





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 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Optional[Any]								=	["image_processor", "tokenizer"]
   a		:							Any								=	"BridgeTowerImageProcessor"
   a		:							List[str]								=	("RobertaTokenizer", "RobertaTokenizerFast")




   def __init__(					self,	__magic_name__,	__magic_name__							)				->   List[Any]:






       """simple docstring"""
       super().__init__(__magic_name__,	__magic_name__							)




   def __call__(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	__magic_name__ = False,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = 0,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = False,	__magic_name__ = False,	__magic_name__ = False,	__magic_name__ = False,	__magic_name__ = True,	__magic_name__ = None,	**__magic_name__,	)				->   BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   self.tokenizer(
           text=__magic_name__,	add_special_tokens=__magic_name__,	padding=__magic_name__,	truncation=__magic_name__,	max_length=__magic_name__,	stride=__magic_name__,	pad_to_multiple_of=__magic_name__,	return_token_type_ids=__magic_name__,	return_attention_mask=__magic_name__,	return_overflowing_tokens=__magic_name__,	return_special_tokens_mask=__magic_name__,	return_offsets_mapping=__magic_name__,	return_length=__magic_name__,	verbose=__magic_name__,	return_tensors=__magic_name__,	**__magic_name__,	)
       # add pixel_values + pixel_mask
       UpperCamelCase__							:       Optional[int]      =   self.image_processor(
           __magic_name__,	return_tensors=__magic_name__,	do_normalize=__magic_name__,	do_center_crop=__magic_name__,	**__magic_name__							)
       encoding.update(__magic_name__							)

       return encoding




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   int:






       """simple docstring"""
       return self.tokenizer.batch_decode(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   List[str]:






       """simple docstring"""
       return self.tokenizer.decode(*__magic_name__,	**__magic_name__							)




   @property
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   self.tokenizer.model_input_names
       UpperCamelCase__							:       List[Any]      =   self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)








import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


UpperCAmelCase_  							=				logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__       )
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							bool     	=	field(default=__lowerCamelCase    ,       metadata={"help": "Whether to use SortishSampler or not."}       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[Union[str, Path, GenerationConfig]]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }    ,       )




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   super().to_dict()
       for k, v in d.items():
           if isinstance(__magic_name__,	__magic_name__							):
               UpperCamelCase__							:       Tuple      =   v.to_dict()
       return d

