






from collections import deque







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Dict       ):
	snake_case__			:				str       =  len(snake_case_       )
	snake_case__			:				Union[str, Any]       =  deque()
	snake_case__			:				Dict       =  [False for _ in range(snake_case_       )]
	snake_case__			:				Any       =  [-1 for _ in range(snake_case_       )]
	snake_case__			:				Optional[Any]       =  index_of[:]

	def strong_connect(snake_case_ :							List[str]					,  snake_case_ :							List[Any]					,  snake_case_ :							Any       ):
		snake_case__			:				List[str]       =  index  # the number when this node is seen
		snake_case__			:				Union[str, Any]       =  index  # lowest rank node reachable from here
		index += 1
		stack.append(snake_case_       )
		snake_case__			:				Dict       =  True

		for w in g[v]:
			if index_of[w] == -1:
				snake_case__			:				List[Any]       =  strong_connect(snake_case_					,  snake_case_					,  snake_case_       )
				snake_case__			:				Any       =  (
				    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
				)
			elif on_stack[w]:
				snake_case__			:				Tuple       =  (
				    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
				)

		if lowlink_of[v] == index_of[v]:
			snake_case__			:				Union[str, Any]       =  []
			snake_case__			:				Union[str, Any]       =  stack.pop()
			snake_case__			:				int       =  False
			component.append(snake_case_       )
			while w != v:
				snake_case__			:				Tuple       =  stack.pop()
				snake_case__			:				int       =  False
				component.append(snake_case_       )
			components.append(snake_case_       )
		return index

	snake_case__			:				Dict       =  []
	for v in range(snake_case_       ):
		if index_of[v] == -1:
			strong_connect(snake_case_					,  0					,  snake_case_       )

	return components







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							List[str]       ):
	snake_case__			:				List[Any]       =  [[] for _ in range(snake_case_       )]
	for u, v in edges:
		g[u].append(snake_case_       )
	return g


if __name__ == "__main__":
	# Test
	__lowerCamelCase :      Optional[Any]										=				7
	__lowerCamelCase :      str										=				[0, 0, 1, 2, 3, 3, 4, 4, 6]
	__lowerCamelCase :      str										=				[1, 3, 2, 0, 1, 4, 5, 6, 5]
	__lowerCamelCase :      str										=				[(u, v) for u, v in zip(source, target)]
	__lowerCamelCase :      Any										=				create_graph(n_vertices, edges)

	assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)











from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase :      List[str]										=				logging.get_logger(__name__)

__lowerCamelCase :      List[Any]										=				{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"xmod"



					def __init__( self					:  List[str]							,		__A					:  List[str]=3_0_5_2_2							,		__A					:  Tuple=7_6_8							,		__A					:  str=1_2							,		__A					:  List[Any]=1_2							,		__A					:  List[str]=3_0_7_2							,		__A					:  List[str]="gelu"							,		__A					:  List[Any]=0.1							,		__A					:  Tuple=0.1							,		__A					:  str=5_1_2							,		__A					:  Union[str, Any]=2							,		__A					:  List[Any]=0.0_2							,		__A					:  List[str]=1e-1_2							,		__A					:  Tuple=1							,		__A					:  List[Any]=0							,		__A					:  Optional[Any]=2							,		__A					:  Optional[int]="absolute"							,		__A					:  Optional[int]=True							,		__A					:  Dict=None							,		__A					:  Optional[int]=False							,		__A					:  Dict=2							,		__A					:  List[str]=False							,		__A					:  Dict=True							,		__A					:  Union[str, Any]=True							,		__A					:  Tuple=("en_XX",)							,		__A					:  Optional[Any]=None							,		**__A					:  Tuple							,		):
						super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)

						snake_case__			:				Tuple       =  vocab_size
						snake_case__			:				Union[str, Any]       =  hidden_size
						snake_case__			:				int       =  num_hidden_layers
						snake_case__			:				str       =  num_attention_heads
						snake_case__			:				Tuple       =  hidden_act
						snake_case__			:				Optional[Any]       =  intermediate_size
						snake_case__			:				List[str]       =  hidden_dropout_prob
						snake_case__			:				Optional[int]       =  attention_probs_dropout_prob
						snake_case__			:				Union[str, Any]       =  max_position_embeddings
						snake_case__			:				str       =  type_vocab_size
						snake_case__			:				List[str]       =  initializer_range
						snake_case__			:				Dict       =  layer_norm_eps
						snake_case__			:				str       =  position_embedding_type
						snake_case__			:				List[str]       =  use_cache
						snake_case__			:				Tuple       =  classifier_dropout
						snake_case__			:				Any       =  pre_norm
						snake_case__			:				List[str]       =  adapter_reduction_factor
						snake_case__			:				List[Any]       =  adapter_layer_norm
						snake_case__			:				str       =  adapter_reuse_layer_norm
						snake_case__			:				Union[str, Any]       =  ln_before_adapter
						snake_case__			:				Tuple       =  list(__A	)
						snake_case__			:				int       =  default_language



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






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



