
from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[int]       				=      c_float




      @property
      def 						snake_case_     ( self						:    List[str]			):
          return sizeof(self.value			)






def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase = 0		)	->  int:
    # < 0 is possible if using a dynamic axis
    if dimension <= 0:
        __lowercase						:     List[str]		      =    fixed_dimension

    dimension -= num_token_to_add
    return dimension


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  int:
    return num_parameters * dtype.size





def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

    try:
        return AutoProcessor.from_pretrained(__lowerCAmelCase		)
    except (ValueError, OSError, KeyError):
        __lowercase      , __lowercase						:     List[Any]		      =    None, None
        try:
            __lowercase						:     Dict		      =    AutoTokenizer.from_pretrained(__lowerCAmelCase		)
        except (OSError, KeyError):
            pass
        try:
            __lowercase						:     Union[str, Any]		      =    AutoFeatureExtractor.from_pretrained(__lowerCAmelCase		)
        except (OSError, KeyError):
            pass

        if tokenizer is not None and feature_extractor is not None:
            raise ValueError(
                F'Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'		)
        elif tokenizer is None and feature_extractor is None:
            return None
        elif tokenizer is not None:
            return tokenizer
        else:
            return feature_extractor




from __future__ import annotations

import math
from itertools import permutations


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  bool:

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5		,   int(math.sqrt(__lowerCAmelCase		) + 1		)		,   6		):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def       UpperCAmelCase_					(  __lowerCAmelCase = 7		)	->  int:
    __lowercase						:     Union[str, Any]		      =    ''''''.join(str(__lowerCAmelCase		) for i in range(1		,   n + 1		)		)
    __lowercase						:     Dict		      =    [int(''''''.join(__lowerCAmelCase		)		) for i in permutations(__lowerCAmelCase		,   __lowerCAmelCase		)]
    __lowercase						:     int		      =    [num for num in perm_list if is_prime(__lowerCAmelCase		)]
    return max(__lowerCAmelCase		) if pandigitals else 0


if __name__ == "__main__":
 print(F'{solution() = }')


