

'''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     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




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







						def __init__(							self					:      Optional[int]			,			snake_case_					:      Any			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								super().__init__(snake_case_			,			snake_case_  )







						def __call__(							self					:      Dict			,			snake_case_					:      Union[str, Any]			,			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_					:      List[str]			,			):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  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
								snake_case__						:       Optional[Any]       						=  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       __magic_name__							(							self					:      Dict			,			*snake_case_					:      List[Any]			,			**snake_case_					:      Tuple  ):



								'''simple docstring'''



								return self.tokenizer.batch_decode(*snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      Tuple			,			*snake_case_					:      int			,			**snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								return self.tokenizer.decode(*snake_case_			,			**snake_case_  )







						@property
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								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  )  )






'''simple docstring'''


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


lowerCAmelCase__						:       Dict										=						logging.get_logger(__name__)

lowerCAmelCase__						:       Tuple										=						{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """ctrl"""
						__UpperCAmelCase			     =    ["""past_key_values"""]
						__UpperCAmelCase			     =    {
						    """max_position_embeddings""": """n_positions""",
						    """hidden_size""": """n_embd""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}







						def __init__(							self					:      Tuple			,			snake_case_					:      Dict=2_4_6_5_3_4			,			snake_case_					:      Optional[int]=2_5_6			,			snake_case_					:      Dict=1_2_8_0			,			snake_case_					:      Union[str, Any]=8_1_9_2			,			snake_case_					:      Any=4_8			,			snake_case_					:      List[Any]=1_6			,			snake_case_					:      Optional[Any]=0.1			,			snake_case_					:      Union[str, Any]=0.1			,			snake_case_					:      Optional[Any]=1e-6			,			snake_case_					:      List[Any]=0.0_2			,			snake_case_					:      Dict=True			,			**snake_case_					:      List[Any]			,			):



								'''simple docstring'''



								snake_case__						:       Any       						=  vocab_size
								snake_case__						:       int       						=  n_positions
								snake_case__						:       Optional[int]       						=  n_embd
								snake_case__						:       str       						=  n_layer
								snake_case__						:       Any       						=  n_head
								snake_case__						:       str       						=  dff
								snake_case__						:       Any       						=  resid_pdrop
								snake_case__						:       Tuple       						=  embd_pdrop
								snake_case__						:       List[str]       						=  layer_norm_epsilon
								snake_case__						:       int       						=  initializer_range

								snake_case__						:       Optional[int]       						=  use_cache

								super().__init__(**snake_case_  )



