





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     __snake_case       (							UpperCamelCase__			):





       '''simple docstring'''







       lowerCAmelCase__      		=     ["""image_processor""", """tokenizer"""]
       lowerCAmelCase__      		=     """BridgeTowerImageProcessor"""
       lowerCAmelCase__      		=     ("""RobertaTokenizer""", """RobertaTokenizerFast""")




       def __init__(  self :    Tuple							,				A :    List[str]							,				A :    Optional[int]		):
         super().__init__(lowerCAmelCase__							,				lowerCAmelCase__		)




       def __call__(  self :    Optional[int]							,				A :    Tuple							,				A :    List[str] = None							,				A :    Optional[Any] = True							,				A :    Tuple = False							,				A :    Optional[int] = None							,				A :    Union[str, Any] = None							,				A :    Any = 0							,				A :    Any = None							,				A :    Optional[int] = None							,				A :    str = None							,				A :    Optional[int] = False							,				A :    Optional[int] = False							,				A :    Optional[Any] = False							,				A :    int = False							,				A :    Tuple = True							,				A :    str = None							,				**A :    Dict							,				):
         __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
         __snake_case:						str   =						self.image_processor(
             lowerCAmelCase__							,				return_tensors=lowerCAmelCase__							,				do_normalize=lowerCAmelCase__							,				do_center_crop=lowerCAmelCase__							,				**lowerCAmelCase__		)
         encoding.update(lowerCAmelCase__		)

         return encoding




       def 							UpperCAmelCase__ (  self :    Tuple							,				*A :    int							,				**A :    Tuple		):
         return self.tokenizer.batch_decode(*lowerCAmelCase__							,				**lowerCAmelCase__		)




       def 							UpperCAmelCase__ (  self :    List[str]							,				*A :    Optional[Any]							,				**A :    Union[str, Any]		):
         return self.tokenizer.decode(*lowerCAmelCase__							,				**lowerCAmelCase__		)






       @property
       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						str   =						self.tokenizer.model_input_names
         __snake_case:						Union[str, Any]   =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)













from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase	:	Optional[Any]		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Optional[int]		     =       {
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					List[Any]						=			"""canine"""







      def __init__(    self		,      lowerCAmelCase__=7_6_8		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=3_0_7_2		,      lowerCAmelCase__="gelu"		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=1_6_3_8_4		,      lowerCAmelCase__=1_6		,      lowerCAmelCase__=0.02		,      lowerCAmelCase__=1E-12		,      lowerCAmelCase__=0		,      lowerCAmelCase__=0XE0_00		,      lowerCAmelCase__=0XE0_01		,      lowerCAmelCase__=4		,      lowerCAmelCase__=4		,      lowerCAmelCase__=8		,      lowerCAmelCase__=1_6_3_8_4		,      lowerCAmelCase__=1_2_8		,      **lowerCAmelCase__		,      )   ->						Dict:



        '''simple docstring'''




        super().__init__(pad_token_id=lowerCAmelCase__		,      bos_token_id=lowerCAmelCase__		,      eos_token_id=lowerCAmelCase__		,      **lowerCAmelCase__							)

        a__	:					Optional[int]										=max_position_embeddings
        a__	:					str										=hidden_size
        a__	:					Optional[Any]										=num_hidden_layers
        a__	:					Tuple										=num_attention_heads
        a__	:					Optional[Any]										=intermediate_size
        a__	:					Optional[int]										=hidden_act
        a__	:					List[Any]										=hidden_dropout_prob
        a__	:					Union[str, Any]										=attention_probs_dropout_prob
        a__	:					Optional[Any]										=initializer_range
        a__	:					Union[str, Any]										=type_vocab_size
        a__	:					Optional[int]										=layer_norm_eps

        # Character config:
        a__	:					int										=downsampling_rate
        a__	:					Optional[Any]										=upsampling_kernel_size
        a__	:					Union[str, Any]										=num_hash_functions
        a__	:					Any										=num_hash_buckets
        a__	:					int										=local_transformer_stride






