



from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase_										=					logging.get_logger(__name__)

UpperCamelCase_										=					{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}







class 	_snake_case							(			__A      ):






   '''simple docstring'''






   A__	:      int    =		"xlm-roberta-xl"


   def __init__(  self:			str   ,lowerCamelCase_:			Union[str, Any]=250880   ,lowerCamelCase_:			List[Any]=2560   ,lowerCamelCase_:			Any=36   ,lowerCamelCase_:			Dict=32   ,lowerCamelCase_:			Optional[int]=10240   ,lowerCamelCase_:			Dict="gelu"   ,lowerCamelCase_:			Union[str, Any]=0.1   ,lowerCamelCase_:			Dict=0.1   ,lowerCamelCase_:			Union[str, Any]=514   ,lowerCamelCase_:			Dict=1   ,lowerCamelCase_:			int=0.0_2   ,lowerCamelCase_:			List[str]=1e-05   ,lowerCamelCase_:			Dict=1   ,lowerCamelCase_:			List[str]=0   ,lowerCamelCase_:			str=2   ,lowerCamelCase_:			Dict="absolute"   ,lowerCamelCase_:			Optional[Any]=True   ,lowerCamelCase_:			Dict=None   ,**lowerCamelCase_:			List[Any]   ,)     ->	Union[str, Any]:
       super().__init__(pad_token_id=_UpperCamelCase   ,bos_token_id=_UpperCamelCase   ,eos_token_id=_UpperCamelCase   ,**_UpperCamelCase				)
       UpperCAmelCase_			:    Dict   						= vocab_size
       UpperCAmelCase_			:    Tuple   						= hidden_size
       UpperCAmelCase_			:    int   						= num_hidden_layers
       UpperCAmelCase_			:    Any   						= num_attention_heads
       UpperCAmelCase_			:    int   						= hidden_act
       UpperCAmelCase_			:    List[Any]   						= intermediate_size
       UpperCAmelCase_			:    Any   						= hidden_dropout_prob
       UpperCAmelCase_			:    Any   						= attention_probs_dropout_prob
       UpperCAmelCase_			:    Optional[int]   						= max_position_embeddings
       UpperCAmelCase_			:    int   						= type_vocab_size
       UpperCAmelCase_			:    Optional[int]   						= initializer_range
       UpperCAmelCase_			:    Optional[int]   						= layer_norm_eps
       UpperCAmelCase_			:    List[str]   						= position_embedding_type
       UpperCAmelCase_			:    Tuple   						= use_cache
       UpperCAmelCase_			:    Any   						= classifier_dropout







class 	_snake_case							(			__A      ):






   '''simple docstring'''






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



from __future__ import annotations


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# We need to create solution object to save path.
						snake_case_   =	[[0 for _ in range(SCREAMING_SNAKE_CASE__       )] for _ in range(SCREAMING_SNAKE_CASE__       )]
						snake_case_   =	run_maze(SCREAMING_SNAKE_CASE__  ,		0  ,		0  ,		SCREAMING_SNAKE_CASE__       )
						if solved:
												print('''\n'''.join(str(SCREAMING_SNAKE_CASE__       ) for row in solutions       )       )
						else:
												print('''No solution exists!'''       )
						return solved


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# Final check point.
						if i == j == (size - 1):
												snake_case_   =	1
												return True

						snake_case_   =	(not i < 0) and (not j < 0)  # Check lower bounds
						snake_case_   =	(i < size) and (j < size)  # Check upper bounds

						if lower_flag and upper_flag:
												# check for already visited and block points.
												snake_case_   =	(not solutions[i][j]) and (not maze[i][j])
												if block_flag:
																		# check visited
																		snake_case_   =	1

																		# check for directions
																		if (
																		    run_maze(SCREAMING_SNAKE_CASE__  ,		i + 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j + 1  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		i - 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j - 1  ,		SCREAMING_SNAKE_CASE__       )
																		):
																								return True

																		snake_case_   =	0
																		return False
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()