
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


a_     =  logging.get_logger(__name__)

a_     =  {
    "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 				UpperCAmelCase_			(		snake_case__   ):
 UpperCAmelCase_						      =  """plbart"""
 UpperCAmelCase_						      =  ["""past_key_values"""]
 UpperCAmelCase_						      =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





 def __init__(      self	,    lowercase_=5_00_05	,    lowercase_=10_24	,    lowercase_=6	,    lowercase_=30_72	,    lowercase_=12	,    lowercase_=6	,    lowercase_=30_72	,    lowercase_=12	,    lowercase_=0.0	,    lowercase_=0.0	,    lowercase_=True	,    lowercase_=True	,    lowercase_="gelu"	,    lowercase_=7_68	,    lowercase_=0.1	,    lowercase_=0.1	,    lowercase_=0.0	,    lowercase_=0.02	,    lowercase_=0.0	,    lowercase_=True	,    lowercase_=1	,    lowercase_=0	,    lowercase_=2	,    lowercase_=2	,    **lowercase_	,    ):
     snake_case_	:      Optional[int]            =  vocab_size
     snake_case_	:      Tuple            =  max_position_embeddings
     snake_case_	:      List[Any]            =  d_model
     snake_case_	:      str            =  encoder_ffn_dim
     snake_case_	:      List[Any]            =  encoder_layers
     snake_case_	:      Union[str, Any]            =  encoder_attention_heads
     snake_case_	:      str            =  decoder_ffn_dim
     snake_case_	:      List[Any]            =  decoder_layers
     snake_case_	:      str            =  decoder_attention_heads
     snake_case_	:      List[str]            =  dropout
     snake_case_	:      List[str]            =  attention_dropout
     snake_case_	:      Union[str, Any]            =  activation_dropout
     snake_case_	:      int            =  activation_function
     snake_case_	:      List[Any]            =  init_std
     snake_case_	:      Dict            =  encoder_layerdrop
     snake_case_	:      List[Any]            =  decoder_layerdrop
     snake_case_	:      Tuple            =  classifier_dropout
     snake_case_	:      int            =  use_cache
     snake_case_	:      int            =  encoder_layers
     snake_case_	:      Optional[Any]            =  scale_embedding  # scale factor will be sqrt(d_model) if True
     super().__init__(
         pad_token_id=lowercase_	,    bos_token_id=lowercase_	,    eos_token_id=lowercase_	,    is_encoder_decoder=lowercase_	,    forced_eos_token_id=lowercase_	,    **lowercase_	,    )






class 				UpperCAmelCase_			(		snake_case__   ):





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





 @property
 def     snake_case__						(      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"}),
             ])






'''simple docstring'''
import math

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Tuple            =  math.log(2,						1_0    ) * number
    snake_case_	:      Dict            =  round((log_number - int(__SCREAMING_SNAKE_CASE    )),						1_5    )

    return difference

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE = 6_7_8_9_1_0    ):
    """simple docstring"""






    snake_case_	:      Any            =  9_0
    snake_case_	:      Optional[int]            =  0

    snake_case_	:      Union[str, Any]            =  math.log(1.23,						1_0    )
    snake_case_	:      Dict            =  math.log(1.24,						1_0    )
    snake_case_	:      List[Any]            =  0

    while position < number:
        snake_case_	:      Union[str, Any]            =  log_difference(__SCREAMING_SNAKE_CASE    )

        if difference >= upper_limit:
            power_iterator += 9_3

        elif difference < lower_limit:
            power_iterator += 1_9_6

        else:
            snake_case_	:      Optional[Any]            =  power_iterator
            power_iterator += 1_9_6
            position += 1

    return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(f"""{solution() = }""")




