






"""simple docstring"""







from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a       :	Dict             = {'configuration_mmbt': ['MMBTConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__a       :	str             = ['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
							from .configuration_mmbt import MMBTConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
							import sys

							__a       :	Any             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a       :	str             = logging.get_logger(__name__)

__a       :	Dict             = {
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='xlm'
							_SCREAMING_SNAKE_CASE    ={
							    'hidden_size': 'emb_dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							    'n_words': 'vocab_size',  # For backward compatibility
							}




							def __init__(   self:     Optional[Any]				,		__A:     str=30145				,		__A:     Union[str, Any]=2048				,		__A:     Union[str, Any]=12				,		__A:     List[Any]=16				,		__A:     List[Any]=0.1				,		__A:     int=0.1				,		__A:     str=True				,		__A:     int=False				,		__A:     Optional[int]=False				,		__A:     Union[str, Any]=False				,		__A:     List[Any]=1				,		__A:     Dict=True				,		__A:     List[Any]=512				,		__A:     Optional[int]=2048**-0.5				,		__A:     Tuple=1e-12				,		__A:     Optional[int]=0.0_2				,		__A:     Optional[int]=0				,		__A:     Union[str, Any]=1				,		__A:     List[Any]=2				,		__A:     Union[str, Any]=3				,		__A:     Tuple=5				,		__A:     Tuple=True				,		__A:     Dict="first"				,		__A:     Optional[int]=True				,		__A:     int=None				,		__A:     Optional[Any]=True				,		__A:     Dict=0.1				,		__A:     Tuple=5				,		__A:     Union[str, Any]=5				,		__A:     Tuple=0				,		__A:     List[str]=0				,		__A:     int=2				,		__A:     Optional[int]=0				,		**__A:     Union[str, Any]				,		):

												'''simple docstring'''

												a__          =  vocab_size
												a__          =  emb_dim
												a__          =  n_layers
												a__          =  n_heads
												a__          =  dropout
												a__          =  attention_dropout
												a__          =  gelu_activation
												a__          =  sinusoidal_embeddings
												a__          =  causal
												a__          =  asm
												a__          =  n_langs
												a__          =  use_lang_emb
												a__          =  layer_norm_eps
												a__          =  bos_index
												a__          =  eos_index
												a__          =  pad_index
												a__          =  unk_index
												a__          =  mask_index
												a__          =  is_encoder
												a__          =  max_position_embeddings
												a__          =  embed_init_std
												a__          =  init_std
												a__          =  summary_type
												a__          =  summary_use_proj
												a__          =  summary_activation
												a__          =  summary_proj_to_labels
												a__          =  summary_first_dropout
												a__          =  start_n_top
												a__          =  end_n_top
												a__          =  mask_token_id
												a__          =  lang_id

												if "n_words" in kwargs:
																	a__          =  kwargs['''n_words''']

												super().__init__(pad_token_id=__A				,		bos_token_id=__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							@property
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												if self.task == "multiple-choice":
																	a__          =  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	a__          =  {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''token_type_ids''', dynamic_axis),
												    ]    )


