






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

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




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


						'''simple docstring'''




						A     :   str						=					field(default='audio-classification'					,  metadata={'include_in_asdict_even_if_is_default': True}					)
						A     :   ClassVar[Features]						=					Features({'audio': Audio()}					)
						A     :   ClassVar[Features]						=					Features({'labels': ClassLabel}					)
						A     :   str						=					"audio"
						A     :   str						=					"labels"






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								if self.label_column not in features:
										raise ValueError(f'''Column {self.label_column} is not present in features.'''				)
								if not isinstance(features[self.label_column]		,				_SCREAMING_SNAKE_CASE				):
										raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''				)
								snake_case_				:							Dict						 =       copy.deepcopy(self				)
								snake_case_				:							List[Any]						 =       self.label_schema.copy()
								snake_case_				:							Dict						 =       features[self.label_column]
								snake_case_				:							Optional[int]						 =       label_schema
								return task_template






						@property
						def       _lowerCAmelCase       (	self				) ->				Dict[str, str]:
								return {
								    self.audio_column: "audio",
								    self.label_column: "labels",
								}








from typing import TYPE_CHECKING

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


lowercase  :							Union[str, Any]			  =    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    ['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    [
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Dict			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
