



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


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}

class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[Any]			       =    "transfo-xl"
					A__				:		Optional[int]			       =    ["mems"]
					A__				:		Dict			       =    {
					    "n_token": "vocab_size",
					    "hidden_size": "d_model",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}



					def __init__(     self					:       Optional[Any]				,					_snake_case					:       Tuple=26_77_35				,					_snake_case					:       Union[str, Any]=[2_00_00, 4_00_00, 20_00_00]				,					_snake_case					:       int=10_24				,					_snake_case					:       int=10_24				,					_snake_case					:       int=16				,					_snake_case					:       Any=64				,					_snake_case					:       int=40_96				,					_snake_case					:       Dict=4				,					_snake_case					:       int=False				,					_snake_case					:       Optional[int]=18				,					_snake_case					:       List[str]=16_00				,					_snake_case					:       Optional[Any]=10_00				,					_snake_case					:       Union[str, Any]=True				,					_snake_case					:       List[Any]=True				,					_snake_case					:       Optional[Any]=0				,					_snake_case					:       Union[str, Any]=-1				,					_snake_case					:       Tuple=True				,					_snake_case					:       Tuple=0.1				,					_snake_case					:       Union[str, Any]=0.0				,					_snake_case					:       str=True				,					_snake_case					:       Union[str, Any]="normal"				,					_snake_case					:       Optional[Any]=0.01				,					_snake_case					:       List[str]=0.01				,					_snake_case					:       Union[str, Any]=0.02				,					_snake_case					:       Optional[int]=1E-5				,					_snake_case					:       int=0				,					**_snake_case					:       Union[str, Any]				,					):






								"""simple docstring"""


								A__       =    vocab_size
								A__       =    []
								self.cutoffs.extend(_snake_case					)
								if proj_share_all_but_first:
											A__       =    [False] + [True] * len(self.cutoffs					)
								else:
											A__       =    [False] + [False] * len(self.cutoffs					)
								A__       =    d_model
								A__       =    d_embed
								A__       =    d_head
								A__       =    d_inner
								A__       =    div_val
								A__       =    pre_lnorm
								A__       =    n_layer
								A__       =    n_head
								A__       =    mem_len
								A__       =    same_length
								A__       =    attn_type
								A__       =    clamp_len
								A__       =    sample_softmax
								A__       =    adaptive
								A__       =    dropout
								A__       =    dropatt
								A__       =    untie_r
								A__       =    init
								A__       =    init_range
								A__       =    proj_init_std
								A__       =    init_std
								A__       =    layer_norm_epsilon
								super().__init__(eos_token_id=_snake_case				,					**_snake_case					)



					@property
					def     _a							(     self					:       Optional[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     _a							(     self					:       Any				,					_snake_case					:       Any					):






								"""simple docstring"""


								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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    ["image_processor", "tokenizer"]
					A__				:		Optional[Any]			       =    "BridgeTowerImageProcessor"
					A__				:		List[Any]			       =    ("RobertaTokenizer", "RobertaTokenizerFast")



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






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       int				,					_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					:       Optional[int]				,					):






								"""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     _a							(     self					:       Any				,					*_snake_case					:       Tuple				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Dict				,					*_snake_case					:       Dict				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


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



					@property
					def     _a							(     self					:       Tuple					):






								"""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					)					)




