






'''simple docstring'''

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        a_		:
  lowercase  	=   42
  lowercase  	=   None
  # Automatically constructed
  lowercase  	=   "dict"
  lowercase  	=   None
  lowercase  	=   field(default="""Translation"""			, init=lowerCamelCase			, repr=lowerCamelCase       )

  def __call__(   self   )						->   List[Any]:



        """simple docstring"""






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


  def 						A__		(   self   )						->   Union["FeatureType", Dict[str, "FeatureType"]]:



        """simple docstring"""






        from .features import Value

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






@dataclass
class        a_		:
  lowercase  	=   None
  lowercase  	=   None
  lowercase  	=   None
  # Automatically constructed
  lowercase  	=   "dict"
  lowercase  	=   None
  lowercase  	=   field(default="""TranslationVariableLanguages"""			, init=lowerCamelCase			, repr=lowerCamelCase       )

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      sorted(set(self.languages   )   ) if self.languages else None
        UpperCamelCase       						=      len(self.languages   ) if self.languages else None

  def __call__(   self   )						->   List[Any]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      set(self.languages   )
        if self.languages and set(_SCREAMING_SNAKE_CASE   ) - lang_set:
              raise ValueError(
                  F"Some languages in example ({', '.join(sorted(set(_SCREAMING_SNAKE_CASE   ) - lang_set   )   )}) are not in valid set ({', '.join(_SCREAMING_SNAKE_CASE   )})."   )

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

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

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


  def 						A__		(   self   )						->   Union["FeatureType", Dict[str, "FeatureType"]]:



        """simple docstring"""






        from .features import Sequence, Value

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








'''simple docstring'''

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__											=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__											=     [
         'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'SwiftFormerForImageClassification',
         'SwiftFormerModel',
         'SwiftFormerPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_swiftformer import (
         SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SwiftFormerConfig,
         SwiftFormerOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swiftformer import (
              SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwiftFormerForImageClassification,
              SwiftFormerModel,
              SwiftFormerPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE__											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
