



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


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

SCREAMING_SNAKE_CASE_:Any             =						{
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Tuple					=      "transfo-xl"
    __lowerCamelCase			: Optional[Any]					=      ["mems"]
    __lowerCamelCase			: int					=      {
        "n_token": "vocab_size",
        "hidden_size": "d_model",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }






    def __init__(       self,		lowerCamelCase__=26_7735,		lowerCamelCase__=[2_0000, 4_0000, 20_0000],		lowerCamelCase__=1024,		lowerCamelCase__=1024,		lowerCamelCase__=16,		lowerCamelCase__=64,		lowerCamelCase__=4096,		lowerCamelCase__=4,		lowerCamelCase__=False,		lowerCamelCase__=18,		lowerCamelCase__=1600,		lowerCamelCase__=1000,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=0,		lowerCamelCase__=-1,		lowerCamelCase__=True,		lowerCamelCase__=0.1,		lowerCamelCase__=0.0,		lowerCamelCase__=True,		lowerCamelCase__="normal",		lowerCamelCase__=0.01,		lowerCamelCase__=0.01,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-5,		lowerCamelCase__=0,		**lowerCamelCase__,		):
        A :       Union[str, Any]          =   vocab_size
        A :       Tuple          =   []
        self.cutoffs.extend(lowerCamelCase__		)
        if proj_share_all_but_first:
            A :       List[str]          =   [False] + [True] * len(self.cutoffs		)
        else:
            A :       Union[str, Any]          =   [False] + [False] * len(self.cutoffs		)
        A :       Optional[Any]          =   d_model
        A :       Dict          =   d_embed
        A :       int          =   d_head
        A :       int          =   d_inner
        A :       Optional[int]          =   div_val
        A :       Tuple          =   pre_lnorm
        A :       Union[str, Any]          =   n_layer
        A :       Tuple          =   n_head
        A :       List[str]          =   mem_len
        A :       List[Any]          =   same_length
        A :       Dict          =   attn_type
        A :       int          =   clamp_len
        A :       Union[str, Any]          =   sample_softmax
        A :       str          =   adaptive
        A :       Dict          =   dropout
        A :       str          =   dropatt
        A :       Optional[int]          =   untie_r
        A :       str          =   init
        A :       Dict          =   init_range
        A :       List[Any]          =   proj_init_std
        A :       List[str]          =   init_std
        A :       Tuple          =   layer_norm_epsilon
        super().__init__(eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)






    @property
    def 			_lowerCAmelCase					(       self		):
        # Message copied from Transformer-XL documentation
        logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''		)
        return -1






    @max_position_embeddings.setter
    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        # Message copied from Transformer-XL documentation
        raise NotImplementedError(
            f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''		)







from collections import OrderedDict
from typing import Mapping

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


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

SCREAMING_SNAKE_CASE_:Dict             =						{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      "camembert"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

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






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       int          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       Tuple          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


