


"""simple docstring"""


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A     :   int			    =			re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class   _UpperCamelCase  :
							'''simple docstring'''







							__UpperCAmelCase						:    List[Any]				      =4_2
							__UpperCAmelCase						:    Tuple				      =None
							__UpperCAmelCase						:    Optional[Any]				      =None
							__UpperCAmelCase						:    Any				      =None
							__UpperCAmelCase						:    List[str]				      =None

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			_str_to_version_tuple(self.version_str	)

							def __repr__(     self	):
										return f"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"

							@property
							def 			snake_case				(     self	):
										return self.major, self.minor, self.patch

							def 			snake_case				(     self    ,   __a	):
										if isinstance(__lowerCamelCase    ,   __lowerCamelCase	):
													return Version(__lowerCamelCase	)
										elif isinstance(__lowerCamelCase    ,   __lowerCamelCase	):
													return other
										raise TypeError(f"{other} (type {type(__lowerCamelCase	)}) cannot be compared to version."	)

							def __eq__(     self    ,   __a	):
										try:
													__lowerCAmelCase				    =			self._validate_operand(__lowerCamelCase	)
										except (TypeError, ValueError):
													return False
										else:
													return self.tuple == other.tuple

							def __lt__(     self    ,   __a	):
										__lowerCAmelCase				    =			self._validate_operand(__lowerCamelCase	)
										return self.tuple < other.tuple

							def __hash__(     self	):
										return hash(_version_tuple_to_str(self.tuple	)	)

							@classmethod
							def 			snake_case				(     cls    ,   __a	):
										__lowerCAmelCase				    =			{f.name for f in dataclasses.fields(cls	)}
										return cls(**{k: v for k, v in dic.items() if k in field_names}	)



							def 			snake_case				(     self	):
										return self.version_str


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			_VERSION_REG.match(UpperCamelCase__      )
			if not res:
						raise ValueError(f"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."      )
			return tuple(int(UpperCamelCase__      ) for v in [res.group("major"      ), res.group("minor"      ), res.group("patch"      )]      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return ".".join(str(UpperCamelCase__      ) for v in version_tuple      )








from typing import TYPE_CHECKING

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


lowerCAmelCase__      		= {
    '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__      		= ['SpeechT5Tokenizer']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= [
        '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__      		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
