
'''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_											=						logging.get_logger(__name__)

a_											=						{
    "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    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""beit"""





 def __init__(    self ,       lowercase_=81_92 ,       lowercase_=7_68 ,       lowercase_=12 ,       lowercase_=12 ,       lowercase_=30_72 ,       lowercase_="gelu" ,       lowercase_=0.0 ,       lowercase_=0.0 ,       lowercase_=0.02 ,       lowercase_=1E-12 ,       lowercase_=2_24 ,       lowercase_=16 ,       lowercase_=3 ,       lowercase_=False ,       lowercase_=False ,       lowercase_=False ,       lowercase_=False ,       lowercase_=0.1 ,       lowercase_=0.1 ,       lowercase_=True ,       lowercase_=[3, 5, 7, 11] ,       lowercase_=[1, 2, 3, 6] ,       lowercase_=True ,       lowercase_=0.4 ,       lowercase_=2_56 ,       lowercase_=1 ,       lowercase_=False ,       lowercase_=2_55 ,       **lowercase_ ,       ):
     super().__init__(**lowercase_)

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

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






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						version.parse("""1.11"""					)





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





 @property
 def        snake_case__		(    self):
     return 1E-4






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_											=						{"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "WavLMForAudioFrameClassification",
      "WavLMForCTC",
      "WavLMForSequenceClassification",
      "WavLMForXVector",
      "WavLMModel",
      "WavLMPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  a_											=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




