

from collections import OrderedDict
from typing import Mapping

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


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

UpperCamelCase__					: str								=      {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		List[Any]  	=					'plbart'
  __lowerCAmelCase       :		Any  	=					['past_key_values']
  __lowerCAmelCase       :		Any  	=					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

  def __init__(   self  ,						_A=50005  ,						_A=1024  ,						_A=6  ,						_A=3072  ,						_A=12  ,						_A=6  ,						_A=3072  ,						_A=12  ,						_A=0.0  ,						_A=0.0  ,						_A=True  ,						_A=True  ,						_A="gelu"  ,						_A=768  ,						_A=0.1  ,						_A=0.1  ,						_A=0.0  ,						_A=0.0_2  ,						_A=0.0  ,						_A=True  ,						_A=1  ,						_A=0  ,						_A=2  ,						_A=2  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  d_model
      SCREAMING_SNAKE_CASE_													=  encoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  encoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  decoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  decoder_layers
      SCREAMING_SNAKE_CASE_													=  decoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  dropout
      SCREAMING_SNAKE_CASE_													=  attention_dropout
      SCREAMING_SNAKE_CASE_													=  activation_dropout
      SCREAMING_SNAKE_CASE_													=  activation_function
      SCREAMING_SNAKE_CASE_													=  init_std
      SCREAMING_SNAKE_CASE_													=  encoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  decoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  classifier_dropout
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=_A  ,						bos_token_id=_A  ,						eos_token_id=_A  ,						is_encoder_decoder=_A  ,						forced_eos_token_id=_A  ,						**_A  ,						)






class  __snake_case					(      lowerCAmelCase__    ):

  @property
  def   lowerCAmelCase__       (   self):
      return OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'sequence'}),
              ('attention_mask', {0: 'batch', 1: 'sequence'}),
          ])


  @property
  def   lowerCAmelCase__       (   self):






      if self.use_past:
          return OrderedDict(
              [
                  ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                  ('past_keys', {0: 'batch', 2: 'sequence'}),
                  ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
              ])
      else:
          return OrderedDict(
              [
                  ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                  ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
              ])





def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    if power < 0:
        return -1
    base %= mod
    SCREAMING_SNAKE_CASE_													=  1

    while power > 0:
        if power & 1:
            SCREAMING_SNAKE_CASE_													=  (result * base) % mod
        SCREAMING_SNAKE_CASE_													=  power >> 1
        SCREAMING_SNAKE_CASE_													=  (base * base) % mod

    return result


def    _UpperCAmelCase    (					):





    """simple docstring"""


    print(modular_exponential(3	,				200	,				13    )    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       main()


