




"""simple docstring"""




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


a_	   =    logging.get_logger(__name__)

class    UpperCAmelCase_  (       snake_case						):
		UpperCamelCase						 =["input_values", "padding_mask"]
		def __init__(		self							,		UpperCamelCase_ = 1							,		UpperCamelCase_ = 2_40_00							,		UpperCamelCase_ = 0.0							,		UpperCamelCase_ = None							,		UpperCamelCase_ = None							,		**UpperCamelCase_							,		)		->    str:
					super().__init__(feature_size=UpperCamelCase_							,		sampling_rate=UpperCamelCase_							,		padding_value=UpperCamelCase_							,		**UpperCamelCase_    )
					__lowercase       :   Optional[Any]        =    chunk_length_s
					__lowercase       :   str        =    overlap
		@property
		def      _lowerCamelCase				(		self    )		->    str:







					if self.chunk_length_s is None:
								return None
					else:
								return int(self.chunk_length_s * self.sampling_rate    )
		@property
		def      _lowerCamelCase				(		self    )		->    str:







					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							,		UpperCamelCase_							,		UpperCamelCase_ = None							,		UpperCamelCase_ = False							,		UpperCamelCase_ = None							,		UpperCamelCase_ = None							,		UpperCamelCase_ = None							,		)		->    List[Any]:







					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
								__lowercase       :   List[Any]        =    True

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

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

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

					# verify inputs are valid
					for idx, example in enumerate(UpperCamelCase_    ):
								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"""    )

					__lowercase       :   int        =    None
					__lowercase       :   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:
											__lowercase       :   Dict        =    min(array.shape[0] for array in raw_audio    )
											__lowercase       :   Dict        =    int(np.floor(max_length / self.chunk_stride    )    )
											__lowercase       :   Optional[Any]        =    (nb_step - 1) * self.chunk_stride + self.chunk_length
								elif padding:
											__lowercase       :   Any        =    max(array.shape[0] for array in raw_audio    )
											__lowercase       :   Union[str, Any]        =    int(np.ceil(max_length / self.chunk_stride    )    )
											__lowercase       :   Optional[int]        =    (nb_step - 1) * self.chunk_stride + self.chunk_length
											__lowercase       :   Optional[int]        =    'max_length'
								else:
											__lowercase       :   Dict        =    input_values

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

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

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

					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.""")