"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__     						=  {
    """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:
    UpperCAmelCase__     						=  ["""FunnelTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__     						=  [
        """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:
    UpperCAmelCase__     						=  [
        """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

    UpperCAmelCase__     						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ):
 __a :  str						=  len(lowerCAmelCase__  )
 __a :  Optional[int]						=  []
 for i in range(len(lowerCAmelCase__  ) - pat_len + 1  ):
  __a :  str						=  True
  for j in range(lowerCAmelCase__  ):
   if s[i + j] != pattern[j]:
    __a :  Tuple						=  False
    break
  if match_found:
   position.append(lowerCAmelCase__  )
 return position


if __name__ == "__main__":
      assert naive_pattern_search('ABCDEFG', 'DE') == [3]
      print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))
