


"""simple docstring"""







# using dfs for finding eulerian path traversal
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase=None		):







    """simple docstring"""
    a					=(path or []) + [u]
    for v in graph[u]:
        if visited_edge[u][v] is False:
            a		,				a					=True, True
            a					=dfs(lowercase	,   lowercase	,   lowercase	,   lowercase		)
    return path
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    a					=0
    a					=-1
    for i in range(lowercase		):
        if i not in graph.keys():
            continue
        if len(graph[i]		) % 2 == 1:
            odd_degree_nodes += 1
            a					=i
    if odd_degree_nodes == 0:
        return 1, odd_node
    if odd_degree_nodes == 2:
        return 2, odd_node
    return 3, odd_node
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    a					=[[False for _ in range(max_node + 1		)] for _ in range(max_node + 1		)]
    a		,				a					=check_circuit_or_path(lowercase	,   lowercase		)
    if check == 3:
        print('''graph is not Eulerian'''		)
        print('''no path'''		)
        return
    a					=1
    if check == 2:
        a					=odd_node
        print('''graph has a Euler path'''		)
    if check == 1:
        print('''graph has a Euler cycle'''		)
    a					=dfs(lowercase	,   lowercase	,   lowercase		)
    print(lowercase		)
def _A						(						):







    """simple docstring"""
    a					={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
    a					={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
    a					={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
    a					={1: [2, 3], 2: [1, 3], 3: [1, 2]}
    a					={
        1: [],
        2: []
        # all degree is zero
    }
    a					=10
    check_euler(lowercase	,   lowercase		)
    check_euler(lowercase	,   lowercase		)
    check_euler(lowercase	,   lowercase		)
    check_euler(lowercase	,   lowercase		)
    check_euler(lowercase	,   lowercase		)


if __name__ == "__main__":
       main()



"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_       :       str      						=  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    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "xlm-roberta"


 def __init__(		self			,   __A=3_0522			,   __A=768			,   __A=12			,   __A=12			,   __A=3072			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=512			,   __A=2			,   __A=0.02			,   __A=1E-1_2			,   __A=1			,   __A=0			,   __A=2			,   __A="absolute"			,   __A=True			,   __A=None			,   **__A			,   ) ->					str:
     super().__init__(pad_token_id=__A			,   bos_token_id=__A			,   eos_token_id=__A			,   **__A						)

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






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




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