






from collections import OrderedDict
from typing import Mapping

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


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}




class  UpperCamelCase_      (			lowerCAmelCase_				):



	'''simple docstring'''




	lowerCAmelCase													= '''data2vec-text'''




	def __init__(       self       ,			a=3_05_22       ,			a=7_68       ,			a=12       ,			a=12       ,			a=30_72       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=2       ,			a=0.02       ,			a=1E-12       ,			a=1       ,			a=0       ,			a=2       ,			a="absolute"       ,			a=True       ,			a=None       ,			**a       ,			)       ->    int:
								super().__init__(pad_token_id=_snake_case       ,			bos_token_id=_snake_case       ,			eos_token_id=_snake_case       ,			**_snake_case					)

								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							hidden_act
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							initializer_range
								snake_case_				=							layer_norm_eps
								snake_case_				=							position_embedding_type
								snake_case_				=							use_cache
								snake_case_				=							classifier_dropout






class  UpperCamelCase_      (			lowerCAmelCase_				):



	'''simple docstring'''




	@property
	def 							_UpperCamelCase			(       self					)       ->    Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
															snake_case_				=							{0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
															snake_case_				=							{0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ]					)










from math import factorial
def        lowercase_							(A : int		,					A : int		,					A : float      ):
       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(A		,					A      ) or not isinstance(A		,					A      ):
              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'      )
       snake_case__							:	List[Any]   	=	(prob**successes) * ((1 - prob) ** (trials - successes))
       # Calculate the binomial coefficient: n! / k!(n-k)!
       snake_case__							:	List[str]   	=	float(factorial(A      )      )
       coefficient /= factorial(A      ) * 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))






