"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_a				:		int	   =       logging.get_logger(__name__)

_a				:		List[Any]	   =       {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[str]		 =		"beit"






     def __init__(		self    ,					a__=8192    ,					a__=768    ,					a__=12    ,					a__=12    ,					a__=3072    ,					a__="gelu"    ,					a__=0.0    ,					a__=0.0    ,					a__=0.0_2    ,					a__=1e-12    ,					a__=224    ,					a__=16    ,					a__=3    ,					a__=False    ,					a__=False    ,					a__=False    ,					a__=False    ,					a__=0.1    ,					a__=0.1    ,					a__=True    ,					a__=[3, 5, 7, 11]    ,					a__=[1, 2, 3, 6]    ,					a__=True    ,					a__=0.4    ,					a__=256    ,					a__=1    ,					a__=False    ,					a__=255    ,					**a__    ,					):
       super().__init__(**a__  )

       _lowerCAmelCase						:	Optional[int]        =					vocab_size
       _lowerCAmelCase						:	Union[str, Any]        =					hidden_size
       _lowerCAmelCase						:	Any        =					num_hidden_layers
       _lowerCAmelCase						:	List[str]        =					num_attention_heads
       _lowerCAmelCase						:	List[str]        =					intermediate_size
       _lowerCAmelCase						:	Dict        =					hidden_act
       _lowerCAmelCase						:	Optional[int]        =					hidden_dropout_prob
       _lowerCAmelCase						:	Any        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	str        =					initializer_range
       _lowerCAmelCase						:	Union[str, Any]        =					layer_norm_eps

       _lowerCAmelCase						:	Dict        =					image_size
       _lowerCAmelCase						:	str        =					patch_size
       _lowerCAmelCase						:	Optional[int]        =					num_channels
       _lowerCAmelCase						:	List[Any]        =					use_mask_token
       _lowerCAmelCase						:	Any        =					use_absolute_position_embeddings
       _lowerCAmelCase						:	List[Any]        =					use_relative_position_bias
       _lowerCAmelCase						:	Any        =					use_shared_relative_position_bias
       _lowerCAmelCase						:	List[str]        =					layer_scale_init_value
       _lowerCAmelCase						:	Any        =					drop_path_rate
       _lowerCAmelCase						:	Union[str, Any]        =					use_mean_pooling
       # decode head attributes (semantic segmentation)
       _lowerCAmelCase						:	Optional[Any]        =					out_indices
       _lowerCAmelCase						:	Optional[int]        =					pool_scales
       # auxiliary head attributes (semantic segmentation)
       _lowerCAmelCase						:	List[str]        =					use_auxiliary_head
       _lowerCAmelCase						:	Tuple        =					auxiliary_loss_weight
       _lowerCAmelCase						:	Union[str, Any]        =					auxiliary_channels
       _lowerCAmelCase						:	str        =					auxiliary_num_convs
       _lowerCAmelCase						:	List[Any]        =					auxiliary_concat_input
       _lowerCAmelCase						:	Any        =					semantic_loss_ignore_index







class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Dict		 =		version.parse("1.11"		)






     @property
     def    __A  (		self  ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]  )






     @property
     def    __A  (		self  ):
       return 1e-4





"""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




