
from typing import TYPE_CHECKING

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


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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[Any]    = [
					    '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:
					A     :	int    = [
					    '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

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

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

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







@dataclass(frozen=a			)
class 			__A( a			):
				snake_case_						   =    field(default='''language-modeling'''  ,   metadata={'''include_in_asdict_even_if_is_default''': True}			)

				snake_case_						   =    Features({'''text''': Value('''string'''			)}			)
				snake_case_						   =    Features({}			)
				snake_case_						   =    "text"




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


								'''simple docstring'''




								return {self.text_column: "text"}