



"""simple docstring"""




from collections.abc import Callable


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase		,					_UpperCAmelCase )   ->	Dict:






      '''simple docstring'''


      lowercase     :					Dict 		=      a
      lowercase     :					List[str] 		=      b
      if function(lowerCamelCase__ ) == 0:  # one of the a or b is a root for the function
            return a
      elif function(lowerCamelCase__ ) == 0:
            return b
      elif (
          function(lowerCamelCase__ ) * function(lowerCamelCase__ ) > 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:
            lowercase     :					Optional[Any] 		=      start + (end - start) / 2.0
            while abs(start - mid ) > 10**-7:  # until precisely equals to 10^-7
                  if function(lowerCamelCase__ ) == 0:
                        return mid
                  elif function(lowerCamelCase__ ) * function(lowerCamelCase__ ) < 0:
                        lowercase     :					Optional[int] 		=      mid
                  else:
                        lowercase     :					List[str] 		=      mid
                  lowercase     :					str 		=      start + (end - start) / 2.0
            return mid


def      lowercase__   (	_UpperCAmelCase )   ->	str:






      '''simple docstring'''


      return x**3 - 2 * x - 5


if __name__ == "__main__":
     print(bisection(f, 1, 1_0_0_0))

     import doctest

     doctest.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,
)


__A				={'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''XGLMTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''XGLMTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XGLMForCausalLM''',
			    '''XGLMModel''',
			    '''XGLMPreTrainedModel''',
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''FlaxXGLMForCausalLM''',
			    '''FlaxXGLMModel''',
			    '''FlaxXGLMPreTrainedModel''',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXGLMForCausalLM''',
			    '''TFXGLMModel''',
			    '''TFXGLMPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure)


