




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


snake_case_  :      Optional[Any]		=		{
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_  :      Optional[int]		=		["Speech2TextTokenizer"]

try:
	if not is_speech_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_  :      int		=		["Speech2TextFeatureExtractor"]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_  :      List[Any]		=		[
	    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFSpeech2TextForConditionalGeneration",
	    "TFSpeech2TextModel",
	    "TFSpeech2TextPreTrainedModel",
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_  :      Any		=		[
	    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "Speech2TextForConditionalGeneration",
	    "Speech2TextModel",
	    "Speech2TextPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
	from .processing_speech_to_text import SpeechaTextProcessor

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_speech_to_text import SpeechaTextTokenizer

	try:
		if not is_speech_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_speech_to_text import (
		    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSpeechaTextForConditionalGeneration,
		    TFSpeechaTextModel,
		    TFSpeechaTextPreTrainedModel,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_to_text import (
		    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SpeechaTextForConditionalGeneration,
		    SpeechaTextModel,
		    SpeechaTextPreTrainedModel,
		)

else:
	import sys

	snake_case_  :      Dict		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''






def 					_a						(       _lowerCamelCase ) ->				int:



			"""simple docstring"""


			if not isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
						raise TypeError("""only integers accepted as input""" )
			else:
						__snake_case					:      List[Any]							      =     str(abs(_lowerCamelCase ) )
						__snake_case					:      Union[str, Any]							      =     [list(_lowerCamelCase ) for char in range(len(_lowerCamelCase ) )]
						for index in range(len(_lowerCamelCase ) ):
									num_transpositions[index].pop(_lowerCamelCase )
						return max(
						    int("""""".join(list(_lowerCamelCase ) ) ) for transposition in num_transpositions )


if __name__ == "__main__":
	__import__("doctest").testmod()


