





'''simple docstring'''






from __future__ import annotations







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = None    ,	_lowerCamelCase = None ) ->				None:



			"""simple docstring"""


			if start is None:
						__snake_case					:      Optional[Any]							      =     0

			if end is None:
						__snake_case					:      Optional[Any]							      =     len(_lowerCamelCase ) - 1

			if start >= end:
						return

			__snake_case					:      Tuple							      =     (start + end) // 2

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
			slowsort(_lowerCamelCase    ,	mid + 1    ,	_lowerCamelCase )

			if sequence[end] < sequence[mid]:
						__snake_case      ,     __snake_case					:      str							      =     sequence[mid], sequence[end]

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	end - 1 )


if __name__ == "__main__":
	from doctest import testmod

	testmod()








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__					:  List[Any]      							=				TaTokenizer

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

lowerCamelCase__					:  Union[str, Any]      							=				TaTokenizerFast

lowerCamelCase__					:  Dict      							=				{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

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

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

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				['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__					:  int      							=				_LazyModule(
       __name__,
       globals()['__file__'],
       _import_structure,
       extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
       module_spec=__spec__,
   )