

'''simple docstring'''







from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
      from ..ta.tokenization_ta import TaTokenizer
else:
      from ...utils.dummy_sentencepiece_objects import TaTokenizer

__lowerCamelCase		       =    TaTokenizer

if is_tokenizers_available():
      from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
      from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__lowerCamelCase		       =    TaTokenizerFast

__lowerCamelCase		       =    {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase		       =    [
          '''MT5EncoderModel''',
          '''MT5ForConditionalGeneration''',
          '''MT5ForQuestionAnswering''',
          '''MT5Model''',
          '''MT5PreTrainedModel''',
          '''MT5Stack''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase		       =    ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase		       =    ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
      from .configuration_mta import MTaConfig, MTaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mta import (
                MTaEncoderModel,
                MTaForConditionalGeneration,
                MTaForQuestionAnswering,
                MTaModel,
                MTaPreTrainedModel,
                MTaStack,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
      import sys

      __lowerCamelCase		       =    _LazyModule(
          __name__,
          globals()['''__file__'''],
          _import_structure,
          extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
          module_spec=__spec__,
      )



'''simple docstring'''







from queue import PriorityQueue
from typing import Any

import numpy as np
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       )			->			float | int:
       for nxt, d in graph[v]:
              if nxt in visited_forward:
                     continue
              A_						=      cst_fwd.get(UpperCAmelCase__,       np.inf				)
              A_						=      cst_fwd[v] + d
              if new_cost_f < old_cost_f:
                     queue.put((new_cost_f, nxt)				)
                     A_						=      new_cost_f
                     A_						=      v
              if nxt in visited_backward:
                     if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                            A_						=      cst_fwd[v] + d + cst_bwd[nxt]
       return shortest_distance
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			int:
       A_						=      -1

       A_						=      set()
       A_						=      set()
       A_						=      {source: 0}
       A_						=      {destination: 0}
       A_						=      {source: None}
       A_						=      {destination: None}
       A_						=      PriorityQueue()
       A_						=      PriorityQueue()

       A_						=      np.inf

       queue_forward.put((0, source)				)
       queue_backward.put((0, destination)				)

       if source == destination:
              return 0

       while not queue_forward.empty() and not queue_backward.empty():
              A_					,			A_						=      queue_forward.get()
              visited_forward.add(UpperCAmelCase__				)

              A_					,			A_						=      queue_backward.get()
              visited_backward.add(UpperCAmelCase__				)

              A_						=      pass_and_relaxation(
                  UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       )

              A_						=      pass_and_relaxation(
                  UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       )

              if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
                     break

       if shortest_distance != np.inf:
              A_						=      shortest_distance
       return shortest_path_distance


__lowerCamelCase		       =    {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}
__lowerCamelCase		       =    {
    '''B''': [['''E''', 1]],
    '''C''': [['''B''', 1]],
    '''D''': [['''C''', 1]],
    '''F''': [['''D''', 1], ['''G''', 1]],
    '''E''': [[None, np.inf]],
    '''G''': [['''E''', 2]],
}

if __name__ == "__main__":
      import doctest

      doctest.testmod()
