'''simple docstring'''





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

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




@dataclass(frozen=SCREAMING_SNAKE_CASE	)
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							field(default='''automatic-speech-recognition'''   , metadata={'''include_in_asdict_even_if_is_default''': True}	)
				UpperCAmelCase__							=							Features({'''audio''': Audio()}	)
				UpperCAmelCase__							=							Features({'''transcription''': Value('''string'''	)}	)
				UpperCAmelCase__							=							"audio"
				UpperCAmelCase__							=							"transcription"





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						List[str]					)							->List[str]:


										'''simple docstring'''




										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]       ,		lowercase__					):
																raise ValueError(f'''Column {self.audio_column} is not an Audio type.'''					)
										_UpperCamelCase			:			Tuple   			=						copy.deepcopy(self					)
										_UpperCamelCase			:			Dict   			=						self.input_schema.copy()
										_UpperCamelCase			:			Union[str, Any]   			=						features[self.audio_column]
										_UpperCamelCase			:			Dict   			=						input_schema
										return task_template







				@property
				def  snake_case__				(						self					:						Tuple					)							->Dict[str, str]:


										'''simple docstring'''




										return {self.audio_column: "audio", self.transcription_column: "transcription"}



'''simple docstring'''





from typing import TYPE_CHECKING

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


lowerCAmelCase_	:		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:
			lowerCAmelCase_	:		int			=     ["""Speech2TextTokenizer"""]

try:
			if not is_speech_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		Optional[Any]			=     ["""Speech2TextFeatureExtractor"""]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		Optional[int]			=     [
			    """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFSpeech2TextForConditionalGeneration""",
			    """TFSpeech2TextModel""",
			    """TFSpeech2TextPreTrainedModel""",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		str			=     [
			    """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

			lowerCAmelCase_	:		Union[str, Any]			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


