



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowercase     							=						{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowercase     							=						[
        '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''GPTNeoForCausalLM''',
        '''GPTNeoForQuestionAnswering''',
        '''GPTNeoForSequenceClassification''',
        '''GPTNeoForTokenClassification''',
        '''GPTNeoModel''',
        '''GPTNeoPreTrainedModel''',
        '''load_tf_weights_in_gpt_neo''',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowercase     							=						[
        '''FlaxGPTNeoForCausalLM''',
        '''FlaxGPTNeoModel''',
        '''FlaxGPTNeoPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_neo import (
            GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTNeoForCausalLM,
            GPTNeoForQuestionAnswering,
            GPTNeoForSequenceClassification,
            GPTNeoForTokenClassification,
            GPTNeoModel,
            GPTNeoPreTrainedModel,
            load_tf_weights_in_gpt_neo,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
    import sys

    _lowercase     							=						_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=A_      )
class 				__A					( A_      ):
			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        _snake_case    (self							,    __magic_name__				):
						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]							,    __magic_name__				):
									raise ValueError(f"Column {self.audio_column} is not an Audio type."				)
						lowerCamelCase__ :      int								=		copy.deepcopy(self				)
						lowerCamelCase__ :      Tuple								=		self.input_schema.copy()
						lowerCamelCase__ :      Tuple								=		features[self.audio_column]
						lowerCamelCase__ :      List[str]								=		input_schema
						return task_template





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



