'''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 				_lowercase	:
      '''simple docstring'''


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




      def __call__(				self							)			->			str:



         '''simple docstring'''



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




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



         '''simple docstring'''



         from .features import Value

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




@dataclass
class 				_lowercase	:
      '''simple docstring'''


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




      def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  sorted(set(self.languages							)							) if self.languages else None
         UpperCAmelCase__							:				List[Any]   				=  len(self.languages							) if self.languages else None




      def __call__(				self							)			->			int:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  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.
         UpperCAmelCase__							:				Union[str, Any]   				=  []
         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.
         UpperCAmelCase__							:				int   				=  zip(*sorted(lowerCamelCase_							)							)

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




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



         '''simple docstring'''



         from .features import Sequence, Value

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





'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   ['''image_processor''', '''tokenizer''']
      UpperCAmelCase_      :      str      							=   '''CLIPImageProcessor'''
      UpperCAmelCase_      :      Dict      							=   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')




      def __init__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''					,lowerCamelCase_					,)
            UpperCAmelCase__							:				str   				=  kwargs.pop('''feature_extractor'''							)

         UpperCAmelCase__							:				Optional[Any]   				=  image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''							)
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''							)

         super().__init__(lowerCamelCase_					,lowerCamelCase_							)




      def __call__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''							)

         if text is not None:
            UpperCAmelCase__							:				Tuple   				=  self.tokenizer(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

         if images is not None:
            UpperCAmelCase__							:				Optional[int]   				=  self.image_processor(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

         if text is not None and images is not None:
            UpperCAmelCase__							:				str   				=  image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**lowerCamelCase_							)					,tensor_type=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         return self.tokenizer.batch_decode(*lowerCamelCase_					,**lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         return self.tokenizer.decode(*lowerCamelCase_					,**lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.tokenizer.model_input_names
         UpperCAmelCase__							:				Optional[Any]   				=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,lowerCamelCase_					,)
         return self.image_processor_class




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,lowerCamelCase_					,)
         return self.image_processor




