






'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCamelCase_				: List[Any]    	=							{
    """configuration_blenderbot""": [
        """BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlenderbotConfig""",
        """BlenderbotOnnxConfig""",
    ],
    """tokenization_blenderbot""": ["""BlenderbotTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: List[str]    	=							["""BlenderbotTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Dict    	=							[
					    """BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BlenderbotForCausalLM""",
					    """BlenderbotForConditionalGeneration""",
					    """BlenderbotModel""",
					    """BlenderbotPreTrainedModel""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Any    	=							[
					    """TFBlenderbotForConditionalGeneration""",
					    """TFBlenderbotModel""",
					    """TFBlenderbotPreTrainedModel""",
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Dict    	=							[
					    """FlaxBlenderbotForConditionalGeneration""",
					    """FlaxBlenderbotModel""",
					    """FlaxBlenderbotPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_blenderbot import (
					    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BlenderbotConfig,
					    BlenderbotOnnxConfig,
					)
					from .tokenization_blenderbot import BlenderbotTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blenderbot import (
										    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlenderbotForCausalLM,
										    BlenderbotForConditionalGeneration,
										    BlenderbotModel,
										    BlenderbotPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blenderbot import (
										    TFBlenderbotForConditionalGeneration,
										    TFBlenderbotModel,
										    TFBlenderbotPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_blenderbot import (
										    FlaxBlenderbotForConditionalGeneration,
										    FlaxBlenderbotModel,
										    FlaxBlenderbotPreTrainedModel,
										)

else:
					import sys

					lowerCamelCase_				: str    	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)














'''simple docstring'''


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    _SCREAMING_SNAKE_CASE							(		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 				A      (   self							:						List[Any] ,      lowercase							:						List[Any]			)							->    Any:

									'''simple docstring'''







									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] ,      lowercase			):
															raise ValueError(f"Column {self.label_column} is not a ClassLabel."			)
									UpperCamelCase__         = copy.deepcopy(self			)
									UpperCamelCase__         = self.label_schema.copy()
									UpperCamelCase__         = features[self.label_column]
									UpperCamelCase__         = label_schema
									return task_template







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

									'''simple docstring'''







									return {
									    self.audio_column: "audio",
									    self.label_column: "labels",
									}
