


'''simple docstring'''






from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





'''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


lowercase							=logging.get_logger(__name__)

lowercase							={
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="levit"






       def __init__(      self  ,							snake_case=2_2_4  ,							snake_case=3  ,							snake_case=3  ,							snake_case=2  ,							snake_case=1  ,							snake_case=1_6  ,							snake_case=[1_2_8, 2_5_6, 3_8_4]  ,							snake_case=[4, 8, 1_2]  ,							snake_case=[4, 4, 4]  ,							snake_case=[1_6, 1_6, 1_6]  ,							snake_case=0  ,							snake_case=[2, 2, 2]  ,							snake_case=[2, 2, 2]  ,							snake_case=0.02  ,							**snake_case  ,							)						->  Any:




          '''simple docstring'''
          super().__init__(**snake_case)
          _UpperCAmelCase       :       List[str]							   =image_size
          _UpperCAmelCase       :       str							   =num_channels
          _UpperCAmelCase       :       int							   =kernel_size
          _UpperCAmelCase       :       Any							   =stride
          _UpperCAmelCase       :       Tuple							   =padding
          _UpperCAmelCase       :       Optional[int]							   =hidden_sizes
          _UpperCAmelCase       :       List[str]							   =num_attention_heads
          _UpperCAmelCase       :       List[Any]							   =depths
          _UpperCAmelCase       :       Any							   =key_dim
          _UpperCAmelCase       :       List[Any]							   =drop_path_rate
          _UpperCAmelCase       :       int							   =patch_size
          _UpperCAmelCase       :       Tuple							   =attention_ratio
          _UpperCAmelCase       :       Any							   =mlp_ratio
          _UpperCAmelCase       :       Optional[Any]							   =initializer_range
          _UpperCAmelCase       :       str							   =[
              ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=version.parse("1.11"    )






       @property
       def       lowerCAmelCase		(      self)						->  Mapping[str, Mapping[int, str]]:




          '''simple docstring'''
          return OrderedDict(
              [
                  ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
              ])






       @property
       def       lowerCAmelCase		(      self)						->  float:




          '''simple docstring'''
          return 1E-4

