





'''simple docstring'''






def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    ) ->int:
						return len_board * len_board_column * row + column





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__					,	lowercase__					,	lowercase__					,	lowercase__					,	) ->bool:

						if board[row][column] != word[word_index]:
												return False

						if word_index == len(lowercase__    ) - 1:
												return True

						_snake_case: Tuple	       =		[(0, 1), (0, -1), (-1, 0), (1, 0)]
						_snake_case: Dict	       =		len(lowercase__    )
						_snake_case: Dict	       =		len(board[0]    )
						for direction in traverts_directions:
												_snake_case: Optional[Any]	       =		row + direction[0]
												_snake_case: List[Any]	       =		column + direction[1]
												if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
																		continue

												_snake_case: Any	       =		get_point_key(lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    )
												if key in visited_points_set:
																		continue

												visited_points_set.add(lowercase__    )
												if exits_word(lowercase__					,	lowercase__					,	lowercase__					,	lowercase__					,	word_index + 1					,	lowercase__    ):
																		return True

												visited_points_set.remove(lowercase__    )

						return False





def    lowercase_   (		lowercase__					,	lowercase__    ) ->bool:
						_snake_case: int	       =		(
						    'The board should be a non empty matrix of single chars strings.'
						)

						_snake_case: Any	       =		len(lowercase__    )
						if not isinstance(lowercase__					,	lowercase__    ) or len(lowercase__    ) == 0:
												raise ValueError(lowercase__    )

						for row in board:
												if not isinstance(lowercase__					,	lowercase__    ) or len(lowercase__    ) == 0:
																		raise ValueError(lowercase__    )

												for item in row:
																		if not isinstance(lowercase__					,	lowercase__    ) or len(lowercase__    ) != 1:
																								raise ValueError(lowercase__    )

    # Validate word
						if not isinstance(lowercase__					,	lowercase__    ) or len(lowercase__    ) == 0:
												raise ValueError(
												    'The word parameter should be a string of length greater than 0.'    )

						_snake_case: Dict	       =		len(board[0]    )
						for i in range(lowercase__    ):
												for j in range(lowercase__    ):
																		if exits_word(
																		    lowercase__					,	lowercase__					,	lowercase__					,	lowercase__					,	0					,	{get_point_key(lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    )}    ):
																								return True

						return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()












'''simple docstring'''






import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor







class       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	bool = False							):





											'''simple docstring'''





											_snake_case: Any	       =		10

											if only_cross_attention:
																	_snake_case: Optional[int]	       =		12
											else:
																	# when only cross attention is not set, the cross attention dim must be the same as the query dim
																	_snake_case: Optional[int]	       =		query_dim

											return {
											    "query_dim": query_dim,
											    "cross_attention_dim": cross_attention_dim,
											    "heads": 2,
											    "dim_head": 4,
											    "added_kv_proj_dim": 6,
											    "norm_num_groups": 1,
											    "only_cross_attention": only_cross_attention,
											    "processor": AttnAddedKVProcessor(),
											}


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		2

											_snake_case: Union[str, Any]	       =		torch.rand(__snake_case							,  __snake_case							,  3							,  2							)
											_snake_case: Optional[int]	       =		torch.rand(__snake_case							,  4							,  __snake_case							)
											_snake_case: int	       =		None

											return {
											    "hidden_states": hidden_states,
											    "encoder_hidden_states": encoder_hidden_states,
											    "attention_mask": attention_mask,
											}




					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											torch.manual_seed(0							)

											_snake_case: int	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Union[str, Any]	       =		Attention(**__snake_case							)

											self.assertTrue(attn.to_k is not None							)
											self.assertTrue(attn.to_v is not None							)

											_snake_case: Optional[int]	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

											# only self attention

											torch.manual_seed(0							)

											_snake_case: str	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Optional[Any]	       =		Attention(**__snake_case							)

											self.assertTrue(attn.to_k is None							)
											self.assertTrue(attn.to_v is None							)

											_snake_case: Dict	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

											self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()							)







class       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: str	       =		DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe'							,  safety_checker=__snake_case							)

											_snake_case: int	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											# the initial conversion succeeds
											_snake_case: Dict	       =		DiffusionPipeline.from_pretrained(
											    'hf-internal-testing/tiny-stable-diffusion-pipe'							,  device_map='sequential'							,  safety_checker=__snake_case							)

											_snake_case: Any	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											with tempfile.TemporaryDirectory() as tmpdir:
																	# save the converted model
																	pipe.save_pretrained(__snake_case							)

																	# can also load the converted weights
																	_snake_case: Union[str, Any]	       =		DiffusionPipeline.from_pretrained(__snake_case							,  device_map='sequential'							,  safety_checker=__snake_case							)

											_snake_case: Union[str, Any]	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											self.assertTrue(np.allclose(__snake_case							,  __snake_case							,  atol=1e-5							)							)
											self.assertTrue(np.allclose(__snake_case							,  __snake_case							,  atol=1e-5							)							)





