
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase__          =		logging.get_logger(__name__)







class      A__							(     __magic_name__	):
				lowercase			    =  ['input_values', 'padding_mask']






				def __init__(						self					:      Tuple    ,     a					:      int = 1    ,     a					:      int = 24_000    ,     a					:      float = 0.0    ,     a					:      float = None    ,     a					:      float = None    ,     **a					:      List[str]    ,     ):






							'''simple docstring'''







							super().__init__(feature_size=a    ,     sampling_rate=a    ,     padding_value=a    ,     **a   )
							lowerCAmelCase__      :      Optional[Any]         =		chunk_length_s
							lowerCAmelCase__      :      str         =		overlap






				@property
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							if self.chunk_length_s is None:
										return None
							else:
										return int(self.chunk_length_s * self.sampling_rate   )






				@property
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							if self.chunk_length_s is None or self.overlap is None:
										return None
							else:
										return max(1    ,     int((1.0 - self.overlap) * self.chunk_length   )   )






				def __call__(						self					:      List[Any]    ,     a					:      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,     a					:      Optional[Union[bool, str, PaddingStrategy]] = None    ,     a					:      Optional[bool] = False    ,     a					:      Optional[int] = None    ,     a					:      Optional[Union[str, TensorType]] = None    ,     a					:      Optional[int] = None    ,     ):






							'''simple docstring'''







							if sampling_rate is not None:
										if sampling_rate != self.sampling_rate:
													raise ValueError(
													    f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
													    f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
													    f''' {self.sampling_rate} and not {sampling_rate}.'''   )
							else:
										logger.warning(
										    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
										    'Failing to do so can result in silent errors that might be hard to debug.'   )

							if padding and truncation:
										raise ValueError('Both padding and truncation were set. Make sure you only set one.'   )
							elif padding is None:
										# by default let's pad the inputs
										lowerCAmelCase__      :      List[Any]         =		True

							lowerCAmelCase__      :      Dict         =		bool(
							    isinstance(a    ,     (list, tuple)   ) and (isinstance(raw_audio[0]    ,     (np.ndarray, tuple, list)   ))   )

							if is_batched:
										lowerCAmelCase__      :      List[str]         =		[np.asarray(a    ,     dtype=np.floataa   ).T for audio in raw_audio]
							elif not is_batched and not isinstance(a    ,     np.ndarray   ):
										lowerCAmelCase__      :      Dict         =		np.asarray(a    ,     dtype=np.floataa   )
							elif isinstance(a    ,     np.ndarray   ) and raw_audio.dtype is np.dtype(np.floataa   ):
										lowerCAmelCase__      :      Optional[Any]         =		raw_audio.astype(np.floataa   )

							# always return batch
							if not is_batched:
										lowerCAmelCase__      :      List[Any]         =		[np.asarray(a   ).T]

							# verify inputs are valid
							for idx, example in enumerate(a   ):
										if example.ndim > 2:
													raise ValueError(f'''Expected input shape (channels, length) but got shape {example.shape}'''   )
										if self.feature_size == 1 and example.ndim != 1:
													raise ValueError(f'''Expected mono audio but example has {example.shape[-1]} channels'''   )
										if self.feature_size == 2 and example.shape[-1] != 2:
													raise ValueError(f'''Expected stereo audio but example has {example.shape[-1]} channels'''   )

							lowerCAmelCase__      :      int         =		None
							lowerCAmelCase__      :      Any         =		BatchFeature({'input_values': raw_audio}   )
							if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
										if truncation:
													lowerCAmelCase__      :      Dict         =		min(array.shape[0] for array in raw_audio   )
													lowerCAmelCase__      :      Dict         =		int(np.floor(max_length / self.chunk_stride   )   )
													lowerCAmelCase__      :      Optional[Any]         =		(nb_step - 1) * self.chunk_stride + self.chunk_length
										elif padding:
													lowerCAmelCase__      :      Any         =		max(array.shape[0] for array in raw_audio   )
													lowerCAmelCase__      :      Union[str, Any]         =		int(np.ceil(max_length / self.chunk_stride   )   )
													lowerCAmelCase__      :      Optional[int]         =		(nb_step - 1) * self.chunk_stride + self.chunk_length
													lowerCAmelCase__      :      Optional[int]         =		'max_length'
										else:
													lowerCAmelCase__      :      Dict         =		input_values

        # normal padding on batch
							if padded_inputs is None:
										lowerCAmelCase__      :      Dict         =		self.pad(
										    a    ,     max_length=a    ,     truncation=a    ,     padding=a    ,     return_attention_mask=a    ,     )
										if padding:
													lowerCAmelCase__      :      Union[str, Any]         =		padded_inputs.pop('attention_mask'   )

							lowerCAmelCase__      :      List[str]         =		[]
							for example in padded_inputs.pop('input_values'   ):
										if self.feature_size == 1:
													lowerCAmelCase__      :      Dict         =		example[..., None]
										input_values.append(example.T   )

							lowerCAmelCase__      :      Optional[Any]         =		input_values
							if return_tensors is not None:
										lowerCAmelCase__      :      int         =		padded_inputs.convert_to_tensors(a   )

							return padded_inputs

from __future__ import annotations

lowerCamelCase__          =		list[list[int]]

# assigning initial values to the grid
lowerCamelCase__          =		[
    [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
lowerCamelCase__          =		[
    [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  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		bool:
			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  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		tuple[int, int] | None:
			for i in range(9     ):
						for j in range(9     ):
									if grid[i][j] == 0:
												return i, j
			return None
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Matrix | None:
			if location := find_empty_location(SCREAMING_SNAKE_CASE_     ):
						lowerCAmelCase__      ,				lowerCAmelCase__      :      Any         =		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_     ):
									lowerCAmelCase__      :      Optional[Any]         =		digit

									if sudoku(SCREAMING_SNAKE_CASE_     ) is not None:
												return grid

									lowerCAmelCase__      :      List[Any]         =		0

			return None
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		None:
			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:""")
										lowerCamelCase__          =		sudoku(example_grid)
										if solution is not None:
															print_solution(solution)
										else:
															print("""Cannot find a solution.""")