

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

"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''efficientnet'''




							def __init__( self    ,				snake_case_ = 3    ,				snake_case_ = 600    ,				snake_case_ = 2.0    ,				snake_case_ = 3.1    ,				snake_case_ = 8    ,				snake_case_ = [3, 3, 5, 3, 5, 5, 3]    ,				snake_case_ = [32, 16, 24, 40, 80, 112, 192]    ,				snake_case_ = [16, 24, 40, 80, 112, 192, 320]    ,				snake_case_ = []    ,				snake_case_ = [1, 2, 2, 2, 1, 2, 1]    ,				snake_case_ = [1, 2, 2, 3, 3, 4, 1]    ,				snake_case_ = [1, 6, 6, 6, 6, 6, 6]    ,				snake_case_ = 0.25    ,				snake_case_ = "swish"    ,				snake_case_ = 2_560    ,				snake_case_ = "mean"    ,				snake_case_ = 0.02    ,				snake_case_ = 0.001    ,				snake_case_ = 0.99    ,				snake_case_ = 0.5    ,				snake_case_ = 0.2    ,				**snake_case_    ,				)       ->  List[str]:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  width_coefficient
													__lowerCAmelCase     =  depth_coefficient
													__lowerCAmelCase     =  depth_divisor
													__lowerCAmelCase     =  kernel_sizes
													__lowerCAmelCase     =  in_channels
													__lowerCAmelCase     =  out_channels
													__lowerCAmelCase     =  depthwise_padding
													__lowerCAmelCase     =  strides
													__lowerCAmelCase     =  num_block_repeats
													__lowerCAmelCase     =  expand_ratios
													__lowerCAmelCase     =  squeeze_expansion_ratio
													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dim
													__lowerCAmelCase     =  pooling_type
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  batch_norm_eps
													__lowerCAmelCase     =  batch_norm_momentum
													__lowerCAmelCase     =  dropout_rate
													__lowerCAmelCase     =  drop_connect_rate
													__lowerCAmelCase     =  sum(snake_case_				) * 4
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		version.parse('''1.11''' )




							@property
							def    A__						( self				)       ->  Mapping[str, Mapping[int, str]]:
													return OrderedDict(
													    [
													        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
													    ]				)




							@property
							def    A__						( self				)       ->  float:
													return 1e-5
