

'''simple docstring'''
from typing import Mapping

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


lowerCamelCase			:				Any 			=					logging.get_logger(__name__)

lowerCamelCase			:				List[str] 			=					{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}







class 							A__   (	A__  ):
  A__					=    't5'
  A__					=    ['past_key_values']
  A__					=    {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





  def __init__(		self				:     Tuple							, _a				:     Any=3_2128							, _a				:     List[Any]=512							, _a				:     List[Any]=64							, _a				:     Union[str, Any]=2048							, _a				:     List[Any]=6							, _a				:     Tuple=None							, _a				:     Tuple=8							, _a				:     List[str]=32							, _a				:     Optional[Any]=128							, _a				:     int=0.1							, _a				:     List[Any]=1e-6							, _a				:     Tuple=1.0							, _a				:     List[str]="relu"							, _a				:     Dict=True							, _a				:     Union[str, Any]=True							, _a				:     Union[str, Any]=0							, _a				:     Optional[Any]=1							, **_a				:     str							, )     ->   Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =vocab_size
       _SCREAMING_SNAKE_CASE							   =d_model
       _SCREAMING_SNAKE_CASE							   =d_kv
       _SCREAMING_SNAKE_CASE							   =d_ff
       _SCREAMING_SNAKE_CASE							   =num_layers
       _SCREAMING_SNAKE_CASE							   =(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       _SCREAMING_SNAKE_CASE							   =num_heads
       _SCREAMING_SNAKE_CASE							   =relative_attention_num_buckets
       _SCREAMING_SNAKE_CASE							   =relative_attention_max_distance
       _SCREAMING_SNAKE_CASE							   =dropout_rate
       _SCREAMING_SNAKE_CASE							   =layer_norm_epsilon
       _SCREAMING_SNAKE_CASE							   =initializer_factor
       _SCREAMING_SNAKE_CASE							   =feed_forward_proj
       _SCREAMING_SNAKE_CASE							   =use_cache

       _SCREAMING_SNAKE_CASE							   =self.feed_forward_proj.split('-'			)
       _SCREAMING_SNAKE_CASE							   =act_info[-1]
       _SCREAMING_SNAKE_CASE							   =act_info[0] == 'gated'

       if len(_a			) > 1 and act_info[0] != "gated" or len(_a			) > 2:
            raise ValueError(
                f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                '\'gated-gelu\' or \'relu\''			)

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
            _SCREAMING_SNAKE_CASE							   ='gelu_new'

       super().__init__(
           pad_token_id=_a							, eos_token_id=_a							, is_encoder_decoder=_a							, **_a							, )







class 							A__   (	A__  ):





  @property
  def 						A  (		self				:     Optional[Any]			)     ->   Mapping[str, Mapping[int, str]]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ={
           'input_ids': {0: 'batch', 1: 'encoder_sequence'},
           'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
       }
       if self.use_past:
            _SCREAMING_SNAKE_CASE							   ='past_encoder_sequence + sequence'
            _SCREAMING_SNAKE_CASE							   ={0: 'batch'}
            _SCREAMING_SNAKE_CASE							   ={0: 'batch', 1: 'past_decoder_sequence + sequence'}
       else:
            _SCREAMING_SNAKE_CASE							   ={0: 'batch', 1: 'decoder_sequence'}
            _SCREAMING_SNAKE_CASE							   ={0: 'batch', 1: 'decoder_sequence'}

       if self.use_past:
            self.fill_with_past_key_values_(_a							, direction='inputs'			)

       return common_inputs





  @property
  def 						A  (		self				:     List[Any]			)     ->   int:





       '''simple docstring'''






       return 13



'''simple docstring'''
from math import factorial


def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					float    )				->      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'    )
     _SCREAMING_SNAKE_CASE							   =(prob**successes) * ((1 - prob) ** (trials - successes))
     # Calculate the binomial coefficient: n! / k!(n-k)!
     _SCREAMING_SNAKE_CASE							   =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, 0.7_5))
