
from collections import OrderedDict
from typing import Mapping

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


__a  			=					logging.get_logger(__name__)

__a  			=					{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}
class  lowercase__(				__lowercase							):



	"""simple docstring"""




	a			:List[str]         =	'bert'
	def __init__(       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=3_0_5_2_2       ,							SCREAMING_SNAKE_CASE_  :			Dict=7_6_8       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=1_2       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=1_2       ,							SCREAMING_SNAKE_CASE_  :			int=3_0_7_2       ,							SCREAMING_SNAKE_CASE_  :			Dict="gelu"       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.1       ,							SCREAMING_SNAKE_CASE_  :			str=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=2       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.02       ,							SCREAMING_SNAKE_CASE_  :			Tuple=1e-12       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=0       ,							SCREAMING_SNAKE_CASE_  :			str="absolute"       ,							SCREAMING_SNAKE_CASE_  :			Any=True       ,							SCREAMING_SNAKE_CASE_  :			Dict=None       ,							**SCREAMING_SNAKE_CASE_  :			str       ,							)       ->     int:
					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

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

class  lowercase__(				__lowercase							):



	"""simple docstring"""




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






from __future__ import annotations




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			list[list[int]])					->    bool:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					len(_lowerCamelCase)
							# We need to create solution object to save path.
							__UpperCamelCase	:   List[str]			   =					[[0 for _ in range(_lowerCamelCase)] for _ in range(_lowerCamelCase)]
							__UpperCamelCase	:   Optional[int]			   =					run_maze(_lowerCamelCase						,  0						,  0						,  _lowerCamelCase)
							if solved:
														print("\n".join(str(_lowerCamelCase) for row in solutions))
							else:
														print("No solution exists!")
							return solved




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			list[list[int]]						,  _lowerCamelCase :			int						,  _lowerCamelCase :			int						,  _lowerCamelCase :			list[list[int]])					->    bool:






							'''simple docstring'''





							__UpperCamelCase	:   Tuple			   =					len(_lowerCamelCase)
							# Final check point.
							if i == j == (size - 1):
														__UpperCamelCase	:   Optional[int]			   =					1
														return True

							__UpperCamelCase	:   List[Any]			   =					(not i < 0) and (not j < 0)  # Check lower bounds
							__UpperCamelCase	:   List[str]			   =					(i < size) and (j < size)  # Check upper bounds

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

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

																					__UpperCamelCase	:   Tuple			   =					0
																					return False
							return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()