
from __future__ import annotations

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

lowercase_											=	{
    """A""": ["""B""", """C"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E"""],
}


def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						{start}
  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						[start]
  __SCREAMING_SNAKE_CASE     :					Queue			=						Queue()
  queue.put(snake_case    )
  while not queue.empty():
    __SCREAMING_SNAKE_CASE     :					List[str]			=						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     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[str]			=						{start}
  __SCREAMING_SNAKE_CASE     :					Dict			=						[start]
  __SCREAMING_SNAKE_CASE     :					str			=						deque([start]    )
  while queue:
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						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     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					str			=						F'''from __main__ import G, {name}'''
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						10_000
  __SCREAMING_SNAKE_CASE     :					List[str]			=						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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_											=	{
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""LEDTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	[
       """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LEDForConditionalGeneration""",
       """LEDForQuestionAnswering""",
       """LEDForSequenceClassification""",
       """LEDModel""",
       """LEDPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

   lowercase_											=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
