





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





      def __call__(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       from .features import Value

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



@dataclass
class       A__     :
      _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=__snake_case ,     repr=__snake_case    )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def __call__(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




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

       # Convert dictionary into tuples, splitting out cases where there are
       # multiple translations for a single language.
       UpperCamelCase			:      List[str] 			=     []
       for lang, text in translation_dict.items():
        if isinstance(A_ ,							A_     ):
         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			:      Dict 			=     zip(*sorted(A_     )     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       from .features import Sequence, Value

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







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Optional[int]							=     ['image_processor', 'tokenizer']
      _UpperCAmelCase			:Tuple							=     'BlipImageProcessor'
      _UpperCAmelCase			:Optional[int]							=     'AutoTokenizer'





      def __init__(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     False
       super().__init__(A_ ,							A_     )
       UpperCamelCase			:      str 			=     self.image_processor





      def __call__(				self ,							A_ = None ,							A_ = None ,							A_ = True ,							A_ = False ,							A_ = None ,							A_ = None ,							A_ = 0 ,							A_ = None ,							A_ = None ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = True ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       if images is None and text is None:
        raise ValueError("You have to specify either images or text."     )

       # Get only text
       if images is None:
        UpperCamelCase			:      int 			=     self.tokenizer
        UpperCamelCase			:      Optional[int] 			=     self.tokenizer(
            text=A_ ,							add_special_tokens=A_ ,							padding=A_ ,							truncation=A_ ,							max_length=A_ ,							stride=A_ ,							pad_to_multiple_of=A_ ,							return_attention_mask=A_ ,							return_overflowing_tokens=A_ ,							return_special_tokens_mask=A_ ,							return_offsets_mapping=A_ ,							return_token_type_ids=A_ ,							return_length=A_ ,							verbose=A_ ,							return_tensors=A_ ,							**A_ ,							)
        return text_encoding

       # add pixel_values
       UpperCamelCase			:      int 			=     self.image_processor(A_ ,							return_tensors=A_     )

       if text is not None:
        UpperCamelCase			:      Dict 			=     self.tokenizer(
            text=A_ ,							add_special_tokens=A_ ,							padding=A_ ,							truncation=A_ ,							max_length=A_ ,							stride=A_ ,							pad_to_multiple_of=A_ ,							return_attention_mask=A_ ,							return_overflowing_tokens=A_ ,							return_special_tokens_mask=A_ ,							return_offsets_mapping=A_ ,							return_token_type_ids=A_ ,							return_length=A_ ,							verbose=A_ ,							return_tensors=A_ ,							**A_ ,							)
       else:
        UpperCamelCase			:      Dict 			=     None

       if text_encoding is not None:
        encoding_image_processor.update(A_     )

       return encoding_image_processor





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




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





      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.tokenizer.model_input_names
       UpperCamelCase			:      int 			=     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
