





"""simple docstring"""


from typing import TYPE_CHECKING

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


__magic_name__       :					List[str]						=			{
    """configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Config"""],
    """feature_extraction_wav2vec2""": ["""Wav2Vec2FeatureExtractor"""],
    """processing_wav2vec2""": ["""Wav2Vec2Processor"""],
    """tokenization_wav2vec2""": ["""Wav2Vec2CTCTokenizer""", """Wav2Vec2Tokenizer"""],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					List[str]						=			[
		    """WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Wav2Vec2ForAudioFrameClassification""",
		    """Wav2Vec2ForCTC""",
		    """Wav2Vec2ForMaskedLM""",
		    """Wav2Vec2ForPreTraining""",
		    """Wav2Vec2ForSequenceClassification""",
		    """Wav2Vec2ForXVector""",
		    """Wav2Vec2Model""",
		    """Wav2Vec2PreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Optional[Any]						=			[
		    """TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFWav2Vec2ForCTC""",
		    """TFWav2Vec2Model""",
		    """TFWav2Vec2PreTrainedModel""",
		    """TFWav2Vec2ForSequenceClassification""",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Dict						=			[
		    """FlaxWav2Vec2ForCTC""",
		    """FlaxWav2Vec2ForPreTraining""",
		    """FlaxWav2Vec2Model""",
		    """FlaxWav2Vec2PreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
		from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
		from .processing_wavaveca import WavaVecaProcessor
		from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_wavaveca import (
				    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    WavaVecaForAudioFrameClassification,
				    WavaVecaForCTC,
				    WavaVecaForMaskedLM,
				    WavaVecaForPreTraining,
				    WavaVecaForSequenceClassification,
				    WavaVecaForXVector,
				    WavaVecaModel,
				    WavaVecaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_wavaveca import (
				    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFWavaVecaForCTC,
				    TFWavaVecaForSequenceClassification,
				    TFWavaVecaModel,
				    TFWavaVecaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_wavaveca import (
				    FlaxWavaVecaForCTC,
				    FlaxWavaVecaForPreTraining,
				    FlaxWavaVecaModel,
				    FlaxWavaVecaPreTrainedModel,
				)


else:
		import sys

		__magic_name__       :					Optional[Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""


def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			0

						# if input_string is "aba" than new_input_string become "a|b|a"
						UpperCamelCase				:		str				   =			""""""
						UpperCamelCase				:		Dict				   =			""""""

						# append each character + "|" in new_string for range(0, length-1)
						for i in input_string[: len(SCREAMING_SNAKE_CASE     ) - 1]:
												new_input_string += i + "|"
						# append last character
						new_input_string += input_string[-1]

						# we will store the starting and ending of previous furthest ending palindromic
						# substring
						UpperCamelCase   ,						UpperCamelCase				:		int				   =			0, 0

						# length[i] shows the length of palindromic substring with center i
						UpperCamelCase				:		List[Any]				   =			[1 for i in range(len(SCREAMING_SNAKE_CASE     )     )]

						# for each character in new_string find corresponding palindromic string
						UpperCamelCase				:		Optional[Any]				   =			0
						for j in range(len(SCREAMING_SNAKE_CASE     )     ):
												UpperCamelCase				:		Union[str, Any]				   =			1 if j > r else min(length[l + r - j] // 2							,     r - j + 1     )
												while (
												    j - k >= 0
												    and j + k < len(SCREAMING_SNAKE_CASE     )
												    and new_input_string[k + j] == new_input_string[j - k]
												):
																		k += 1

												UpperCamelCase				:		int				   =			2 * k - 1

												# does this string is ending after the previously explored end (that is r) ?
												# if yes the update the new r to the last index of this
												if j + k - 1 > r:
																		UpperCamelCase				:		int				   =			j - k + 1  # noqa: E741
																		UpperCamelCase				:		List[Any]				   =			j + k - 1

												# update max_length and start position
												if max_length < length[j]:
																		UpperCamelCase				:		List[str]				   =			length[j]
																		UpperCamelCase				:		List[str]				   =			j

    # create that string
						UpperCamelCase				:		Optional[Any]				   =			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
						for i in s:
												if i != "|":
																		output_string += i

						return output_string


if __name__ == "__main__":
		import doctest

		doctest.testmod()



