



'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCAmelCase	:		Optional[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:
						lowerCAmelCase	:		str				=   ['SpeechT5Tokenizer']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase	:		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

						lowerCAmelCase	:		Optional[int]				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCAmelCase	:		Optional[Any]				=   {
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TrOCRForCausalLM''',
	    '''TrOCRPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
	from .processing_trocr import TrOCRProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
	import sys

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