'''simple docstring'''

from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

_A      :				Tuple 				=    {
    '''A''': ['''B''', '''C'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E'''],
}







def 					UpperCamelCase_						( snake_case_	:	dict						,		snake_case_	:	str				) ->					list[str]:






 '''simple docstring'''

 __lowerCAmelCase    							=						{start}
 __lowerCAmelCase    							=						[start]
 __lowerCAmelCase    							=						Queue()
 queue.put(snake_case_				)
 while not queue.empty():
  __lowerCAmelCase    							=						queue.get()
  for w in graph[v]:
   if w not in explored:
    explored.add(snake_case_				)
    result.append(snake_case_				)
    queue.put(snake_case_				)
 return result







def 					UpperCamelCase_						( snake_case_	:	dict						,		snake_case_	:	str				) ->					list[str]:






 '''simple docstring'''

 __lowerCAmelCase    							=						{start}
 __lowerCAmelCase    							=						[start]
 __lowerCAmelCase    							=						deque([start]				)
 while queue:
  __lowerCAmelCase    							=						queue.popleft()
  for child in graph[v]:
   if child not in visited:
    visited.add(snake_case_				)
    result.append(snake_case_				)
    queue.append(snake_case_				)
 return result







def 					UpperCamelCase_						( snake_case_	:	str				) ->					None:






 '''simple docstring'''

 __lowerCAmelCase    							=						f"""from __main__ import G, {name}"""
 __lowerCAmelCase    							=						1_00_00
 __lowerCAmelCase    							=						timeit(f"""{name}(G, 'A')"""						,		setup=snake_case_						,		number=snake_case_				)
 print(f"""{name:<35} finished {number} runs in {res:.5f} seconds"""				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 benchmark_function('''breadth_first_search''')
 benchmark_function('''breadth_first_search_with_deque''')
 # breadth_first_search                finished 10000 runs in 0.20999 seconds
 # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A      :				Optional[int] 				=    {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A      :				Any 				=    [
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

 _A      :				Tuple 				=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






