'''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 lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   ["image_processor", "tokenizer"]
   __lowerCAmelCase         =   "BridgeTowerImageProcessor"
   __lowerCAmelCase         =   ("RobertaTokenizer", "RobertaTokenizerFast")
   def __init__(      self      :			Any, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			str     )  ->			int:
        super().__init__(UpperCamelCase__, UpperCamelCase__     )
   def __call__(      self      :			List[Any], UpperCamelCase__      :			List[str], UpperCamelCase__      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, UpperCamelCase__      :			bool = True, UpperCamelCase__      :			Union[bool, str, PaddingStrategy] = False, UpperCamelCase__      :			Union[bool, str, TruncationStrategy] = None, UpperCamelCase__      :			Optional[int] = None, UpperCamelCase__      :			int = 0, UpperCamelCase__      :			Optional[int] = None, UpperCamelCase__      :			Optional[bool] = None, UpperCamelCase__      :			Optional[bool] = None, UpperCamelCase__      :			bool = False, UpperCamelCase__      :			bool = False, UpperCamelCase__      :			bool = False, UpperCamelCase__      :			bool = False, UpperCamelCase__      :			bool = True, UpperCamelCase__      :			Optional[Union[str, TensorType]] = None, **UpperCamelCase__      :			Any, )  ->			BatchEncoding:
        _A    		=  self.tokenizer(
            text=UpperCamelCase__, add_special_tokens=UpperCamelCase__, padding=UpperCamelCase__, truncation=UpperCamelCase__, max_length=UpperCamelCase__, stride=UpperCamelCase__, pad_to_multiple_of=UpperCamelCase__, return_token_type_ids=UpperCamelCase__, return_attention_mask=UpperCamelCase__, return_overflowing_tokens=UpperCamelCase__, return_special_tokens_mask=UpperCamelCase__, return_offsets_mapping=UpperCamelCase__, return_length=UpperCamelCase__, verbose=UpperCamelCase__, return_tensors=UpperCamelCase__, **UpperCamelCase__, )
        # add pixel_values + pixel_mask
        _A    		=  self.image_processor(
            UpperCamelCase__, return_tensors=UpperCamelCase__, do_normalize=UpperCamelCase__, do_center_crop=UpperCamelCase__, **UpperCamelCase__     )
        encoding.update(UpperCamelCase__     )

        return encoding
   def 	__UpperCAmelCase   (      self      :			Tuple, *UpperCamelCase__      :			Tuple, **UpperCamelCase__      :			Optional[int]     )  ->			List[Any]:
        return self.tokenizer.batch_decode(*UpperCamelCase__, **UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[Any], *UpperCamelCase__      :			Union[str, Any], **UpperCamelCase__      :			Optional[int]     )  ->			str:
        return self.tokenizer.decode(*UpperCamelCase__, **UpperCamelCase__     )

   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			int:
        _A    		=  self.tokenizer.model_input_names
        _A    		=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )










"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


a_					=     logging.get_logger(__name__)






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 def __init__(       self   ,		*__lowerCamelCase   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     warnings.warn(
         '''The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
         ''' Please use PoolFormerImageProcessor instead.'''   ,		__lowerCamelCase   ,		)
     super().__init__(*__lowerCamelCase   ,		**__lowerCamelCase    )




