





"""simple docstring"""






def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							bool:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       if len(SCREAMING_SNAKE_CASE				) == 1:
              return True
       try:
              _UpperCAmelCase			     =  series[1] / series[0]
              for index in range(len(SCREAMING_SNAKE_CASE				) - 1				):
                     if series[index + 1] / series[index] != common_ratio:
                            return False
       except ZeroDivisionError:
              return False
       return True

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							float:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       _UpperCAmelCase			     =  1
       for value in series:
              answer *= value
       return pow(SCREAMING_SNAKE_CASE,1 / len(SCREAMING_SNAKE_CASE				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()













"""simple docstring"""






from typing import Callable, Optional, Union

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


lowerCAmelCase_      =				logging.get_logger(__name__)

lowerCAmelCase_      =				{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	"""prophetnet"""
       lowerCAmelCase__    			=	["""past_key_values"""]
       lowerCAmelCase__    			=	{
           """num_attention_heads""": """num_encoder_attention_heads""",
       }






       def __init__( self     ,	a__ = 0.1     ,	a__ = "gelu"     ,	a__ = 3_05_22     ,	a__ = 10_24     ,	a__ = 40_96     ,	a__ = 12     ,	a__ = 16     ,	a__ = 40_96     ,	a__ = 12     ,	a__ = 16     ,	a__ = 0.1     ,	a__ = 0.1     ,	a__ = 5_12     ,	a__ = 0.02     ,	a__ = True     ,	a__ = True     ,	a__ = 0     ,	a__ = 2     ,	a__ = 32     ,	a__ = 1_28     ,	a__ = False     ,	a__ = 0.0     ,	a__ = True     ,	a__ = 0     ,	a__ = 1     ,	a__ = 2     ,	**a__     ,	):
              _UpperCAmelCase			     =  vocab_size
              _UpperCAmelCase			     =  hidden_size
              _UpperCAmelCase			     =  encoder_ffn_dim
              _UpperCAmelCase			     =  num_encoder_layers
              _UpperCAmelCase			     =  num_encoder_attention_heads
              _UpperCAmelCase			     =  decoder_ffn_dim
              _UpperCAmelCase			     =  num_decoder_layers
              _UpperCAmelCase			     =  num_decoder_attention_heads
              _UpperCAmelCase			     =  max_position_embeddings
              _UpperCAmelCase			     =  init_std  # Normal(0, this parameter)
              _UpperCAmelCase			     =  activation_function

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

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

              _UpperCAmelCase			     =  use_cache

              super().__init__(
                  pad_token_id=a__     ,	bos_token_id=a__     ,	eos_token_id=a__     ,	is_encoder_decoder=a__     ,	add_cross_attention=a__     ,	decoder_start_token_id=a__     ,	**a__     ,	)






       @property
       def 				__A			( self  ):
              return self.num_encoder_layers + self.num_decoder_layers






       @num_hidden_layers.setter
       def 				__A			( self     ,	a__  ):
              raise NotImplementedError(
                  'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
                  ' `num_decoder_layers`.'  )






