


"""simple docstring"""






from typing import TYPE_CHECKING

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


__snake_case							:     List[Any]   =					{
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:     str   =					['SpeechT5Tokenizer']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:     Optional[Any]   =					[
	    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SpeechT5ForSpeechToText',
	    'SpeechT5ForSpeechToSpeech',
	    'SpeechT5ForTextToSpeech',
	    'SpeechT5Model',
	    'SpeechT5PreTrainedModel',
	    'SpeechT5HifiGan',
	]

if TYPE_CHECKING:
	from .configuration_speechta import (
	    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
	    SpeechTaConfig,
	    SpeechTaHifiGanConfig,
	)
	from .feature_extraction_speechta import SpeechTaFeatureExtractor
	from .processing_speechta import SpeechTaProcessor

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_speechta import SpeechTaTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speechta import (
		    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SpeechTaForSpeechToSpeech,
		    SpeechTaForSpeechToText,
		    SpeechTaForTextToSpeech,
		    SpeechTaHifiGan,
		    SpeechTaModel,
		    SpeechTaPreTrainedModel,
		)

else:
	import sys

	__snake_case							:     str   =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""






import re

from filelock import FileLock


try:
	import nltk

	__snake_case							:     Any   =					True
except (ImportError, ModuleNotFoundError):
	__snake_case							:     Optional[int]   =					False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)


def     _lowercase       (						__snake_case			)		->     str:
			re.sub("<n>"      ,""      ,__snake_case			)  # remove pegasus newline char
			assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
			return "\n".join(nltk.sent_tokenize(__snake_case			)			)