





"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


a		:					int	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[int]   )					->List[int]:






	'''simple docstring'''






	if isinstance(snake_case__       ,			np.ndarray   ):
		return list(tensor.shape   )

	a :						List[str]      =							tf.shape(snake_case__   )

	if tensor.shape == tf.TensorShape(snake_case__   ):
		return dynamic

	a :						Optional[Any]      =							tensor.shape.as_list()

	return [dynamic[i] if s is None else s for i, s in enumerate(snake_case__   )]






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict       ,			_lowercase		:       Union[str, Any] = None       ,			_lowercase		:       Optional[int] = None   )					->tf.Tensor:






	'''simple docstring'''






	return tf.nn.softmax(logits=logits + 1E-9       ,			axis=snake_case__       ,			name=snake_case__   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       List[str]       ,			_lowercase		:       Any       ,			_lowercase		:       int=1E-5       ,			_lowercase		:       Dict=-1   )					->Dict:






	'''simple docstring'''






	if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(snake_case__       ,			snake_case__   ):
		raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."   )

	# Get mean and variance on the axis to be normalized
	a :						int      =							tf.nn.moments(snake_case__       ,			axes=[axis]       ,			keepdims=snake_case__   )

	if axis != -1:
		# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
		# on every dimension except axis
		a :						List[Any]      =							[1] * inputs.shape.rank
		a :						str      =							shape_list(snake_case__   )[axis]
		a :						Optional[int]      =							tf.reshape(snake_case__       ,			snake_case__   )
		a :						Optional[int]      =							tf.reshape(snake_case__       ,			snake_case__   )

	# Compute layer normalization using the batch_normalization
	# function.
	a :						Optional[Any]      =							tf.nn.batch_normalization(
	    snake_case__       ,			snake_case__       ,			snake_case__       ,			offset=snake_case__       ,			scale=snake_case__       ,			variance_epsilon=snake_case__       ,			)
	return outputs






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Any       ,			_lowercase		:       int=0       ,			_lowercase		:       Tuple=-1   )					->List[str]:






	'''simple docstring'''






	if end_dim < 0:
		end_dim += input.shape.rank
	if start_dim < 0:
		start_dim += input.shape.rank

	if start_dim == end_dim:
		return input

	a :						Dict      =							tf.shape(snake_case__   )
	a :						int      =							tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]   )
	a :						str      =							tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]       ,			axis=0   )
	return tf.reshape(snake_case__       ,			snake_case__   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->tf.Tensor:






	'''simple docstring'''






	if not isinstance(snake_case__       ,			tf.Tensor   ):
		a :						int      =							tf.convert_to_tensor(snake_case__   )  # Catches stray NumPy inputs
	if encoder_attention_mask.shape.rank == 3:
		a :						int      =							encoder_attention_mask[:, None, :, :]
	if encoder_attention_mask.shape.rank == 2:
		a :						Dict      =							encoder_attention_mask[:, None, None, :]
	# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
	# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
	# /transformer/transformer_layers.py#L270
	# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
	# encoder_extended_attention_mask.transpose(-1, -2))
	a :						List[Any]      =							(
	    tf.cast(1       ,			encoder_attention_mask.dtype   ) - encoder_extended_attention_mask
	) * encoder_extended_attention_mask.dtype.min

	return encoder_extended_attention_mask






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int       ,			_lowercase		:       Any       ,			_lowercase		:       Optional[int] = "input_ids"   )					->None:






	'''simple docstring'''






	tf.debugging.assert_less(
	    snake_case__       ,			tf.cast(snake_case__       ,			dtype=tensor.dtype   )       ,			message=(
	        F"""The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case__   )}) must be smaller than the embedding """
	        F"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
	    )       ,			)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int       ,			_lowercase		:       Union[str, Any]       ,			_lowercase		:       Optional[Any]   )					->str:






	'''simple docstring'''






	a :						str      =							6_4512
	# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
	# because in that case even chunking the array would not make the saving
	# possible.
	a :						List[str]      =							[x for x in data if len(snake_case__   ) > HDF5_OBJECT_HEADER_LIMIT]

	# Expecting this to never be true.
	if bad_attributes:
		raise RuntimeError(
		    "The following attributes cannot be saved to HDF5 file because "
		    F"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
		    F"""bytes: {bad_attributes}"""   )

	a :						int      =							np.asarray(snake_case__   )

	a :						Union[str, Any]      =							1
	a :						Tuple      =							np.array_split(snake_case__       ,			snake_case__   )

	# This will never loop forever thanks to the test above.
	while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data   ):
		num_chunks += 1
		a :						Tuple      =							np.array_split(snake_case__       ,			snake_case__   )

	if num_chunks > 1:
		for chunk_id, chunk_data in enumerate(snake_case__   ):
			a :						Tuple      =							chunk_data
	else:
		a :						Tuple      =							data






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[Any]       ,			_lowercase		:       Union[str, Any]   )					->Union[str, Any]:






	'''simple docstring'''






	if name in group.attrs:
		a :						Dict      =							[n.decode("utf8"   ) if hasattr(snake_case__       ,			"decode"   ) else n for n in group.attrs[name]]
	else:
		a :						int      =							[]
		a :						List[Any]      =							0
		while "%s%d" % (name, chunk_id) in group.attrs:
			data.extend(
			    [n.decode("utf8"   ) if hasattr(snake_case__       ,			"decode"   ) else n for n in group.attrs["%s%d" % (name, chunk_id)]]   )
			chunk_id += 1
	return data







def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Any   )					->Optional[Any]:






	'''simple docstring'''






	def _expand_single_ad_tensor(_lowercase		:       str   ):
		if isinstance(snake_case__       ,			tf.Tensor   ) and t.shape.rank == 1:
			return tf.expand_dims(snake_case__       ,			axis=-1   )
		return t

	return tf.nest.map_structure(_expand_single_ad_tensor       ,			snake_case__   )






"""simple docstring"""






def 				A_       ( snake_case__		)					->  int:
			_UpperCamelCase					:Dict					     =	1
			for i in range(1       ,					num + 1		):
						fact *= i
			return fact





def 				A_       ( snake_case__		)					->  int:
			_UpperCamelCase					:Dict					     =	0
			while number > 0:
						_UpperCamelCase					:Tuple					     =	number % 10
						sum_of_digits += last_digit
						_UpperCamelCase					:str					     =	number // 10  # Removing the last_digit from the given number
			return sum_of_digits





def 				A_       ( snake_case__ = 1_00		)					->  int:
			_UpperCamelCase					:int					     =	factorial(snake_case__		)
			_UpperCamelCase					:Dict					     =	split_and_add(snake_case__		)
			return result


if __name__ == "__main__":
					print(solution(int(input("""Enter the Number: """).strip())))

