



'''simple docstring'''





from __future__ import annotations
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__      ,   lowerCAmelCase__ :  Tuple							      =					position
    lowerCAmelCase__ :  int							      =					[
        (y + 1, x + 2),
        (y - 1, x + 2),
        (y + 1, x - 2),
        (y - 1, x - 2),
        (y + 2, x + 1),
        (y + 2, x - 1),
        (y - 2, x + 1),
        (y - 2, x - 1),
    ]
    lowerCAmelCase__ :  int							      =					[]

    for position in positions:
        lowerCAmelCase__      ,   lowerCAmelCase__ :  Dict							      =					position
        if 0 <= y_test < n and 0 <= x_test < n:
            permissible_positions.append(UpperCamelCase			)

    return permissible_positions
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return not any(elem == 0 for row in board for elem in row			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    if is_complete(UpperCamelCase			):
        return True

    for position in get_valid_pos(UpperCamelCase							,    len(UpperCamelCase			)			):
        lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					position

        if board[y][x] == 0:
            lowerCAmelCase__ :  List[str]							      =					curr + 1
            if open_knight_tour_helper(UpperCamelCase							,    UpperCamelCase							,    curr + 1			):
                return True
            lowerCAmelCase__ :  Optional[int]							      =					0

    return False
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					[[0 for i in range(UpperCamelCase			)] for j in range(UpperCamelCase			)]

    for i in range(UpperCamelCase			):
        for j in range(UpperCamelCase			):
            lowerCAmelCase__ :  int							      =					1
            if open_knight_tour_helper(UpperCamelCase							,    (i, j)							,    1			):
                return board
            lowerCAmelCase__ :  Any							      =					0

    lowerCAmelCase__ :  Optional[int]							      =					f"""Open Kight Tour cannot be performed on a board of size {n}"""
    raise ValueError(UpperCamelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






'''simple docstring'''



from typing import Mapping

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


__lowercase	:						Dict       =	logging.get_logger(__name__)

__lowercase	:						Optional[Any]       =	{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "umt5"
	A_				=    ["past_key_values"]




	def __init__(   self						,				__a=25_0112						,				__a=512						,				__a=64						,				__a=1024						,				__a=8						,				__a=None						,				__a=6						,				__a=32						,				__a=128						,				__a=0.1						,				__a=1E-6						,				__a=1.0						,				__a="gated-gelu"						,				__a=True						,				__a=True						,				__a="T5Tokenizer"						,				__a=True						,				__a=0						,				__a=1						,				__a=0						,				**__a						,				):

						'''simple docstring'''





						super().__init__(
						    is_encoder_decoder=__a						,				tokenizer_class=__a						,				tie_word_embeddings=__a						,				pad_token_id=__a						,				eos_token_id=__a						,				decoder_start_token_id=__a						,				**__a						,				)
						__a			:       Any					    =  vocab_size
						__a			:       Any					    =  d_model
						__a			:       str					    =  d_kv
						__a			:       Dict					    =  d_ff
						__a			:       Union[str, Any]					    =  num_layers
						__a			:       int					    =  (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__a			:       Optional[int]					    =  num_heads
						__a			:       Tuple					    =  relative_attention_num_buckets
						__a			:       Optional[Any]					    =  relative_attention_max_distance
						__a			:       Optional[int]					    =  dropout_rate
						__a			:       List[Any]					    =  layer_norm_epsilon
						__a			:       int					    =  initializer_factor
						__a			:       Union[str, Any]					    =  feed_forward_proj
						__a			:       Any					    =  use_cache

						__a			:       List[Any]					    =  self.feed_forward_proj.split('-'							)
						__a			:       Dict					    =  act_info[-1]
						__a			:       Dict					    =  act_info[0] == 'gated'

						if len(__a							) > 1 and act_info[0] != "gated" or len(__a							) > 2:
											raise ValueError(
											    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
											    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
											    '\'gated-gelu\' or \'relu\''							)

						if feed_forward_proj == "gated-gelu":
											__a			:       Optional[int]					    =  'gelu_new'




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.d_model




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_heads





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_layers





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  {
						    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
						    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
						}
						if self.use_past:
											__a			:       Dict					    =  'past_encoder_sequence + sequence'
											__a			:       Tuple					    =  {0: 'batch'}
											__a			:       Tuple					    =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
											__a			:       List[Any]					    =  {0: 'batch', 1: 'decoder_sequence'}
											__a			:       int					    =  {0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
											self.fill_with_past_key_values_(__a						,				direction='inputs'							)

						return common_inputs




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 13





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 5E-4



