


from functools import lru_cache

def       lowercase_		(		_A			:    Optional[int]						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  2
      lowerCamelCase__		:				Any    =  set()
      while i * i <= n:
            if n % i:
                  i += 1
            else:
                  n //= i
                  factors.add(A_						)
      if n > 1:
            factors.add(A_						)
      return factors

@lru_cache
def       lowercase_		(		_A			:    List[str]						):

      """simple docstring"""




      return len(unique_prime_factors(A_						)						)

def       lowercase_		(		_A			:    Any						):

      """simple docstring"""




      return len(set(A_						)						) in (0, 1)

def       lowercase_		(		_A			:    Tuple						):

      """simple docstring"""




      lowerCamelCase__		:				Any    =  2

      while True:
            # Increment each value of a generated range
            lowerCamelCase__		:				int    =  [base + i for i in range(A_						)]

            # Run elements through out unique_prime_factors function
            # Append our target number to the end.
            lowerCamelCase__		:				Tuple    =  [upf_len(A_						) for x in group]
            checker.append(A_						)

            # If all numbers in the list are equal, return the group variable.
            if equality(A_						):
                  return group

            # Increment our base variable by 1
            base += 1

def       lowercase_		(		_A			:    int = 4						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  run(A_						)
      return results[0] if len(A_						) else None


if __name__ == "__main__":
    print(solution())




from typing import TYPE_CHECKING

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


__lowerCAmelCase			:     str           =   {
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Any           =   ['FunnelTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Optional[int]           =   [
        'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'FunnelBaseModel',
        'FunnelForMaskedLM',
        'FunnelForMultipleChoice',
        'FunnelForPreTraining',
        'FunnelForQuestionAnswering',
        'FunnelForSequenceClassification',
        'FunnelForTokenClassification',
        'FunnelModel',
        'FunnelPreTrainedModel',
        'load_tf_weights_in_funnel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Tuple           =   [
        'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFFunnelBaseModel',
        'TFFunnelForMaskedLM',
        'TFFunnelForMultipleChoice',
        'TFFunnelForPreTraining',
        'TFFunnelForQuestionAnswering',
        'TFFunnelForSequenceClassification',
        'TFFunnelForTokenClassification',
        'TFFunnelModel',
        'TFFunnelPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
    from .tokenization_funnel import FunnelTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_funnel_fast import FunnelTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_funnel import (
            FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
            FunnelBaseModel,
            FunnelForMaskedLM,
            FunnelForMultipleChoice,
            FunnelForPreTraining,
            FunnelForQuestionAnswering,
            FunnelForSequenceClassification,
            FunnelForTokenClassification,
            FunnelModel,
            FunnelPreTrainedModel,
            load_tf_weights_in_funnel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_funnel import (
            TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFFunnelBaseModel,
            TFFunnelForMaskedLM,
            TFFunnelForMultipleChoice,
            TFFunnelForPreTraining,
            TFFunnelForQuestionAnswering,
            TFFunnelForSequenceClassification,
            TFFunnelForTokenClassification,
            TFFunnelModel,
            TFFunnelPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase			:     List[Any]           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

