


'''simple docstring'''






from typing import Callable, Optional, Union

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


a_       :     Optional[Any] 							=     logging.get_logger(__name__)

a_       :     Union[str, Any] 							=     {
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}




class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						"""prophetnet"""
 _lowerCAmelCase			       =						["""past_key_values"""]
 _lowerCAmelCase			       =						{
     """num_attention_heads""": """num_encoder_attention_heads""",
 }
 def __init__(			self   ,					__magic_name__ = 0.1   ,					__magic_name__ = "gelu"   ,					__magic_name__ = 3_05_22   ,					__magic_name__ = 10_24   ,					__magic_name__ = 40_96   ,					__magic_name__ = 12   ,					__magic_name__ = 16   ,					__magic_name__ = 40_96   ,					__magic_name__ = 12   ,					__magic_name__ = 16   ,					__magic_name__ = 0.1   ,					__magic_name__ = 0.1   ,					__magic_name__ = 5_12   ,					__magic_name__ = 0.0_2   ,					__magic_name__ = True   ,					__magic_name__ = True   ,					__magic_name__ = 0   ,					__magic_name__ = 2   ,					__magic_name__ = 32   ,					__magic_name__ = 1_28   ,					__magic_name__ = False   ,					__magic_name__ = 0.0   ,					__magic_name__ = True   ,					__magic_name__ = 0   ,					__magic_name__ = 1   ,					__magic_name__ = 2   ,					**__magic_name__   ,					)       ->				int:
      _a         =				vocab_size
      _a         =				hidden_size
      _a         =				encoder_ffn_dim
      _a         =				num_encoder_layers
      _a         =				num_encoder_attention_heads
      _a         =				decoder_ffn_dim
      _a         =				num_decoder_layers
      _a         =				num_decoder_attention_heads
      _a         =				max_position_embeddings
      _a         =				init_std  # Normal(0, this parameter)
      _a         =				activation_function

      # parameters for prophetnet
      _a         =				ngram
      _a         =				num_buckets
      _a         =				relative_max_distance
      _a         =				disable_ngram_loss
      _a         =				eps

      # 3 Types of Dropout
      _a         =				attention_dropout
      _a         =				activation_dropout
      _a         =				dropout

      _a         =				use_cache

      super().__init__(
          pad_token_id=__magic_name__   ,					bos_token_id=__magic_name__   ,					eos_token_id=__magic_name__   ,					is_encoder_decoder=__magic_name__   ,					add_cross_attention=__magic_name__   ,					decoder_start_token_id=__magic_name__   ,					**__magic_name__   ,					)
 @property
 def 				__UpperCAmelCase		(			self    )       ->				int:
      return self.num_encoder_layers + self.num_decoder_layers




 @num_hidden_layers.setter
 def 				__UpperCAmelCase		(			self   ,					__magic_name__    )       ->				Union[str, Any]:
      raise NotImplementedError(
          'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
          ' `num_decoder_layers`.'    )





'''simple docstring'''






from typing import TYPE_CHECKING

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


a_       :     Any 							=     {"configuration_bit": ["BIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BitConfig", "BitOnnxConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     int 							=     [
     "BIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BitForImageClassification",
     "BitModel",
     "BitPreTrainedModel",
     "BitBackbone",
 ]


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     Tuple 							=     ["BitImageProcessor"]


if TYPE_CHECKING:
 from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bit import (
      BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BitBackbone,
      BitForImageClassification,
      BitModel,
      BitPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bit import BitImageProcessor

else:
 import sys

 a_       :     Union[str, Any] 							=     _LazyModule(__name__, globals()["__file__"], _import_structure)

