






'''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  snake_case__      (					UpperCamelCase):
					a_        =					["image_processor", "tokenizer"]
					a_        =					"BridgeTowerImageProcessor"
					a_        =					("RobertaTokenizer", "RobertaTokenizerFast")




					def __init__(  self  :		Any				, _A  :		Tuple				, _A  :		Optional[int]      )    ->							Optional[int]:
								super().__init__(snake_case__				, snake_case__      )




					def __call__(  self  :		int				, _A  :		int				, _A  :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				, _A  :		bool = True				, _A  :		Union[bool, str, PaddingStrategy] = False				, _A  :		Union[bool, str, TruncationStrategy] = None				, _A  :		Optional[int] = None				, _A  :		int = 0				, _A  :		Optional[int] = None				, _A  :		Optional[bool] = None				, _A  :		Optional[bool] = None				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = True				, _A  :		Optional[Union[str, TensorType]] = None				, **_A  :		Dict				, )    ->							Optional[Any]:
								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]				, *_A  :		Dict				, **_A  :		str      )    ->							Optional[int]:
								return self.tokenizer.batch_decode(*snake_case__				, **snake_case__      )




					def 		A      (  self  :		Tuple				, *_A  :		List[Any]				, **_A  :		Any      )    ->							List[Any]:
								return self.tokenizer.decode(*snake_case__				, **snake_case__      )





					@property
					def 		A      (  self  :		Tuple      )    ->							Any:
								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"""



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	float							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	str							,			snake_case__      :	bool = False							,			):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Optional[int]							=							nn.Embedding(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							nn.Embedding(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							False

        UpperCAmelCase__						:    Optional[int]							=							nn.Dropout(p=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							TaConfig(
            vocab_size=snake_case__							,			d_model=snake_case__							,			num_heads=snake_case__							,			d_kv=snake_case__							,			d_ff=snake_case__							,			dropout_rate=snake_case__							,			feed_forward_proj=snake_case__							,			is_decoder=snake_case__							,			is_encoder_decoder=snake_case__							,			)

        UpperCAmelCase__						:    Tuple							=							nn.ModuleList()
        for lyr_num in range(snake_case__							):
             UpperCAmelCase__						:    Tuple							=							TaBlock(snake_case__							)
             self.encoders.append(snake_case__							)

        UpperCAmelCase__						:    str							=							TaLayerNorm(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							nn.Dropout(p=snake_case__							)




   def       __a							(		self      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.token_embedder(snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							encoder_input_tokens.shape[1]
        UpperCAmelCase__						:    List[Any]							=							torch.arange(snake_case__							,			device=encoder_input_tokens.device							)
        x += self.position_encoding(snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.dropout_pre(snake_case__							)

        # inverted the attention mask
        UpperCAmelCase__						:    List[Any]							=							encoder_input_tokens.size()
        UpperCAmelCase__						:    Tuple							=							self.get_extended_attention_mask(snake_case__							,			snake_case__							)

        for lyr in self.encoders:
             UpperCAmelCase__						:    Any							=							lyr(snake_case__							,			snake_case__							)[0]
        UpperCAmelCase__						:    Any							=							self.layer_norm(snake_case__							)

        return self.dropout_post(snake_case__							), encoder_inputs_mask


