






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_	:    int            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Optional[int]            =      {
    '''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   UpperCamelCase_ (       a_  ):
  _A       :					List[str]				=	'xlm'
  _A       :					List[Any]				=	{
      'hidden_size': 'emb_dim',
      'num_attention_heads': 'n_heads',
      'num_hidden_layers': 'n_layers',
      'n_words': 'vocab_size',  # For backward compatibility
  }



  def __init__(      self ,		snake_case__=3_01_45 ,		snake_case__=20_48 ,		snake_case__=12 ,		snake_case__=16 ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=True ,		snake_case__=False ,		snake_case__=False ,		snake_case__=False ,		snake_case__=1 ,		snake_case__=True ,		snake_case__=5_12 ,		snake_case__=20_48**-0.5 ,		snake_case__=1e-12 ,		snake_case__=0.02 ,		snake_case__=0 ,		snake_case__=1 ,		snake_case__=2 ,		snake_case__=3 ,		snake_case__=5 ,		snake_case__=True ,		snake_case__="first" ,		snake_case__=True ,		snake_case__=None ,		snake_case__=True ,		snake_case__=0.1 ,		snake_case__=5 ,		snake_case__=5 ,		snake_case__=0 ,		snake_case__=0 ,		snake_case__=2 ,		snake_case__=0 ,		**snake_case__ ,		)			->			Optional[Any]:



         """simple docstring"""





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

         if "n_words" in kwargs:
                UpperCAmelCase	    =			kwargs["""n_words"""]

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





class   UpperCamelCase_ (       a_  ):



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



         """simple docstring"""





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








"""simple docstring"""







from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)
