




'''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    :
     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=__lowerCAmelCase , repr=__lowerCAmelCase							)






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






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

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







@dataclass
class        _a    :
     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=__lowerCAmelCase , repr=__lowerCAmelCase							)






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				sorted(set(self.languages   )   ) if self.languages else None
          _snake_case							=				len(self.languages   ) if self.languages else None






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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          _snake_case							=				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.
          _snake_case							=				[]
          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.
          _snake_case							, _snake_case							=				zip(*sorted(_SCREAMING_SNAKE_CASE   )   )

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






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

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








'''simple docstring'''





from typing import List, Optional, Union

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

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Any       =						["""image_processor""", """tokenizer"""]
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						"""BridgeTowerImageProcessor"""
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          super().__init__(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def __call__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 0 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      BatchEncoding:
          _snake_case							=				self.tokenizer(
              text=_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ,pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,return_token_type_ids=_SCREAMING_SNAKE_CASE ,return_attention_mask=_SCREAMING_SNAKE_CASE ,return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,return_offsets_mapping=_SCREAMING_SNAKE_CASE ,return_length=_SCREAMING_SNAKE_CASE ,verbose=_SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
          # add pixel_values + pixel_mask
          _snake_case							=				self.image_processor(
              _SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,do_normalize=_SCREAMING_SNAKE_CASE ,do_center_crop=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )
          encoding.update(_SCREAMING_SNAKE_CASE   )

          return encoding






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     @property
     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				self.tokenizer.model_input_names
          _snake_case							=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )


