

import warnings

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


_lowerCAmelCase					:			Optional[int]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Tuple  				=			{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'xlnet'
			SCREAMING_SNAKE_CASE							   =						['mems']
			SCREAMING_SNAKE_CASE							   =						{
			    'n_token': 'vocab_size',  # Backward compatibility
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}





			def __init__(		self					,  __snake_case=3_2000					,  __snake_case=1024					,  __snake_case=24					,  __snake_case=16					,  __snake_case=4096					,  __snake_case="gelu"					,  __snake_case=True					,  __snake_case="bi"					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=None					,  __snake_case=True					,  __snake_case=False					,  __snake_case=False					,  __snake_case=-1					,  __snake_case=False					,  __snake_case="last"					,  __snake_case=True					,  __snake_case="tanh"					,  __snake_case=0.1					,  __snake_case=5					,  __snake_case=5					,  __snake_case=5					,  __snake_case=1					,  __snake_case=2					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						__a        =vocab_size
						__a        =d_model
						__a        =n_layer
						__a        =n_head
						if d_model % n_head != 0:
									raise ValueError(f'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'					)
						if "d_head" in kwargs:
									if kwargs["d_head"] != d_model // n_head:
												raise ValueError(
												    f'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})'					)
						__a        =d_model // n_head
						__a        =ff_activation
						__a        =d_inner
						__a        =untie_r
						__a        =attn_type

						__a        =initializer_range
						__a        =layer_norm_eps

						__a        =dropout
						__a        =mem_len
						__a        =reuse_len
						__a        =bi_data
						__a        =clamp_len
						__a        =same_length

						__a        =summary_type
						__a        =summary_use_proj
						__a        =summary_activation
						__a        =summary_last_dropout
						__a        =start_n_top
						__a        =end_n_top

						__a        =bos_token_id
						__a        =pad_token_id
						__a        =eos_token_id

						if "use_cache" in kwargs:
									warnings.warn(
									    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
									    ' instead.'					,  __snake_case					,  )
									__a        =kwargs['use_cache']

						__a        =use_mems_eval
						__a        =use_mems_train
						super().__init__(pad_token_id=__snake_case					,  bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  **__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						logger.info(f'The model {self.model_type} is one of the few models that has no sequence length limit.'					)
						return -1





			@max_position_embeddings.setter
			def 		__magic_name__    (		self					,  __snake_case					)   -> int:




						'''simple docstring'''
						# Message copied from Transformer-XL documentation
						raise NotImplementedError(
						    f'The model {self.model_type} is one of the few models that has no sequence length limit.'					)









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 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						['image_processor', 'tokenizer']
			SCREAMING_SNAKE_CASE							   =						'BridgeTowerImageProcessor'
			SCREAMING_SNAKE_CASE							   =						('RobertaTokenizer', 'RobertaTokenizerFast')





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Optional[int]:




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





			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'''
						__a        =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
						__a        =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					,  *__snake_case					,  **__snake_case					)   -> str:




						'''simple docstring'''
						return self.tokenizer.batch_decode(*__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> Optional[Any]:




						'''simple docstring'''
						return self.tokenizer.decode(*__snake_case					,  **__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =self.tokenizer.model_input_names
						__a        =self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






