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		(   UpperCamelCase_    ):
				__a  =      ["image_processor", "tokenizer"]
				__a  =      "BridgeTowerImageProcessor"
				__a  =      ("RobertaTokenizer", "RobertaTokenizerFast")
				def __init__(  self , lowerCAmelCase , lowerCAmelCase   )       ->					List[Any]:
					super().__init__(lowerCAmelCase , lowerCAmelCase   )
				def __call__(  self , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = True , lowerCAmelCase = False , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = 0 , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = False , lowerCAmelCase = False , lowerCAmelCase = False , lowerCAmelCase = False , lowerCAmelCase = True , lowerCAmelCase = None , **lowerCAmelCase , )       ->					BatchEncoding:
					SCREAMING_SNAKE_CASE__:   int=      self.tokenizer(
					    text=lowerCAmelCase , add_special_tokens=lowerCAmelCase , padding=lowerCAmelCase , truncation=lowerCAmelCase , max_length=lowerCAmelCase , stride=lowerCAmelCase , pad_to_multiple_of=lowerCAmelCase , return_token_type_ids=lowerCAmelCase , return_attention_mask=lowerCAmelCase , return_overflowing_tokens=lowerCAmelCase , return_special_tokens_mask=lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , return_length=lowerCAmelCase , verbose=lowerCAmelCase , return_tensors=lowerCAmelCase , **lowerCAmelCase , )
					# add pixel_values + pixel_mask
					SCREAMING_SNAKE_CASE__:   int=      self.image_processor(
					    lowerCAmelCase , return_tensors=lowerCAmelCase , do_normalize=lowerCAmelCase , do_center_crop=lowerCAmelCase , **lowerCAmelCase   )
					encoding.update(lowerCAmelCase   )

					return encoding
				def 						UpperCamelCase_				(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					str:
					return self.tokenizer.batch_decode(*lowerCAmelCase , **lowerCAmelCase   )
				def 						UpperCamelCase_				(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					int:
					return self.tokenizer.decode(*lowerCAmelCase , **lowerCAmelCase   )



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


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_     :   List[Any] 				=				logging.get_logger(__name__)

lowercase_     :   List[Any] 				=				{
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      "xlm-roberta-xl"
    def __init__(  self , lowerCAmelCase=250880 , lowerCAmelCase=2560 , lowerCAmelCase=36 , lowerCAmelCase=32 , lowerCAmelCase=10240 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=514 , lowerCAmelCase=1 , lowerCAmelCase=0.02 , lowerCAmelCase=1e-05 , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=2 , lowerCAmelCase="absolute" , lowerCAmelCase=True , lowerCAmelCase=None , **lowerCAmelCase , )       ->					Union[str, Any]:
     super().__init__(pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      vocab_size
     SCREAMING_SNAKE_CASE__:   Dict=      hidden_size
     SCREAMING_SNAKE_CASE__:   Tuple=      num_hidden_layers
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      num_attention_heads
     SCREAMING_SNAKE_CASE__:   int=      hidden_act
     SCREAMING_SNAKE_CASE__:   Any=      intermediate_size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__:   str=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__:   List[Any]=      max_position_embeddings
     SCREAMING_SNAKE_CASE__:   Dict=      type_vocab_size
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      initializer_range
     SCREAMING_SNAKE_CASE__:   Dict=      layer_norm_eps
     SCREAMING_SNAKE_CASE__:   Dict=      position_embedding_type
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      use_cache
     SCREAMING_SNAKE_CASE__:   str=      classifier_dropout






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    @property
    def 						UpperCamelCase_				(  self   )       ->					Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
      SCREAMING_SNAKE_CASE__:   Dict=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
      SCREAMING_SNAKE_CASE__:   List[str]=      {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]   )

