






"""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 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =['''image_processor''', '''tokenizer''']
   SCREAMING_SNAKE_CASE_    ='''BridgeTowerImageProcessor'''
   SCREAMING_SNAKE_CASE_    =('''RobertaTokenizer''', '''RobertaTokenizerFast''')
   def __init__(		self      :	Any							,			snake_case__      :	Tuple							,			snake_case__      :	Optional[int]							):


        '''simple docstring'''
        super().__init__(snake_case__							,			snake_case__							)
   def __call__(		self      :	int							,			snake_case__      :	int							,			snake_case__      :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,			snake_case__      :	bool = True							,			snake_case__      :	Union[bool, str, PaddingStrategy] = False							,			snake_case__      :	Union[bool, str, TruncationStrategy] = None							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	int = 0							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	Optional[bool] = None							,			snake_case__      :	Optional[bool] = None							,			snake_case__      :	bool = False							,			snake_case__      :	bool = False							,			snake_case__      :	bool = False							,			snake_case__      :	bool = False							,			snake_case__      :	bool = True							,			snake_case__      :	Optional[Union[str, TensorType]] = None							,			**snake_case__      :	Dict							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							self.tokenizer(
            text=snake_case__							,			add_special_tokens=snake_case__							,			padding=snake_case__							,			truncation=snake_case__							,			max_length=snake_case__							,			stride=snake_case__							,			pad_to_multiple_of=snake_case__							,			return_token_type_ids=snake_case__							,			return_attention_mask=snake_case__							,			return_overflowing_tokens=snake_case__							,			return_special_tokens_mask=snake_case__							,			return_offsets_mapping=snake_case__							,			return_length=snake_case__							,			verbose=snake_case__							,			return_tensors=snake_case__							,			**snake_case__							,			)
        # add pixel_values + pixel_mask
        UpperCAmelCase__						:    List[str]							=							self.image_processor(
            snake_case__							,			return_tensors=snake_case__							,			do_normalize=snake_case__							,			do_center_crop=snake_case__							,			**snake_case__							)
        encoding.update(snake_case__							)

        return encoding
   def       __a							(		self      :	List[str]							,			*snake_case__      :	Dict							,			**snake_case__      :	str							):


        '''simple docstring'''
        return self.tokenizer.batch_decode(*snake_case__							,			**snake_case__							)
   def       __a							(		self      :	Tuple							,			*snake_case__      :	List[Any]							,			**snake_case__      :	Any							):


        '''simple docstring'''
        return self.tokenizer.decode(*snake_case__							,			**snake_case__							)




   @property
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							self.tokenizer.model_input_names
        UpperCAmelCase__						:    str							=							self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)










"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase	:	Tuple				=      {
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	int				=      ["""LxmertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	Tuple				=      [
     """LxmertEncoder""",
     """LxmertForPreTraining""",
     """LxmertForQuestionAnswering""",
     """LxmertModel""",
     """LxmertPreTrainedModel""",
     """LxmertVisualFeatureEncoder""",
     """LxmertXLayer""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	Optional[int]				=      [
     """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFLxmertForPreTraining""",
     """TFLxmertMainLayer""",
     """TFLxmertModel""",
     """TFLxmertPreTrainedModel""",
     """TFLxmertVisualFeatureEncoder""",
 ]


if TYPE_CHECKING:
 from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
 from .tokenization_lxmert import LxmertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_lxmert_fast import LxmertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_lxmert import (
      LxmertEncoder,
      LxmertForPreTraining,
      LxmertForQuestionAnswering,
      LxmertModel,
      LxmertPreTrainedModel,
      LxmertVisualFeatureEncoder,
      LxmertXLayer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_lxmert import (
      TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLxmertForPreTraining,
      TFLxmertMainLayer,
      TFLxmertModel,
      TFLxmertPreTrainedModel,
      TFLxmertVisualFeatureEncoder,
  )

else:
 import sys

 _lowerCAmelCase	:	Any				=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


