

'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a				: Optional[int] 							=						{'configuration_cvt': ['CVT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CvtConfig']}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a				: Optional[Any] 							=						[
      'CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'CvtForImageClassification',
      'CvtModel',
      'CvtPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a				: Tuple 							=						[
      'TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFCvtForImageClassification',
      'TFCvtModel',
      'TFCvtPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_cvt import (
        CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
        CvtForImageClassification,
        CvtModel,
        CvtPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_cvt import (
        TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCvtForImageClassification,
        TFCvtModel,
        TFCvtPreTrainedModel,
    )


else:
  import sys

  _a				: Any 							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


_a				: Optional[int] 							=						logging.get_logger(__name__)

_a				: Dict 							=						{
    'mnaylor/mega-base-wikitext': 'https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json',
}



class        lowercase_	(		a ):

  '''simple docstring'''





  __lowerCAmelCase					: List[str]      =   "mega"
  def __init__(     self   , a_=3_0_5_2_2   , a_=1_2_8   , a_=4   , a_=2_5_6   , a_=1_6   , a_=True   , a_=6_4   , a_=False   , a_=-1   , a_=None   , a_=True   , a_="scalenorm"   , a_=True   , a_="silu"   , a_="softmax"   , a_=0.1   , a_=0.1   , a_=0.1   , a_=False   , a_=True   , a_=2_5_6   , a_=True   , a_=0.1   , a_=2_0_4_8   , a_=False   , a_=2   , a_=0.02   , a_=0.2   , a_=0.02   , a_=1.0   , a_=1   , a_=0   , a_=2   , a_="rotary"   , a_=None   , a_=True   , a_=True   , **a_   , )							->				Any:


    """simple docstring"""
    super().__init__(pad_token_id=a_   , bos_token_id=a_   , eos_token_id=a_   , **a_     )

    UpperCAmelCase													=	vocab_size
    UpperCAmelCase													=	hidden_size
    UpperCAmelCase													=	num_hidden_layers
    UpperCAmelCase													=	activation
    UpperCAmelCase													=	attention_activation
    UpperCAmelCase													=	intermediate_size
    UpperCAmelCase													=	ema_projection_size
    UpperCAmelCase													=	bidirectional
    UpperCAmelCase													=	shared_representation_size
    UpperCAmelCase													=	use_chunking
    UpperCAmelCase													=	chunk_size
    UpperCAmelCase													=	truncation
    UpperCAmelCase													=	normalize_before_mega
    UpperCAmelCase													=	normalization_type
    UpperCAmelCase													=	norm_affine
    UpperCAmelCase													=	dropout_prob
    UpperCAmelCase													=	hidden_dropout_prob
    UpperCAmelCase													=	attention_probs_dropout_prob
    UpperCAmelCase													=	use_feature_dropout
    UpperCAmelCase													=	use_normalized_ffn
    UpperCAmelCase													=	nffn_hidden_size
    UpperCAmelCase													=	normalize_before_ffn
    UpperCAmelCase													=	nffn_activation_dropout_prob
    UpperCAmelCase													=	max_positions
    UpperCAmelCase													=	add_token_type_embeddings
    UpperCAmelCase													=	type_vocab_size
    UpperCAmelCase													=	initializer_range
    UpperCAmelCase													=	ema_delta_alpha_range
    UpperCAmelCase													=	ema_beta_range
    UpperCAmelCase													=	ema_gamma_omega_range
    UpperCAmelCase													=	relative_positional_bias
    UpperCAmelCase													=	use_cache
    UpperCAmelCase													=	classifier_dropout
    UpperCAmelCase													=	add_lm_hidden_dense_layer
    UpperCAmelCase													=	1  # not used but required by Hugging Face




class        lowercase_	(		a ):

  '''simple docstring'''





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


    """simple docstring"""
    if self.task == "multiple-choice":
      UpperCAmelCase													=	{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      UpperCAmelCase													=	{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
        ]     )


