


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__				):



					'''simple docstring'''







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






					def __init__(				self ,       snake_case_ ,       snake_case_						)   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__(__lowerCAmelCase ,       __lowerCAmelCase						)






					def __call__(				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = True ,       snake_case_ = False ,       snake_case_ = None ,       snake_case_ = None ,       snake_case_ = 0 ,       snake_case_ = None ,       snake_case_ = None ,       snake_case_ = None ,       snake_case_ = False ,       snake_case_ = False ,       snake_case_ = False ,       snake_case_ = False ,       snake_case_ = True ,       snake_case_ = None ,       **snake_case_ ,       )   ->			BatchEncoding:







								'''simple docstring'''

								__lowercase 					=   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
								__lowercase 					=   self.image_processor(
								    __lowerCAmelCase ,       return_tensors=__lowerCAmelCase ,       do_normalize=__lowerCAmelCase ,       do_center_crop=__lowerCAmelCase ,       **__lowerCAmelCase						)
								encoding.update(__lowerCAmelCase						)

								return encoding






					def A      (				self ,       *snake_case_ ,       **snake_case_						)   ->			Dict:







								'''simple docstring'''

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






					def A      (				self ,       *snake_case_ ,       **snake_case_						)   ->			Tuple:







								'''simple docstring'''

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






					@property
					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   self.tokenizer.model_input_names
								__lowercase 					=   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)






from typing import List

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


a : Any		=				logging.get_logger(__name__)

a : int		=				{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"efficientformer"






					def __init__(				self ,       snake_case_ = [3, 2, 6, 4] ,       snake_case_ = [4_8, 9_6, 2_2_4, 4_4_8] ,       snake_case_ = [True, True, True, True] ,       snake_case_ = 4_4_8 ,       snake_case_ = 3_2 ,       snake_case_ = 4 ,       snake_case_ = 7 ,       snake_case_ = 5 ,       snake_case_ = 8 ,       snake_case_ = 4 ,       snake_case_ = 0.0 ,       snake_case_ = 1_6 ,       snake_case_ = 3 ,       snake_case_ = 3 ,       snake_case_ = 3 ,       snake_case_ = 2 ,       snake_case_ = 1 ,       snake_case_ = 0.0 ,       snake_case_ = 1 ,       snake_case_ = True ,       snake_case_ = True ,       snake_case_ = 1e-5 ,       snake_case_ = "gelu" ,       snake_case_ = 0.0_2 ,       snake_case_ = 1e-1_2 ,       snake_case_ = 2_2_4 ,       snake_case_ = 1e-0_5 ,       **snake_case_ ,       )   ->			None:







								'''simple docstring'''

								super().__init__(**snake_case_						)

								__lowercase 					=   hidden_act
								__lowercase 					=   hidden_dropout_prob
								__lowercase 					=   hidden_sizes
								__lowercase 					=   num_hidden_layers
								__lowercase 					=   num_attention_heads
								__lowercase 					=   initializer_range
								__lowercase 					=   layer_norm_eps
								__lowercase 					=   patch_size
								__lowercase 					=   num_channels
								__lowercase 					=   depths
								__lowercase 					=   mlp_expansion_ratio
								__lowercase 					=   downsamples
								__lowercase 					=   dim
								__lowercase 					=   key_dim
								__lowercase 					=   attention_ratio
								__lowercase 					=   resolution
								__lowercase 					=   pool_size
								__lowercase 					=   downsample_patch_size
								__lowercase 					=   downsample_stride
								__lowercase 					=   downsample_pad
								__lowercase 					=   drop_path_rate
								__lowercase 					=   num_metaad_blocks
								__lowercase 					=   distillation
								__lowercase 					=   use_layer_scale
								__lowercase 					=   layer_scale_init_value
								__lowercase 					=   image_size
								__lowercase 					=   batch_norm_eps


