



"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:Any					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """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 SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			List[str] =				"xlm"
    __lowerCamelCase      :			Tuple =				{
        "hidden_size": "emb_dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
        "n_words": "vocab_size",  # For backward compatibility
    }






    def __init__(	self,		lowerCamelCase__=3_0145,		lowerCamelCase__=2048,		lowerCamelCase__=12,		lowerCamelCase__=16,		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=1,		lowerCamelCase__=True,		lowerCamelCase__=512,		lowerCamelCase__=2048**-0.5,		lowerCamelCase__=1e-12,		lowerCamelCase__=0.02,		lowerCamelCase__=0,		lowerCamelCase__=1,		lowerCamelCase__=2,		lowerCamelCase__=3,		lowerCamelCase__=5,		lowerCamelCase__=True,		lowerCamelCase__="first",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__=0.1,		lowerCamelCase__=5,		lowerCamelCase__=5,		lowerCamelCase__=0,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__=0,		**lowerCamelCase__,		):
        A			:	Dict										=       vocab_size
        A			:	int										=       emb_dim
        A			:	str										=       n_layers
        A			:	Union[str, Any]										=       n_heads
        A			:	Optional[int]										=       dropout
        A			:	Union[str, Any]										=       attention_dropout
        A			:	Optional[Any]										=       gelu_activation
        A			:	Dict										=       sinusoidal_embeddings
        A			:	int										=       causal
        A			:	Optional[Any]										=       asm
        A			:	Any										=       n_langs
        A			:	List[str]										=       use_lang_emb
        A			:	Union[str, Any]										=       layer_norm_eps
        A			:	str										=       bos_index
        A			:	int										=       eos_index
        A			:	Tuple										=       pad_index
        A			:	str										=       unk_index
        A			:	Optional[Any]										=       mask_index
        A			:	Union[str, Any]										=       is_encoder
        A			:	Tuple										=       max_position_embeddings
        A			:	List[str]										=       embed_init_std
        A			:	Tuple										=       init_std
        A			:	Tuple										=       summary_type
        A			:	int										=       summary_use_proj
        A			:	List[Any]										=       summary_activation
        A			:	Optional[Any]										=       summary_proj_to_labels
        A			:	Optional[Any]										=       summary_first_dropout
        A			:	Optional[int]										=       start_n_top
        A			:	Optional[Any]										=       end_n_top
        A			:	List[str]										=       mask_token_id
        A			:	Tuple										=       lang_id

        if "n_words" in kwargs:
            A			:	List[str]										=       kwargs["""n_words"""]

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






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






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







from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst




def      __UpperCamelCase     (  )	->  Tuple:



    """simple docstring"""






    A					,							A			:	Tuple										=       9, 14  # noqa: F841
    A			:	Optional[int]										=       [
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    A			:	int										=       defaultdict(_lowerCAmelCase		)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]		)
        adjancency[nodea].append([nodea, cost]		)

    A			:	List[str]										=       mst(_lowerCAmelCase		)

    A			:	str										=       [
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        A			:	Dict										=       tuple(answer[:2]		)
        A			:	List[str]										=       tuple(edge[::-1]		)
        assert edge in result or reverse in result


