code: 
  
  
  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,
  )
  
  
  A_														=      {
      "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
  }
  
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    A_														=      ["BigBirdTokenizer"]
  
  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    A_														=      ["BigBirdTokenizerFast"]
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    A_														=      [
        "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BigBirdForCausalLM",
        "BigBirdForMaskedLM",
        "BigBirdForMultipleChoice",
        "BigBirdForPreTraining",
        "BigBirdForQuestionAnswering",
        "BigBirdForSequenceClassification",
        "BigBirdForTokenClassification",
        "BigBirdLayer",
        "BigBirdModel",
        "BigBirdPreTrainedModel",
        "load_tf_weights_in_big_bird",
    ]
  
  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    A_														=      [
        "FlaxBigBirdForCausalLM",
        "FlaxBigBirdForMaskedLM",
        "FlaxBigBirdForMultipleChoice",
        "FlaxBigBirdForPreTraining",
        "FlaxBigBirdForQuestionAnswering",
        "FlaxBigBirdForSequenceClassification",
        "FlaxBigBirdForTokenClassification",
        "FlaxBigBirdModel",
        "FlaxBigBirdPreTrainedModel",
    ]
  
  if TYPE_CHECKING:
    from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig
  
    try:
      if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .tokenization_big_bird import BigBirdTokenizer
  
    try:
      if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .tokenization_big_bird_fast import BigBirdTokenizerFast
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_big_bird import (
          BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
          BigBirdForCausalLM,
          BigBirdForMaskedLM,
          BigBirdForMultipleChoice,
          BigBirdForPreTraining,
          BigBirdForQuestionAnswering,
          BigBirdForSequenceClassification,
          BigBirdForTokenClassification,
          BigBirdLayer,
          BigBirdModel,
          BigBirdPreTrainedModel,
          load_tf_weights_in_big_bird,
      )
  
    try:
      if not is_flax_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_flax_big_bird import (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
          FlaxBigBirdModel,
          FlaxBigBirdPreTrainedModel,
      )
  
  else:
    import sys
  
    A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  
  
  import unittest
  
  from transformers import load_tool
  from transformers.tools.agent_types import AGENT_TYPE_MAPPING
  
  from .test_tools_common import ToolTesterMixin, output_types
  
  class 						snake_case			(       unittest.TestCase	, lowerCAmelCase__	):
  			'''simple docstring'''
  
  			def _lowercase (	self     :     List[Any]					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       load_tool('''translation'''					)
  								self.tool.setup()
  								SCREAMING_SNAKE_CASE_	      =       load_tool('''translation'''							,							remote=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Tuple					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.tool('''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
  								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)
  
  			def _lowercase (	self     :     Optional[Any]					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.remote_tool('''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
  								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)
  
  			def _lowercase (	self     :     Optional[int]					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.tool(text='''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
  								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)
  
  			def _lowercase (	self     :     str					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.remote_tool(text='''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
  								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)
  
  			def _lowercase (	self     :     Optional[int]					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       ['''Hey, what\'s up?''', '''English''', '''Spanish''']
  								SCREAMING_SNAKE_CASE_	      =       self.tool(*lowerCAmelCase_					)
  
  								# There is a single output
  								if len(self.tool.outputs					) == 1:
  													SCREAMING_SNAKE_CASE_	      =       [outputs]
  
  								self.assertListEqual(output_types(lowerCAmelCase_					)							,							self.tool.outputs					)
  
  			def _lowercase (	self     :     Optional[int]					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       ['''Hey, what\'s up?''', '''English''', '''Spanish''']
  								SCREAMING_SNAKE_CASE_	      =       self.tool(*lowerCAmelCase_					)
  
  								if not isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  													SCREAMING_SNAKE_CASE_	      =       [outputs]
  
  								self.assertEqual(len(lowerCAmelCase_					)							,							len(self.tool.outputs					)					)
  
  								for output, output_type in zip(lowerCAmelCase_							,							self.tool.outputs					):
  													SCREAMING_SNAKE_CASE_	      =       AGENT_TYPE_MAPPING[output_type]
  													self.assertTrue(isinstance(lowerCAmelCase_							,							lowerCAmelCase_					)					)
  
  
  
  
  			def _lowercase (	self     :     Tuple					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       ['''Hey, what\'s up?''', '''English''', '''Spanish''']
  
  								SCREAMING_SNAKE_CASE_	      =       []
  
  								for _input, input_type in zip(lowerCAmelCase_							,							self.tool.inputs					):
  													if isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  																		_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input					) for _input_type in input_type]					)
  													else:
  																		_inputs.append(AGENT_TYPE_MAPPING[input_type](_input					)					)
  
          # Should not raise an error
  								SCREAMING_SNAKE_CASE_	      =       self.tool(*lowerCAmelCase_					)
  
  								if not isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  													SCREAMING_SNAKE_CASE_	      =       [outputs]
  
  								self.assertEqual(len(lowerCAmelCase_					)							,							len(self.tool.outputs					)					)
  
  
  