
"""simple docstring"""

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        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							str							 =		c_float




      @property
      def       a_ (							self							)  ->   int:
          return sizeof(self.value							)




def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   int	,			lowerCAmelCase			:   int = 0	)	-> int:






    """simple docstring"""




    if dimension <= 0:
        UpperCAmelCase       =   fixed_dimension

    dimension -= num_token_to_add
    return dimension




def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   ParameterFormat	)	-> int:






    """simple docstring"""




    return num_parameters * dtype.size







def       lowercase__  (   lowerCAmelCase			:   str	)	-> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:






    """simple docstring"""




    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

    try:
        return AutoProcessor.from_pretrained(lowerCAmelCase	)
    except (ValueError, OSError, KeyError):
        UpperCAmelCase     ,							UpperCAmelCase       =   None, None
        try:
            UpperCAmelCase       =   AutoTokenizer.from_pretrained(lowerCAmelCase	)
        except (OSError, KeyError):
            pass
        try:
            UpperCAmelCase       =   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





"""simple docstring"""

import heapq




def       lowercase__  (   lowerCAmelCase			:   str	,			lowerCAmelCase			:   int	,			lowerCAmelCase			:   Optional[Any]	)	-> Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase       =   [(0, start)]  # cost from start node,end node
    UpperCAmelCase       =   set()
    while heap:
        ((UpperCAmelCase)     ,							(UpperCAmelCase))       =   heapq.heappop(lowerCAmelCase	)
        if u in visited:
            continue
        visited.add(lowerCAmelCase	)
        if u == end:
            return cost
        for v, c in graph[u]:
            if v in visited:
                continue
            UpperCAmelCase       =   cost + c
            heapq.heappush(lowerCAmelCase	,			(next_item, v)	)
    return -1


SCREAMING_SNAKE_CASE_         =  {
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
SCREAMING_SNAKE_CASE_         =  {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
SCREAMING_SNAKE_CASE_         =  {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

SCREAMING_SNAKE_CASE_         =  dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

SCREAMING_SNAKE_CASE_         =  dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

SCREAMING_SNAKE_CASE_         =  dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
   import doctest

   doctest.testmod()



