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        lowercase_   :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		None
							# Automatically constructed
							_lowerCamelCase						=		'dict'
							_lowerCamelCase						=		None
							_lowerCamelCase						=		field(default='Translation'						,			init=__UpperCamelCase						,			repr=__UpperCamelCase		)



							def __call__(						self    ):
													return pa.struct({lang: pa.string() for lang in sorted(self.languages    )}    )



							def 	UpperCamelCase	(						self    ):
													from .features import Value

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




@dataclass
class        lowercase_   :
							_lowerCamelCase						=		None
							_lowerCamelCase						=		None
							_lowerCamelCase						=		None
							# Automatically constructed
							_lowerCamelCase						=		'dict'
							_lowerCamelCase						=		None
							_lowerCamelCase						=		field(default='TranslationVariableLanguages'						,			init=__UpperCamelCase						,			repr=__UpperCamelCase		)



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   sorted(set(self.languages    )    ) if self.languages else None
													_snake_case						:					List[str]    =   len(self.languages    ) if self.languages else None



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



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					Optional[int]    =   set(self.languages    )
													if self.languages and set(lowercase_    ) - lang_set:
																			raise ValueError(
																			    f"""Some languages in example ({', '.join(sorted(set(lowercase_    ) - lang_set    )    )}) are not in valid set ({', '.join(lowercase_    )})."""    )

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

        # Ensure translations are in ascending order by language code.
													_snake_case						:					List[Any]    =   zip(*sorted(lowercase_    )    )

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



							def 	UpperCamelCase	(						self    ):
													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_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[int]         =			['DistilBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   int         =			[
				    'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DistilBertForMaskedLM',
				    'DistilBertForMultipleChoice',
				    'DistilBertForQuestionAnswering',
				    'DistilBertForSequenceClassification',
				    'DistilBertForTokenClassification',
				    'DistilBertModel',
				    'DistilBertPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Any         =			[
				    'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFDistilBertForMaskedLM',
				    'TFDistilBertForMultipleChoice',
				    'TFDistilBertForQuestionAnswering',
				    'TFDistilBertForSequenceClassification',
				    'TFDistilBertForTokenClassification',
				    'TFDistilBertMainLayer',
				    'TFDistilBertModel',
				    'TFDistilBertPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'FlaxDistilBertForMaskedLM',
				    'FlaxDistilBertForMultipleChoice',
				    'FlaxDistilBertForQuestionAnswering',
				    'FlaxDistilBertForSequenceClassification',
				    'FlaxDistilBertForTokenClassification',
				    'FlaxDistilBertModel',
				    'FlaxDistilBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_distilbert import (
				    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    DistilBertConfig,
				    DistilBertOnnxConfig,
				)
				from .tokenization_distilbert import DistilBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_distilbert_fast import DistilBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_distilbert import (
								    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DistilBertForMaskedLM,
								    DistilBertForMultipleChoice,
								    DistilBertForQuestionAnswering,
								    DistilBertForSequenceClassification,
								    DistilBertForTokenClassification,
								    DistilBertModel,
								    DistilBertPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_distilbert import (
								    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDistilBertForMaskedLM,
								    TFDistilBertForMultipleChoice,
								    TFDistilBertForQuestionAnswering,
								    TFDistilBertForSequenceClassification,
								    TFDistilBertForTokenClassification,
								    TFDistilBertMainLayer,
								    TFDistilBertModel,
								    TFDistilBertPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_distilbert import (
								    FlaxDistilBertForMaskedLM,
								    FlaxDistilBertForMultipleChoice,
								    FlaxDistilBertForQuestionAnswering,
								    FlaxDistilBertForSequenceClassification,
								    FlaxDistilBertForTokenClassification,
								    FlaxDistilBertModel,
								    FlaxDistilBertPreTrainedModel,
								)

else:
				import sys

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