





from collections import OrderedDict
from typing import Mapping

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


lowercase				:	int      						=  logging.get_logger(__name__)

lowercase				:	str      						=  {
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Union[str, Any]	  =  '''xlm-roberta-xl'''

   def __init__( self ,  lowercase=25_0880 ,  lowercase=2560 ,  lowercase=36 ,  lowercase=32 ,  lowercase=1_0240 ,  lowercase="gelu" ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=514 ,  lowercase=1 ,  lowercase=0.02 ,  lowercase=1e-05 ,  lowercase=1 ,  lowercase=0 ,  lowercase=2 ,  lowercase="absolute" ,  lowercase=True ,  lowercase=None ,  **lowercase ,  )      ->						Optional[int]:







          '''simple docstring'''

          super().__init__(pad_token_id=lowercase ,  bos_token_id=lowercase ,  eos_token_id=lowercase ,  **lowercase)
          a__						:     Optional[Any]			=    vocab_size
          a__						:     Optional[Any]			=    hidden_size
          a__						:     Optional[int]			=    num_hidden_layers
          a__						:     Dict			=    num_attention_heads
          a__						:     Tuple			=    hidden_act
          a__						:     Optional[int]			=    intermediate_size
          a__						:     Tuple			=    hidden_dropout_prob
          a__						:     List[str]			=    attention_probs_dropout_prob
          a__						:     Union[str, Any]			=    max_position_embeddings
          a__						:     Optional[Any]			=    type_vocab_size
          a__						:     List[str]			=    initializer_range
          a__						:     Optional[Any]			=    layer_norm_eps
          a__						:     Optional[int]			=    position_embedding_type
          a__						:     Tuple			=    use_cache
          a__						:     List[Any]			=    classifier_dropout





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   @property
   def 							__lowercase		( self)      ->						Mapping[str, Mapping[int, str]]:







          '''simple docstring'''

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









import enum
import shutil
import sys


lowercase						, lowercase				:	List[Any]      						=  shutil.get_terminal_size()

lowercase				:	Union[str, Any]      						=  {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}





class 			A__   (							enum.Enum     ):




   """simple docstring"""



   __A       :    List[str]	  =  0
   __A       :    str	  =  1



def A_     (		A__		,				A__=""    )  ->		int:
       sys.stdout.write(str(A__    ) + end    )
       sys.stdout.flush()



def A_     (		A__		,				A__		,				A__=""    )  ->		int:
       forceWrite(F'\u001b[{color}m{content}\u001b[0m'		,				A__    )



def A_     (		)  ->		Any:
       forceWrite('\r'    )



def A_     (		A__		,				A__    )  ->		List[str]:
       forceWrite(F'\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'    )



def A_     (		)  ->		Any:
       forceWrite(' ' * TERMINAL_WIDTH    )
       reset_cursor()



def A_     (		)  ->		Any:
       reset_cursor()
       forceWrite('-' * TERMINAL_WIDTH    )


