					) / float(max(1						,					UpperCAmelCase					)					)
          snake_case_					       =					float(current_step - num_warmup_steps					) / float(max(1						,					num_training_steps - num_warmup_steps					)					)
          return max(0.0						,					0.5 * (1.0 + math.cos(math.pi * float(UpperCAmelCase					) * 2.0 * progress					))					)

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase = 1						,					UpperCAmelCase = -1					)				->      Optional[int]:

     def lr_lambda(UpperCAmelCase					):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase					) / float(max(1						,					UpperCAmelCase					)					)
          snake_case_					       =					float(current_step - num_warmup_steps					) / float(max(1						,					num_training_steps - num_warmup_steps					)					)
          if progress >= 1.0:
               return 0.0
          return max(0.0						,					0.5 * (1.0 + math.cos(math.pi * ((float(UpperCAmelCase					) * progress) % 1.0)					))					)

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=1e-7						,					UpperCAmelCase=1.0						,					UpperCAmelCase=-1					)				->      List[str]:
     snake_case_					       =					optimizer.defaults['lr']
     if not (lr_init > lr_end):
          raise ValueError(f'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'					)

     def lr_lambda(UpperCAmelCase					):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase					) / float(max(1						,					UpperCAmelCase					)					)
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               snake_case_					       =					lr_init - lr_end
               snake_case_					       =					num_training_steps - num_warmup_steps
               snake_case_					       =					1 - (current_step - num_warmup_steps) / decay_steps
               snake_case_					       =					lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)


__UpperCamelCase									=			{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase = None						,					UpperCAmelCase = None						,					UpperCAmelCase = None						,					UpperCAmelCase = 1						,					UpperCAmelCase = 1.0						,					UpperCAmelCase = -1						,					)				->      Union[str, Any]:
     snake_case_					       =					SchedulerType(UpperCAmelCase					)
     snake_case_					       =					TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(UpperCAmelCase						,					last_epoch=UpperCAmelCase					)

     if name == SchedulerType.PIECEWISE_CONSTANT:
          return schedule_func(UpperCAmelCase						,					step_rules=UpperCAmelCase						,					last_epoch=UpperCAmelCase					)

     # All other schedulers require `num_warmup_steps`
     if num_warmup_steps is None:
          raise ValueError(f'{name} requires `num_warmup_steps`, please provide that argument.'					)

     if name == SchedulerType.CONSTANT_WITH_WARMUP:
          return schedule_func(UpperCAmelCase						,					num_warmup_steps=UpperCAmelCase						,					last_epoch=UpperCAmelCase					)

     # All other schedulers require `num_training_steps`
     if num_training_steps is None:
          raise ValueError(f'{name} requires `num_training_steps`, please provide that argument.'					)

     if name == SchedulerType.COSINE_WITH_RESTARTS:
          return schedule_func(
              UpperCAmelCase						,					num_warmup_steps=UpperCAmelCase						,					num_training_steps=UpperCAmelCase						,					num_cycles=UpperCAmelCase						,					last_epoch=UpperCAmelCase						,					)

     if name == SchedulerType.POLYNOMIAL:
          return schedule_func(
              UpperCAmelCase						,					num_warmup_steps=UpperCAmelCase						,					num_training_steps=UpperCAmelCase						,					power=UpperCAmelCase						,					last_epoch=UpperCAmelCase						,					)

     return schedule_func(
         UpperCAmelCase						,					num_warmup_steps=UpperCAmelCase						,					num_training_steps=UpperCAmelCase						,					last_epoch=UpperCAmelCase					)


"""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
