

'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase :      List[Any]					 =     logging.get_logger(__name__)

lowerCamelCase :      Optional[int]					 =     {
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}







class 							A__    (       A__  ):
  A__		 =				'xlm-roberta'





  def __init__(		self      :	int							,					_a      :	Union[str, Any]=3_0522							,					_a      :	Any=768							,					_a      :	Optional[Any]=12							,					_a      :	str=12							,					_a      :	str=3072							,					_a      :	Any="gelu"							,					_a      :	Optional[int]=0.1							,					_a      :	int=0.1							,					_a      :	List[str]=512							,					_a      :	Optional[Any]=2							,					_a      :	Dict=0.02							,					_a      :	str=1e-12							,					_a      :	Any=1							,					_a      :	List[str]=0							,					_a      :	Optional[Any]=2							,					_a      :	str="absolute"							,					_a      :	Dict=True							,					_a      :	str=None							,					**_a      :	Dict							,					)	->    List[Any]:





       '''simple docstring'''






       super().__init__(pad_token_id=_a							,					bos_token_id=_a							,					eos_token_id=_a							,					**_a )

       _SCREAMING_SNAKE_CASE							    =vocab_size
       _SCREAMING_SNAKE_CASE							    =hidden_size
       _SCREAMING_SNAKE_CASE							    =num_hidden_layers
       _SCREAMING_SNAKE_CASE							    =num_attention_heads
       _SCREAMING_SNAKE_CASE							    =hidden_act
       _SCREAMING_SNAKE_CASE							    =intermediate_size
       _SCREAMING_SNAKE_CASE							    =hidden_dropout_prob
       _SCREAMING_SNAKE_CASE							    =attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE							    =max_position_embeddings
       _SCREAMING_SNAKE_CASE							    =type_vocab_size
       _SCREAMING_SNAKE_CASE							    =initializer_range
       _SCREAMING_SNAKE_CASE							    =layer_norm_eps
       _SCREAMING_SNAKE_CASE							    =position_embedding_type
       _SCREAMING_SNAKE_CASE							    =use_cache
       _SCREAMING_SNAKE_CASE							    =classifier_dropout







class 							A__    (       A__  ):





  @property
  def 						A  (		self      :	Optional[Any] )	->    Mapping[str, Mapping[int, str]]:





       '''simple docstring'''






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






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


__lowerCAmelCase		:				Optional[Any]       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				str       			=		{'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Dict							 =					"""openai-gpt"""
				SCREAMING_SNAKE_CASE_       :							Tuple							 =					{
				    """max_position_embeddings""": """n_positions""",
				    """hidden_size""": """n_embd""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}





				def __init__(      self				:     str					,      __lowerCamelCase				:     List[str]=4_04_78					,      __lowerCamelCase				:     List[Any]=5_12					,      __lowerCamelCase				:     List[str]=7_68					,      __lowerCamelCase				:     List[str]=12					,      __lowerCamelCase				:     Optional[Any]=12					,      __lowerCamelCase				:     str="gelu"					,      __lowerCamelCase				:     Optional[int]=0.1					,      __lowerCamelCase				:     Any=0.1					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     Any=1e-5					,      __lowerCamelCase				:     Optional[int]=0.02					,      __lowerCamelCase				:     Optional[int]="cls_index"					,      __lowerCamelCase				:     List[Any]=True					,      __lowerCamelCase				:     Optional[int]=None					,      __lowerCamelCase				:     Optional[Any]=True					,      __lowerCamelCase				:     Tuple=0.1					,      **__lowerCamelCase				:     Union[str, Any]					,      )					->     List[str]:
							a    =			vocab_size
							a    =			n_positions
							a    =			n_embd
							a    =			n_layer
							a    =			n_head
							a    =			afn
							a    =			resid_pdrop
							a    =			embd_pdrop
							a    =			attn_pdrop
							a    =			layer_norm_epsilon
							a    =			initializer_range
							a    =			summary_type
							a    =			summary_use_proj
							a    =			summary_activation
							a    =			summary_first_dropout
							a    =			summary_proj_to_labels
							super().__init__(**__lowerCamelCase							)






