



'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase							       =			{
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''Swinv2ForImageClassification''',
      '''Swinv2ForMaskedImageModeling''',
      '''Swinv2Model''',
      '''Swinv2PreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swinva import (
        SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinvaForImageClassification,
        SwinvaForMaskedImageModeling,
        SwinvaModel,
        SwinvaPreTrainedModel,
    )


else:
  import sys

  lowercase							       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase							       =			logging.get_logger(__name__)

lowercase							       =			{
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: int  =      "switch_transformers"
 __magic_name__					: List[Any]  =      ["past_key_values"]
 __magic_name__					: Union[str, Any]  =      {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




 def __init__(   self							,  lowerCAmelCase_=3_2_1_2_8							,  lowerCAmelCase_=7_6_8							,  lowerCAmelCase_=6_4							,  lowerCAmelCase_=2_0_4_8							,  lowerCAmelCase_=6_4							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=3							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=3							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=8							,  lowerCAmelCase_=False							,  lowerCAmelCase_=0.0_1							,  lowerCAmelCase_="float32"							,  lowerCAmelCase_=False							,  lowerCAmelCase_=3_2							,  lowerCAmelCase_=1_2_8							,  lowerCAmelCase_=0.1							,  lowerCAmelCase_=1e-6							,  lowerCAmelCase_=0.0_0_1							,  lowerCAmelCase_=0.0_0_1							,  lowerCAmelCase_=1.0							,  lowerCAmelCase_="relu"							,  lowerCAmelCase_=True							,  lowerCAmelCase_=False							,  lowerCAmelCase_=True							,  lowerCAmelCase_=0							,  lowerCAmelCase_=1							,  **lowerCAmelCase_							,  ) ->      Optional[int]:



      """simple docstring"""







      a_       		=vocab_size
      a_       		=d_model
      a_       		=d_kv
      a_       		=d_ff

      a_       		=num_sparse_encoder_layers

      a_       		=num_layers
      a_       		=(
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      a_       		=num_sparse_decoder_layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_encoder_layers > 0:
           a_       		=self.num_layers // self.num_sparse_encoder_layers
      else:
           a_       		=self.num_layers  # HACK: this will create 0 sparse layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_decoder_layers > 0:
           a_       		=self.num_decoder_layers // self.num_sparse_decoder_layers
      else:
           a_       		=self.num_decoder_layers  # HACK: this will create 0 sparse layers

      a_       		=num_heads
      a_       		=num_experts
      a_       		=expert_capacity
      a_       		=router_bias
      a_       		=router_jitter_noise
      if router_dtype not in ["float32", "float16", "bfloat16"]:
           raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""")
      a_       		=router_dtype

      a_       		=router_ignore_padding_tokens
      a_       		=relative_attention_num_buckets
      a_       		=relative_attention_max_distance

      a_       		=dropout_rate
      a_       		=layer_norm_epsilon
      a_       		=initializer_factor
      a_       		=feed_forward_proj
      a_       		=use_cache
      a_       		=add_router_probs

      a_       		=router_z_loss_coef
      a_       		=router_aux_loss_coef

      a_       		=self.feed_forward_proj.split("-")
      a_       		=act_info[-1]
      a_       		=act_info[0] == "gated"

      if len(lowerCAmelCase_) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_) > 2:
           raise ValueError(
               f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'")

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
           a_       		="gelu_new"

      super().__init__(
          pad_token_id=lowerCAmelCase_							,  eos_token_id=lowerCAmelCase_							,  is_encoder_decoder=lowerCAmelCase_							,  **lowerCAmelCase_							,  )


