


'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


__A   :    Union[str, Any]											= logging.get_logger(__name__)

__A   :    List[Any]											= {
    "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   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'xlm-roberta'

    def __init__(  self       :    List[Any]       ,		lowerCamelCase       :    str=3_05_22       ,		lowerCamelCase       :    int=7_68       ,		lowerCamelCase       :    Tuple=12       ,		lowerCamelCase       :    Union[str, Any]=12       ,		lowerCamelCase       :    Optional[Any]=30_72       ,		lowerCamelCase       :    Any="gelu"       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    Tuple=0.1       ,		lowerCamelCase       :    Dict=5_12       ,		lowerCamelCase       :    List[str]=2       ,		lowerCamelCase       :    Tuple=0.02       ,		lowerCamelCase       :    int=1E-12       ,		lowerCamelCase       :    List[Any]=1       ,		lowerCamelCase       :    List[Any]=0       ,		lowerCamelCase       :    List[str]=2       ,		lowerCamelCase       :    Any="absolute"       ,		lowerCamelCase       :    Union[str, Any]=True       ,		lowerCamelCase       :    Dict=None       ,		**lowerCamelCase       :    List[Any]       ,		)  ->    List[Any]:
      super().__init__(pad_token_id=lowerCamelCase       ,		bos_token_id=lowerCamelCase       ,		eos_token_id=lowerCamelCase       ,		**lowerCamelCase						)

      lowerCAmelCase_     : Optional[int]     =  vocab_size
      lowerCAmelCase_     : List[str]     =  hidden_size
      lowerCAmelCase_     : Optional[int]     =  num_hidden_layers
      lowerCAmelCase_     : Optional[Any]     =  num_attention_heads
      lowerCAmelCase_     : Optional[Any]     =  hidden_act
      lowerCAmelCase_     : Optional[Any]     =  intermediate_size
      lowerCAmelCase_     : Any     =  hidden_dropout_prob
      lowerCAmelCase_     : Union[str, Any]     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Any     =  max_position_embeddings
      lowerCAmelCase_     : List[str]     =  type_vocab_size
      lowerCAmelCase_     : str     =  initializer_range
      lowerCAmelCase_     : List[Any]     =  layer_norm_eps
      lowerCAmelCase_     : List[str]     =  position_embedding_type
      lowerCAmelCase_     : Optional[Any]     =  use_cache
      lowerCAmelCase_     : Tuple     =  classifier_dropout







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @property
    def __lowercase  (  self       :    int						)  ->    Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
        lowerCAmelCase_     : Any     =  {0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
        lowerCAmelCase_     : int     =  {0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]						)







'''simple docstring'''






from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
  from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



