





'''simple docstring'''






from typing import Mapping

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


__lowerCamelCase						: List[Any]		=   logging.get_logger(__name__)

__lowerCamelCase						: Optional[int]		=   {
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				"""umt5"""
  lowerCAmelCase_					=				["""past_key_values"""]


  def __init__(				self    :					Any							,	UpperCamelCase__    :					int=25_0112							,	UpperCamelCase__    :					str=512							,	UpperCamelCase__    :					Optional[Any]=64							,	UpperCamelCase__    :					int=1024							,	UpperCamelCase__    :					Tuple=8							,	UpperCamelCase__    :					Optional[int]=None							,	UpperCamelCase__    :					int=6							,	UpperCamelCase__    :					int=32							,	UpperCamelCase__    :					Any=128							,	UpperCamelCase__    :					List[str]=0.1							,	UpperCamelCase__    :					Optional[int]=1E-6							,	UpperCamelCase__    :					Dict=1.0							,	UpperCamelCase__    :					Any="gated-gelu"							,	UpperCamelCase__    :					int=True							,	UpperCamelCase__    :					Optional[Any]=True							,	UpperCamelCase__    :					Optional[Any]="T5Tokenizer"							,	UpperCamelCase__    :					Optional[int]=True							,	UpperCamelCase__    :					str=0							,	UpperCamelCase__    :					int=1							,	UpperCamelCase__    :					Union[str, Any]=0							,	**UpperCamelCase__    :					Tuple							,	)     ->						Dict:
    super().__init__(
        is_encoder_decoder=UpperCamelCase__							,	tokenizer_class=UpperCamelCase__							,	tie_word_embeddings=UpperCamelCase__							,	pad_token_id=UpperCamelCase__							,	eos_token_id=UpperCamelCase__							,	decoder_start_token_id=UpperCamelCase__							,	**UpperCamelCase__							,	)
    _UpperCamelCase       			=vocab_size
    _UpperCamelCase       			=d_model
    _UpperCamelCase       			=d_kv
    _UpperCamelCase       			=d_ff
    _UpperCamelCase       			=num_layers
    _UpperCamelCase       			=(
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    _UpperCamelCase       			=num_heads
    _UpperCamelCase       			=relative_attention_num_buckets
    _UpperCamelCase       			=relative_attention_max_distance
    _UpperCamelCase       			=dropout_rate
    _UpperCamelCase       			=layer_norm_epsilon
    _UpperCamelCase       			=initializer_factor
    _UpperCamelCase       			=feed_forward_proj
    _UpperCamelCase       			=use_cache

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

    if len(UpperCamelCase__  ) > 1 and act_info[0] != "gated" or len(UpperCamelCase__  ) > 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\''''  )

    if feed_forward_proj == "gated-gelu":
      _UpperCamelCase       			='''gelu_new'''


  @property
  def   UpperCamelCase__      (				self    :					Dict  )     ->						Dict:
    return self.d_model


  @property
  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						List[str]:
    return self.num_heads




  @property
  def   UpperCamelCase__      (				self    :					Dict  )     ->						int:
    return self.num_layers







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  @property
  # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Mapping[str, Mapping[int, str]]:
    _UpperCamelCase       			={
        '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
        '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
    }
    if self.use_past:
      _UpperCamelCase       			='''past_encoder_sequence + sequence'''
      _UpperCamelCase       			={0: '''batch'''}
      _UpperCamelCase       			={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    else:
      _UpperCamelCase       			={0: '''batch''', 1: '''decoder_sequence'''}
      _UpperCamelCase       			={0: '''batch''', 1: '''decoder_sequence'''}

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

    return common_inputs


  @property
  # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						int:
    return 13




  @property
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						float:
    return 5E-4








'''simple docstring'''






def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return 1 if digit in (0, 1) else (digit * factorial(digit - 1   ))


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=0
  _UpperCamelCase       			=number
  while duplicate > 0:
    _UpperCamelCase			,     _UpperCamelCase       			=divmod(__SCREAMING_SNAKE_CASE  ,      10   )
    fact_sum += factorial(__SCREAMING_SNAKE_CASE   )
  return fact_sum == number


if __name__ == "__main__":
      print('Program to check whether a number is a Krisnamurthy Number or not.')
      __lowerCamelCase						: Optional[Any]		=   int(input('Enter number: ').strip())
      print(
          F"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
      )






