



"""simple docstring"""
from __future__ import annotations

SCREAMING_SNAKE_CASE_				= list[list[int]]

# assigning initial values to the grid
SCREAMING_SNAKE_CASE_				= [
    [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
SCREAMING_SNAKE_CASE_				= [
    [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__	):
				a_						,				a_     :      Optional[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__	):
						a_     :      List[Any] 						=     digit

						if sudoku(SCREAMING_SNAKE_CASE__	) is not None:
								return grid

						a_     :      Tuple 						=     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:""")
										SCREAMING_SNAKE_CASE_				= sudoku(example_grid)
										if solution is not None:
															print_solution(solution)
										else:
															print("""Cannot find a solution.""")




"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch

if is_datasets_available():
					from datasets import load_dataset

SCREAMING_SNAKE_CASE_				= random.Random()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=1.0, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None	)			->      Tuple:
		if rng is None:
				a_     :      Any 						=     global_rng

		a_     :      Dict 						=     []
		for batch_idx in range(shape[0]	):
				values.append([]	)
				for _ in range(shape[1]	):
						values[-1].append(rng.random() * scale	)

		return values




class 			snake_case_						(						unittest.TestCase				):






							def __init__(   self			,     a_			,     a_=7			,     a_=4_0_0			,     a_=2_0_0_0			,     a_=2_0_4_8			,     a_=1_2_8			,     a_=1			,     a_=5_1_2			,     a_=3_0			,     a_=4_4_1_0_0			,     ):
									a_     :      Optional[Any] 						=     parent
									a_     :      Tuple 						=     batch_size
									a_     :      Union[str, Any] 						=     min_seq_length
									a_     :      Dict 						=     max_seq_length
									a_     :      Union[str, Any] 						=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									a_     :      int 						=     spectrogram_length
									a_     :      Optional[Any] 						=     feature_size
									a_     :      Optional[int] 						=     num_audio_channels
									a_     :      List[str] 						=     hop_length
									a_     :      Optional[int] 						=     chunk_length
									a_     :      List[str] 						=     sampling_rate






							def snake_case_  (   self      ):
									return {
									    "spectrogram_length": self.spectrogram_length,
									    "feature_size": self.feature_size,
									    "num_audio_channels": self.num_audio_channels,
									    "hop_length": self.hop_length,
									    "chunk_length": self.chunk_length,
									    "sampling_rate": self.sampling_rate,
									}






							def snake_case_  (   self			,     a_=False			,     a_=False      ):
									def _flatten(a_      ):
											return list(itertools.chain(*a_      )      )

									if equal_length:
											a_     :      Tuple 						=     [floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
									else:
											# make sure that inputs increase in size
											a_     :      Optional[Any] 						=     [
											    floats_list((x, self.feature_size)      )
											    for x in range(self.min_seq_length			,     self.max_seq_length			,     self.seq_length_diff      )
											]
									if numpify:
											a_     :      Tuple 						=     [np.asarray(a_      ) for x in speech_inputs]
									return speech_inputs




@require_torch
@require_torchaudio
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					TvltFeatureExtractor






							def snake_case_  (   self      ):
									a_     :      Dict 						=     TvltFeatureExtractionTester(self      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     self.feature_extraction_class(**self.feat_extract_dict      )
									self.assertTrue(hasattr(a_			,     "spectrogram_length"      )      )
									self.assertTrue(hasattr(a_			,     "feature_size"      )      )
									self.assertTrue(hasattr(a_			,     "num_audio_channels"      )      )
									self.assertTrue(hasattr(a_			,     "hop_length"      )      )
									self.assertTrue(hasattr(a_			,     "chunk_length"      )      )
									self.assertTrue(hasattr(a_			,     "sampling_rate"      )      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.feature_extraction_class(**self.feat_extract_dict      )

									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      List[Any] 						=     feat_extract_first.save_pretrained(a_      )[0]
											check_json_file_has_correct_format(a_      )
											a_     :      Tuple 						=     self.feature_extraction_class.from_pretrained(a_      )

									a_     :      Dict 						=     feat_extract_first.to_dict()
									a_     :      List[str] 						=     feat_extract_second.to_dict()
									a_     :      List[Any] 						=     dict_first.pop("mel_filters"      )
									a_     :      str 						=     dict_second.pop("mel_filters"      )
									self.assertTrue(np.allclose(a_			,     a_      )      )
									self.assertEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self.feature_extraction_class(**self.feat_extract_dict      )

									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      Dict 						=     os.path.join(a_			,     "feat_extract.json"      )
											feat_extract_first.to_json_file(a_      )
											a_     :      str 						=     self.feature_extraction_class.from_json_file(a_      )

									a_     :      Dict 						=     feat_extract_first.to_dict()
									a_     :      str 						=     feat_extract_second.to_dict()
									a_     :      int 						=     dict_first.pop("mel_filters"      )
									a_     :      List[Any] 						=     dict_second.pop("mel_filters"      )
									self.assertTrue(np.allclose(a_			,     a_      )      )
									self.assertEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									# Initialize feature_extractor
									a_     :      str 						=     self.feature_extraction_class(**self.feat_extract_dict      )

									# create three inputs of length 800, 1000, and 1200
									a_     :      Optional[Any] 						=     [floats_list((1, x)      )[0] for x in range(8_0_0			,     1_4_0_0			,     2_0_0      )]
									a_     :      Union[str, Any] 						=     [np.asarray(a_      ) for speech_input in speech_inputs]

									# Test not batched input
									a_     :      Dict 						=     feature_extractor(np_speech_inputs[0]			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).audio_values

									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

									# Test batched
									a_     :      int 						=     feature_extractor(a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).audio_values

									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

									# Test audio masking
									a_     :      Optional[int] 						=     feature_extractor(
									    a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0			,     mask_audio=a_      ).audio_values

									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

									# Test 2-D numpy arrays are batched.
									a_     :      List[str] 						=     [floats_list((1, x)      )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
									a_     :      Union[str, Any] 						=     np.asarray(a_      )
									a_     :      Dict 						=     feature_extractor(a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).audio_values
									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Optional[int] 						=     load_dataset("hf-internal-testing/librispeech_asr_dummy"			,     "clean"			,     split="validation"      )
									# automatic decoding with librispeech
									a_     :      List[str] 						=     ds.sort("id"      ).select(range(a_      )      )[:num_samples]["audio"]

									return [x["array"] for x in speech_samples]






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self._load_datasamples(1      )
									a_     :      Any 						=     TvltFeatureExtractor()
									a_     :      Union[str, Any] 						=     feature_extractor(a_			,     return_tensors="pt"      ).audio_values

									self.assertEquals(audio_values.shape			,     (1, 1, 1_9_2, 1_2_8)      )

									a_     :      Dict 						=     torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]      )
									self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,     a_			,     atol=1e-4      )      )