




'''simple docstring'''






from collections.abc import Callable






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__       :   Union[str, Any]	      =    a
      snake_case__       :   Any	      =    b
      if function(__SCREAMING_SNAKE_CASE		) == 0:  # one of the a or b is a root for the function
            return a
      elif function(__SCREAMING_SNAKE_CASE		) == 0:
            return b
      elif (
          function(__SCREAMING_SNAKE_CASE		) * function(__SCREAMING_SNAKE_CASE		) > 0
      ):  # if none of these are root and they are both positive or negative,
            # then this algorithm can't find the root
            raise ValueError('could not find root in given interval.'		)
      else:
            snake_case__       :   Optional[int]	      =    start + (end - start) / 2.0
            while abs(start - mid		) > 10**-7:  # until precisely equals to 10^-7
                  if function(__SCREAMING_SNAKE_CASE		) == 0:
                        return mid
                  elif function(__SCREAMING_SNAKE_CASE		) * function(__SCREAMING_SNAKE_CASE		) < 0:
                        snake_case__       :   str	      =    mid
                  else:
                        snake_case__       :   int	      =    mid
                  snake_case__       :   Tuple	      =    start + (end - start) / 2.0
            return mid






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   List[str]:
      return x**3 - 2 * x - 5


if __name__ == "__main__":
      print(bisection(f, 1, 1000))

      import doctest

      doctest.testmod()


'''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					:    Tuple           = TaTokenizer

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

lowerCAmelCase					:    Optional[int]           = TaTokenizerFast

lowerCAmelCase					:    Any           = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

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

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

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




