


from typing import Callable, Optional, Union

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


UpperCamelCase						:				int             =      logging.get_logger(__name__)

UpperCamelCase						:				Union[str, Any]             =      {
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}



class 			lowerCamelCase__						(   __A			):
						lowerCAmelCase       =     """xlm-prophetnet"""
						lowerCAmelCase       =     ["""past_key_values"""]
						lowerCAmelCase       =     {
						    """num_attention_heads""": """num_encoder_attention_heads""",
						}







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    int = 0.1			,			_lowercase   :    Optional[Any] = "gelu"			,			_lowercase   :    int = 30_522			,			_lowercase   :    Optional[Any] = 1_024			,			_lowercase   :    Optional[int] = 4_096			,			_lowercase   :    Any = 12			,			_lowercase   :    Tuple = 16			,			_lowercase   :    str = 4_096			,			_lowercase   :    List[str] = 12			,			_lowercase   :    str = 16			,			_lowercase   :    Optional[int] = 0.1			,			_lowercase   :    List[str] = 0.1			,			_lowercase   :    List[str] = 512			,			_lowercase   :    List[Any] = 0.0_2			,			_lowercase   :    List[Any] = True			,			_lowercase   :    List[str] = True			,			_lowercase   :    Any = 0			,			_lowercase   :    Optional[int] = 2			,			_lowercase   :    Any = 32			,			_lowercase   :    Optional[Any] = 128			,			_lowercase   :    str = False			,			_lowercase   :    Optional[int] = 0.0			,			_lowercase   :    str = True			,			_lowercase   :    Optional[Any] = 0			,			_lowercase   :    Tuple = 1			,			_lowercase   :    Optional[Any] = 2			,			**_lowercase   :    Dict			,			):
								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 xlmprophetnet
								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=_lowercase			,			bos_token_id=_lowercase			,			eos_token_id=_lowercase			,			is_encoder_decoder=_lowercase			,			add_cross_attention=_lowercase			,			decoder_start_token_id=_lowercase			,			**_lowercase			,			)







						@property
						def __a						(							self   :    List[Any]		):
								return self.num_encoder_layers + self.num_decoder_layers







						@num_hidden_layers.setter
						def __a						(							self   :    Optional[int]			,			_lowercase   :    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 math import factorial







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		float:







		"""simple docstring"""





		if successes > trials:
				raise ValueError('successes must be lower or equal to trials'	)
		if trials < 0 or successes < 0:
				raise ValueError('the function is defined for non-negative integers'	)
		if not isinstance(UpperCamelCase__ ,			UpperCamelCase__	) or not isinstance(UpperCamelCase__ ,			UpperCamelCase__	):
				raise ValueError('the function is defined for non-negative integers'	)
		if not 0 < prob < 1:
				raise ValueError('prob has to be in range of 1 - 0'	)
		A					       = (prob**successes) * ((1 - prob) ** (trials - successes))
		# Calculate the binomial coefficient: n! / k!(n-k)!
		A					       = float(factorial(UpperCamelCase__	)	)
		coefficient /= factorial(UpperCamelCase__	) * factorial(trials - successes	)
		return probability * coefficient


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	print("Probability of 2 successes out of 4 trails")
	print("with probability of 0.75 is:", end=" ")
	print(binomial_distribution(2, 4, 0.75))





