




from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCamelCase	:	Optional[int]				=   1_0




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase					)  ->       int:
   for i in range(lowercase						,lowercase					):
      if array[i] == target:
         return i
   return -1




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:
   snake_case				:    Optional[int]           =	0
   snake_case				:    Union[str, Any]           =	len(lowercase					)
   while left <= right:
      if right - left < precision:
         return lin_search(lowercase						,lowercase						,lowercase						,lowercase					)

      snake_case				:    str           =	(left + right) // 3 + 1
      snake_case				:    Tuple           =	2 * (left + right) // 3 + 1

      if array[one_third] == target:
         return one_third
      elif array[two_third] == target:
         return two_third

      elif target < array[one_third]:
         snake_case				:    Dict           =	one_third - 1
      elif array[two_third] < target:
         snake_case				:    int           =	two_third + 1

      else:
         snake_case				:    str           =	one_third + 1
         snake_case				:    List[str]           =	two_third - 1
   else:
      return -1




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase					)  ->       int:
   if left < right:
      if right - left < precision:
         return lin_search(lowercase						,lowercase						,lowercase						,lowercase					)
      snake_case				:    List[str]           =	(left + right) // 3 + 1
      snake_case				:    List[str]           =	2 * (left + right) // 3 + 1

      if array[one_third] == target:
         return one_third
      elif array[two_third] == target:
         return two_third

      elif target < array[one_third]:
         return rec_ternary_search(lowercase						,one_third - 1						,lowercase						,lowercase					)
      elif array[two_third] < target:
         return rec_ternary_search(two_third + 1						,lowercase						,lowercase						,lowercase					)
      else:
         return rec_ternary_search(one_third + 1						,two_third - 1						,lowercase						,lowercase					)
   else:
      return -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       lowerCamelCase	:	Any				=   input('Enter numbers separated by comma:\n').strip()
       lowerCamelCase	:	str				=   [int(item.strip()) for item in user_input.split(',')]
       assert collection == sorted(collection), f"List must be ordered.\n{collection}."
       lowerCamelCase	:	List[Any]				=   int(input('Enter the number to be found in the list:\n').strip())
       lowerCamelCase	:	List[str]				=   ite_ternary_search(collection, target)
       lowerCamelCase	:	int				=   rec_ternary_search(0, len(collection) - 1, collection, target)
       if resulta != -1:
              print(f"""Iterative search: {target} found at positions: {resulta}""")
              print(f"""Recursive search: {target} found at positions: {resulta}""")
       else:
              print('Not found')











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase	:	Optional[Any]				=   {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Tuple				=   ['RobertaTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[str]				=   [
           'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaForCausalLM',
           'RobertaForMaskedLM',
           'RobertaForMultipleChoice',
           'RobertaForQuestionAnswering',
           'RobertaForSequenceClassification',
           'RobertaForTokenClassification',
           'RobertaModel',
           'RobertaPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaForCausalLM',
           'TFRobertaForMaskedLM',
           'TFRobertaForMultipleChoice',
           'TFRobertaForQuestionAnswering',
           'TFRobertaForSequenceClassification',
           'TFRobertaForTokenClassification',
           'TFRobertaMainLayer',
           'TFRobertaModel',
           'TFRobertaPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'FlaxRobertaForCausalLM',
           'FlaxRobertaForMaskedLM',
           'FlaxRobertaForMultipleChoice',
           'FlaxRobertaForQuestionAnswering',
           'FlaxRobertaForSequenceClassification',
           'FlaxRobertaForTokenClassification',
           'FlaxRobertaModel',
           'FlaxRobertaPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase	:	str				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





