






from math import factorial

lowercase  :							dict[str, int]			  =    {str(digit): factorial(digit) for digit in range(10)}

def      lowerCAmelCase__	(    _a       :			int    ):
		if not isinstance(_a  ,      _a    ):
				raise TypeError("Parameter number must be int"    )

		if number < 0:
				raise ValueError("Parameter number must be greater than or equal to 0"    )

		# Converts number in string to iterate on its digits and adds its factorial.
		return sum(DIGIT_FACTORIAL[digit] for digit in str(_a    )    )

def      lowerCAmelCase__	(    _a       :			int = 60  ,      _a       :			int = 1_00_00_00    ):

		if not isinstance(_a  ,      _a    ) or not isinstance(_a  ,      _a    ):
				raise TypeError("Parameters chain_length and number_limit must be int"    )

		if chain_length <= 0 or number_limit <= 0:
				raise ValueError(
				    "Parameters chain_length and number_limit must be greater than 0"    )

		# the counter for the chains with the exact desired length
		snake_case_				:							int						 =       0
		# the cached sizes of the previous chains
		snake_case_				:							dict[int, int]						 =       {}

		for start_chain_element in range(1  ,      _a    ):
				# The temporary set will contain the elements of the chain
				snake_case_				:							Any						 =       set()
				snake_case_				:							Any						 =       0

				# Stop computing the chain when you find a cached size, a repeating item or the
				# length is greater then the desired one.
				snake_case_				:							Any						 =       start_chain_element
				while (
				    chain_element not in chain_sets_lengths
				    and chain_element not in chain_set
				    and chain_set_length <= chain_length
				):
						chain_set.add(_a    )
						chain_set_length += 1
						snake_case_				:							Any						 =       digit_factorial_sum(_a    )

				if chain_element in chain_sets_lengths:
						chain_set_length += chain_sets_lengths[chain_element]

				snake_case_				:							Union[str, Any]						 =       chain_set_length

				# If chain contains the exact amount of elements increase the counter
				if chain_set_length == chain_length:
						chains_counter += 1

		return chains_counter


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(F"""{solution()}""")














import functools
import operator

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


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

lowercase  :							Any			  =    {
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Optional[int]						=					'sew-d'






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=32		,				_SCREAMING_SNAKE_CASE=768		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=3072		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=512		,				_SCREAMING_SNAKE_CASE=256		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=("p2c", "c2p")		,				_SCREAMING_SNAKE_CASE="layer_norm"		,				_SCREAMING_SNAKE_CASE="gelu_python"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-7		,				_SCREAMING_SNAKE_CASE=1e-5		,				_SCREAMING_SNAKE_CASE="group"		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,				_SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,				_SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=128		,				_SCREAMING_SNAKE_CASE=16		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=0.05		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE="mean"		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=256		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=2		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[Any]:
								super().__init__(**_SCREAMING_SNAKE_CASE		,				pad_token_id=_SCREAMING_SNAKE_CASE		,				bos_token_id=_SCREAMING_SNAKE_CASE		,				eos_token_id=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[Any]						 =       hidden_size
								snake_case_				:							Any						 =       feat_extract_norm
								snake_case_				:							Union[str, Any]						 =       feat_extract_activation
								snake_case_				:							Any						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Any						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[Any]						 =       conv_bias
								snake_case_				:							Union[str, Any]						 =       num_conv_pos_embeddings
								snake_case_				:							Tuple						 =       num_conv_pos_embedding_groups
								snake_case_				:							Optional[int]						 =       len(self.conv_dim				)
								snake_case_				:							str						 =       num_hidden_layers
								snake_case_				:							List[Any]						 =       intermediate_size
								snake_case_				:							List[Any]						 =       squeeze_factor
								snake_case_				:							Tuple						 =       max_position_embeddings
								snake_case_				:							Optional[int]						 =       position_buckets
								snake_case_				:							Union[str, Any]						 =       share_att_key
								snake_case_				:							Optional[int]						 =       relative_attention
								snake_case_				:							List[str]						 =       norm_rel_ebd
								snake_case_				:							Tuple						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       hidden_act
								snake_case_				:							Any						 =       num_attention_heads
								snake_case_				:							Dict						 =       hidden_dropout
								snake_case_				:							int						 =       attention_dropout
								snake_case_				:							Any						 =       activation_dropout
								snake_case_				:							Optional[Any]						 =       feat_proj_dropout
								snake_case_				:							Tuple						 =       final_dropout
								snake_case_				:							Tuple						 =       layer_norm_eps
								snake_case_				:							Tuple						 =       feature_layer_norm_eps
								snake_case_				:							Any						 =       initializer_range
								snake_case_				:							Optional[int]						 =       vocab_size

								if (
								    (len(self.conv_stride				) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel				) != self.num_feat_extract_layers)
								    or (len(self.conv_dim				) != self.num_feat_extract_layers)
								):
										raise ValueError(
										    "Configuration for convolutional layers is incorrect."
										    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
										    f'''but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)'''
										    f'''= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`.'''				)

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								snake_case_				:							List[str]						 =       apply_spec_augment
								snake_case_				:							List[Any]						 =       mask_time_prob
								snake_case_				:							str						 =       mask_time_length
								snake_case_				:							Optional[Any]						 =       mask_time_min_masks
								snake_case_				:							Optional[Any]						 =       mask_feature_prob
								snake_case_				:							str						 =       mask_feature_length
								snake_case_				:							Optional[int]						 =       mask_feature_min_masks

								# ctc loss
								snake_case_				:							Any						 =       ctc_loss_reduction
								snake_case_				:							Tuple						 =       ctc_zero_infinity

								# sequence classification
								snake_case_				:							int						 =       use_weighted_layer_sum
								snake_case_				:							List[Any]						 =       classifier_proj_size






						@property
						def       _lowerCAmelCase       (	self				) ->				Dict:
								return functools.reduce(operator.mul		,				self.conv_stride		,				1				)
