

from typing import TYPE_CHECKING

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


lowerCAmelCase							:  str   	=						{
    """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							:  Optional[int]   	=						["""SpeechT5Tokenizer"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  int   	=						[
						    """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							:  str   	=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate



@dataclass(frozen=UpperCAmelCase_	)
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							str										=     field(default='''automatic-speech-recognition'''		,       metadata={'''include_in_asdict_even_if_is_default''': True}	)
			_UpperCAmelCase   :							ClassVar[Features]										=     Features({'''audio''': Audio()}	)
			_UpperCAmelCase   :							ClassVar[Features]										=     Features({'''transcription''': Value('''string'''	)}	)
			_UpperCAmelCase   :							str										=     "audio"
			_UpperCAmelCase   :							str										=     "transcription"



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									if self.audio_column not in features:
															raise ValueError(F"Column {self.audio_column} is not present in features.")
									if not isinstance(features[self.audio_column]   ,						lowerCAmelCase__):
															raise ValueError(F"Column {self.audio_column} is not an Audio type.")
									SCREAMING_SNAKE_CASE_: Tuple  		=       copy.deepcopy(self)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.input_schema.copy()
									SCREAMING_SNAKE_CASE_: Dict  		=       features[self.audio_column]
									SCREAMING_SNAKE_CASE_: int  		=       input_schema
									return task_template




			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return {self.audio_column: "audio", self.transcription_column: "transcription"}






