




from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
		from .features import FeatureType




@dataclass
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[str]
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					None
				# Automatically constructed
				SCREAMING_SNAKE_CASE_       :							ClassVar[str]							 =					"dict"
				SCREAMING_SNAKE_CASE_       :							ClassVar[Any]							 =					None
				SCREAMING_SNAKE_CASE_       :							str							 =					field(default="""Translation"""    ,       init=_UpperCamelCase    ,       repr=_UpperCamelCase   )





				def __call__(      self				:     Any							)					->     Union[str, Any]:
							return pa.struct({lang: pa.string() for lang in sorted(self.languages							)}							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Union["FeatureType", Dict[str, "FeatureType"]]:
							from .features import Value

							return {k: Value("string"							) for k in sorted(self.languages							)}




@dataclass
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[List]							 =					None
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					None
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					None
				# Automatically constructed
				SCREAMING_SNAKE_CASE_       :							ClassVar[str]							 =					"dict"
				SCREAMING_SNAKE_CASE_       :							ClassVar[Any]							 =					None
				SCREAMING_SNAKE_CASE_       :							str							 =					field(default="""TranslationVariableLanguages"""    ,       init=_UpperCamelCase    ,       repr=_UpperCamelCase   )





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[Any]:
							a    =			sorted(set(self.languages							)							) if self.languages else None
							a    =			len(self.languages							) if self.languages else None





				def __call__(      self				:     str							)					->     Dict:
							return pa.struct({"language": pa.list_(pa.string()							), "translation": pa.list_(pa.string()							)}							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     Tuple							)					->     Dict:
							a    =			set(self.languages							)
							if self.languages and set(__lowerCamelCase							) - lang_set:
										raise ValueError(
										    f"""Some languages in example ({", ".join(sorted(set(__lowerCamelCase							) - lang_set							)							)}) are not in valid set ({", ".join(__lowerCamelCase							)})."""							)

							# Convert dictionary into tuples, splitting out cases where there are
							# multiple translations for a single language.
							a    =			[]
							for lang, text in translation_dict.items():
										if isinstance(__lowerCamelCase					,      __lowerCamelCase							):
													translation_tuples.append((lang, text)							)
										else:
													translation_tuples.extend([(lang, el) for el in text]							)

        # Ensure translations are in ascending order by language code.
							a    ,		a    =			zip(*sorted(__lowerCamelCase							)							)

							return {"language": languages, "translation": translations}





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Union["FeatureType", Dict[str, "FeatureType"]]:
							from .features import Sequence, Value

							return {
							    "language": Sequence(Value("string"							)							),
							    "translation": Sequence(Value("string"							)							),
							}












from typing import TYPE_CHECKING

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


__lowerCAmelCase		:				List[str]       			=		{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Dict       			=		[
		    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ResNetForImageClassification',
		    'ResNetModel',
		    'ResNetPreTrainedModel',
		    'ResNetBackbone',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				int       			=		[
		    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFResNetForImageClassification',
		    'TFResNetModel',
		    'TFResNetPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Optional[Any]       			=		[
		    'FlaxResNetForImageClassification',
		    'FlaxResNetModel',
		    'FlaxResNetPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		__lowerCAmelCase		:				int       			=		_LazyModule(__name__, globals()['__file__'], _import_structure)






