
"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A			:       Tuple   		=       {
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       List[str]   		=       [
       """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """Data2VecAudioForAudioFrameClassification""",
       """Data2VecAudioForCTC""",
       """Data2VecAudioForSequenceClassification""",
       """Data2VecAudioForXVector""",
       """Data2VecAudioModel""",
       """Data2VecAudioPreTrainedModel""",
   ]
   _A			:       str   		=       [
       """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """Data2VecTextForCausalLM""",
       """Data2VecTextForMaskedLM""",
       """Data2VecTextForMultipleChoice""",
       """Data2VecTextForQuestionAnswering""",
       """Data2VecTextForSequenceClassification""",
       """Data2VecTextForTokenClassification""",
       """Data2VecTextModel""",
       """Data2VecTextPreTrainedModel""",
   ]
   _A			:       Union[str, Any]   		=       [
       """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """Data2VecVisionForImageClassification""",
       """Data2VecVisionForMaskedImageModeling""",
       """Data2VecVisionForSemanticSegmentation""",
       """Data2VecVisionModel""",
       """Data2VecVisionPreTrainedModel""",
   ]

if is_tf_available():
   _A			:       Optional[Any]   		=       [
       """TFData2VecVisionForImageClassification""",
       """TFData2VecVisionForSemanticSegmentation""",
       """TFData2VecVisionModel""",
       """TFData2VecVisionPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
   from .configuration_dataavec_text import (
       DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecTextConfig,
       DataaVecTextOnnxConfig,
   )
   from .configuration_dataavec_vision import (
       DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecVisionConfig,
       DataaVecVisionOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dataavec_audio import (
          DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecAudioForAudioFrameClassification,
          DataaVecAudioForCTC,
          DataaVecAudioForSequenceClassification,
          DataaVecAudioForXVector,
          DataaVecAudioModel,
          DataaVecAudioPreTrainedModel,
      )
      from .modeling_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecTextForCausalLM,
          DataaVecTextForMaskedLM,
          DataaVecTextForMultipleChoice,
          DataaVecTextForQuestionAnswering,
          DataaVecTextForSequenceClassification,
          DataaVecTextForTokenClassification,
          DataaVecTextModel,
          DataaVecTextPreTrainedModel,
      )
      from .modeling_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecVisionForImageClassification,
          DataaVecVisionForMaskedImageModeling,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
          DataaVecVisionPreTrainedModel,
      )
   if is_tf_available():
      from .modeling_tf_dataavec_vision import (
          TFDataaVecVisionForImageClassification,
          TFDataaVecVisionForSemanticSegmentation,
          TFDataaVecVisionModel,
          TFDataaVecVisionPreTrainedModel,
      )

else:
   import sys

   _A			:       Union[str, Any]   		=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		[0 for i in range(len(SCREAMING_SNAKE_CASE	)	)]

    # initialize interval's left pointer and right pointer
    __UpperCamelCase  ,       __UpperCamelCase     :str			  =		0, 0

    for i in range(1  ,			len(SCREAMING_SNAKE_CASE	)	):
        # case when current index is inside the interval
        if i <= right_pointer:
            __UpperCamelCase     :Union[str, Any]			  =		min(right_pointer - i + 1  ,			z_result[i - left_pointer]	)
            __UpperCamelCase     :Tuple			  =		min_edge

        while go_next(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
            z_result[i] += 1

        # if new index's result gives us more right interval,
        # we've to update left_pointer and right_pointer
        if i + z_result[i] - 1 > right_pointer:
            __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		i, i + z_result[i] - 1

    return z_result



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    return i + z_result[i] < len(SCREAMING_SNAKE_CASE	) and s[z_result[i]] == s[i + z_result[i]]



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		0
    # concatenate 'pattern' and 'input_str' and call z_function
    # with concatenated string
    __UpperCamelCase     :Tuple			  =		z_function(pattern + input_str	)

    for val in z_result:
        # if value is greater then length of the pattern string
        # that means this index is starting position of substring
        # which is equal to pattern string
        if val >= len(SCREAMING_SNAKE_CASE	):
            answer += 1

    return answer


if __name__ == "__main__":
    import doctest

    doctest.testmod()





