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,
  )
  
  
  SCREAMING_SNAKE_CASE : int          =    {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}
  
  try:
  			if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			SCREAMING_SNAKE_CASE : Optional[Any]          =    ["T5Tokenizer"]
  
  try:
  			if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			SCREAMING_SNAKE_CASE : List[Any]          =    ["T5TokenizerFast"]
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			SCREAMING_SNAKE_CASE : List[Any]          =    [
  			    "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
  			    "T5EncoderModel",
  			    "T5ForConditionalGeneration",
  			    "T5Model",
  			    "T5PreTrainedModel",
  			    "load_tf_weights_in_t5",
  			    "T5ForQuestionAnswering",
  			]
  
  try:
  			if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			SCREAMING_SNAKE_CASE : Tuple          =    [
  			    "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
  			    "TFT5EncoderModel",
  			    "TFT5ForConditionalGeneration",
  			    "TFT5Model",
  			    "TFT5PreTrainedModel",
  			]
  
  try:
  			if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			SCREAMING_SNAKE_CASE : List[Any]          =    [
  			    "FlaxT5EncoderModel",
  			    "FlaxT5ForConditionalGeneration",
  			    "FlaxT5Model",
  			    "FlaxT5PreTrainedModel",
  			]
  
  
  if TYPE_CHECKING:
  			from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig
  
  			try:
  						if not is_sentencepiece_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_ta import TaTokenizer
  
  			try:
  						if not is_tokenizers_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_ta_fast import TaTokenizerFast
  
  			try:
  						if not is_torch_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .modeling_ta import (
  						    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    TaEncoderModel,
  						    TaForConditionalGeneration,
  						    TaForQuestionAnswering,
  						    TaModel,
  						    TaPreTrainedModel,
  						    load_tf_weights_in_ta,
  						)
  
  			try:
  						if not is_tf_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .modeling_tf_ta import (
  						    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    TFTaEncoderModel,
  						    TFTaForConditionalGeneration,
  						    TFTaModel,
  						    TFTaPreTrainedModel,
  						)
  
  			try:
  						if not is_flax_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .modeling_flax_ta import (
  						    FlaxTaEncoderModel,
  						    FlaxTaForConditionalGeneration,
  						    FlaxTaModel,
  						    FlaxTaPreTrainedModel,
  						)
  
  
  else:
  			import sys
  
  			SCREAMING_SNAKE_CASE : Tuple          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list[int]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   List[Any]			=	len(SCREAMING_SNAKE_CASE_      )
  							if n in (0, 1):
  														return 0
  							return sum(query * (n - i - 1) for i, query in enumerate(sorted(SCREAMING_SNAKE_CASE_      )      )      )
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  