

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 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						"""switch_transformers"""
  _a		    =						["""past_key_values"""]
  _a		    =						{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




  def __init__(       self							,     lowerCAmelCase=32_128							,     lowerCAmelCase=768							,     lowerCAmelCase=64							,     lowerCAmelCase=2_048							,     lowerCAmelCase=64							,     lowerCAmelCase=12							,     lowerCAmelCase=3							,     lowerCAmelCase=12							,     lowerCAmelCase=3							,     lowerCAmelCase=12							,     lowerCAmelCase=8							,     lowerCAmelCase=False							,     lowerCAmelCase=0.01							,     lowerCAmelCase="float32"							,     lowerCAmelCase=False							,     lowerCAmelCase=32							,     lowerCAmelCase=128							,     lowerCAmelCase=0.1							,     lowerCAmelCase=1e-6							,     lowerCAmelCase=0.001							,     lowerCAmelCase=0.001							,     lowerCAmelCase=1.0							,     lowerCAmelCase="relu"							,     lowerCAmelCase=True							,     lowerCAmelCase=False							,     lowerCAmelCase=True							,     lowerCAmelCase=0							,     lowerCAmelCase=1							,     **lowerCAmelCase							,     )						->				int:
        '''simple docstring'''






        _lowercase			 =vocab_size
        _lowercase			 =d_model
        _lowercase			 =d_kv
        _lowercase			 =d_ff

        _lowercase			 =num_sparse_encoder_layers

        _lowercase			 =num_layers
        _lowercase			 =(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        _lowercase			 =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:
              _lowercase			 =self.num_layers // self.num_sparse_encoder_layers
        else:
              _lowercase			 =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:
              _lowercase			 =self.num_decoder_layers // self.num_sparse_decoder_layers
        else:
              _lowercase			 =self.num_decoder_layers  # HACK: this will create 0 sparse layers

        _lowercase			 =num_heads
        _lowercase			 =num_experts
        _lowercase			 =expert_capacity
        _lowercase			 =router_bias
        _lowercase			 =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}'''      )
        _lowercase			 =router_dtype

        _lowercase			 =router_ignore_padding_tokens
        _lowercase			 =relative_attention_num_buckets
        _lowercase			 =relative_attention_max_distance

        _lowercase			 =dropout_rate
        _lowercase			 =layer_norm_epsilon
        _lowercase			 =initializer_factor
        _lowercase			 =feed_forward_proj
        _lowercase			 =use_cache
        _lowercase			 =add_router_probs

        _lowercase			 =router_z_loss_coef
        _lowercase			 =router_aux_loss_coef

        _lowercase			 =self.feed_forward_proj.split('-'      )
        _lowercase			 =act_info[-1]
        _lowercase			 =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":
              _lowercase			 ='gelu_new'

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


lowercase_             =     {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     ['BeitFeatureExtractor']
  lowercase_             =     ['BeitImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BeitForImageClassification',
      'BeitForMaskedImageModeling',
      'BeitForSemanticSegmentation',
      'BeitModel',
      'BeitPreTrainedModel',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      'FlaxBeitForImageClassification',
      'FlaxBeitForMaskedImageModeling',
      'FlaxBeitModel',
      'FlaxBeitPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_beit import BeitFeatureExtractor
    from .image_processing_beit import BeitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_beit import (
        BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BeitForImageClassification,
        BeitForMaskedImageModeling,
        BeitForSemanticSegmentation,
        BeitModel,
        BeitPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_beit import (
        FlaxBeitForImageClassification,
        FlaxBeitForMaskedImageModeling,
        FlaxBeitModel,
        FlaxBeitPreTrainedModel,
    )


else:
  import sys

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


