





"""simple docstring"""





from math import factorial

__A											=						{str(digit): factorial(digit) for digit in range(10)}
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  if not isinstance(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ):
    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(_SCREAMING_SNAKE_CASE     )     )
def 					__A  (_SCREAMING_SNAKE_CASE = 60	,  _SCREAMING_SNAKE_CASE = 100_0000     )   ->int:





  """simple docstring"""


  if not isinstance(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ) or not isinstance(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ):
    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
  lowerCAmelCase__     :Union[str, Any]											=				0
  # the cached sizes of the previous chains
  lowerCAmelCase__     :dict[int, int]											=				{}

  for start_chain_element in range(1	,  _SCREAMING_SNAKE_CASE     ):
    # The temporary set will contain the elements of the chain
    lowerCAmelCase__     :Optional[Any]											=				set()
    lowerCAmelCase__     :Tuple											=				0

    # Stop computing the chain when you find a cached size, a repeating item or the
    # length is greater then the desired one.
    lowerCAmelCase__     :Dict											=				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(_SCREAMING_SNAKE_CASE     )
      chain_set_length += 1
      lowerCAmelCase__     :int											=				digit_factorial_sum(_SCREAMING_SNAKE_CASE     )

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

    lowerCAmelCase__     :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()}''')








# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
__a   :							Tuple						= "\nHuman: <<task>>\n\nAssistant: "


__a   :							Union[str, Any]						= "huggingface-tools/default-prompts"
__a   :							Optional[int]						= {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Optional[Any]      ,					__lowercase :      List[Any]="run"     )    ->   Optional[int]:





    """simple docstring"""

    if prompt_or_repo_id is None:
        __A       =						DEFAULT_PROMPTS_REPO

    # prompt is considered a repo ID when it does not contain any kind of space
    if re.search("""\\s"""      ,					__lowercase     ) is not None:
        return prompt_or_repo_id

    __A       =						cached_file(
        __lowercase      ,					PROMPT_FILES[mode]      ,					repo_type="""dataset"""      ,					user_agent={"""agent""": agent_name}     )
    with open(__lowercase      ,					"""r"""      ,					encoding="""utf-8"""     ) as f:
        return f.read()




