

from __future__ import annotations

UpperCamelCase__  :      List[str]			=    list[list[int]]

# assigning initial values to the grid
UpperCamelCase__  :      Dict			=    [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
UpperCamelCase__  :      List[str]			=    [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Any       ):





				"""simple docstring"""


				for i in range(9       ):
								if grid[row][i] == n or grid[i][column] == n:
												return False

				for i in range(3       ):
								for j in range(3       ):
												if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
																return False

				return True


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple       ):





				"""simple docstring"""


				for i in range(9       ):
								for j in range(9       ):
												if grid[i][j] == 0:
																return i, j
				return None


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





				"""simple docstring"""


				if location := find_empty_location(__SCREAMING_SNAKE_CASE       ):
								SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						location
				else:
								# If the location is ``None``, then the grid is solved.
								return grid

				for digit in range(1 ,   10       ):
								if is_safe(__SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE_									=						digit

												if sudoku(__SCREAMING_SNAKE_CASE       ) is not None:
																return grid

												SCREAMING_SNAKE_CASE_									=						0

				return None


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str]       ):





				"""simple docstring"""


				for row in grid:
								for cell in row:
												print(__SCREAMING_SNAKE_CASE ,   end=' '       )
								print()


if __name__ == "__main__":
							# make a copy of grid so that you can compare with the unmodified grid
							for example_grid in (initial_grid, no_solution):
														print("\nExample grid:\n" + "=" * 20)
														print_solution(example_grid)
														print("\nExample grid solution:")
														UpperCamelCase__  :      str			=    sudoku(example_grid)
														if solution is not None:
																					print_solution(solution)
														else:
																					print("Cannot find a solution.")





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__  :      List[Any]			=    logging.get_logger(__name__)

UpperCamelCase__  :      List[str]			=    {
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Any						 =  'biogpt'

  def __init__(					self						,		_A=42384						,		_A=1024						,		_A=24						,		_A=16						,		_A=4096						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=1024						,		_A=0.0_2						,		_A=1E-12						,		_A=True						,		_A=True						,		_A=0.0						,		_A=0.0						,		_A=1						,		_A=0						,		_A=2						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						vocab_size
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings
      SCREAMING_SNAKE_CASE_									=						hidden_size
      SCREAMING_SNAKE_CASE_									=						num_hidden_layers
      SCREAMING_SNAKE_CASE_									=						num_attention_heads
      SCREAMING_SNAKE_CASE_									=						intermediate_size
      SCREAMING_SNAKE_CASE_									=						hidden_act
      SCREAMING_SNAKE_CASE_									=						hidden_dropout_prob
      SCREAMING_SNAKE_CASE_									=						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_									=						initializer_range
      SCREAMING_SNAKE_CASE_									=						layer_norm_eps
      SCREAMING_SNAKE_CASE_									=						scale_embedding
      SCREAMING_SNAKE_CASE_									=						use_cache
      SCREAMING_SNAKE_CASE_									=						layerdrop
      SCREAMING_SNAKE_CASE_									=						activation_dropout
      super().__init__(pad_token_id=_A						,		bos_token_id=_A						,		eos_token_id=_A						,		**_A)


