
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCamelCase__          =		random.Random()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=1.0       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None     )				->		List[Any]:
			if rng is None:
						lowerCAmelCase__      :      Union[str, Any]         =		global_rng

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

			return values







class      A__							(     unittest.TestCase	):






				def __init__(						self					:      List[str]    ,     a					:      List[str]    ,     a					:      Tuple=7    ,     a					:      Union[str, Any]=400    ,     a					:      Union[str, Any]=2_000    ,     a					:      Optional[Any]=1    ,     a					:      List[str]=0.0    ,     a					:      Optional[Any]=16_000    ,     a					:      Union[str, Any]=True    ,     a					:      Optional[int]=True    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		parent
							lowerCAmelCase__      :      Union[str, Any]         =		batch_size
							lowerCAmelCase__      :      Dict         =		min_seq_length
							lowerCAmelCase__      :      Dict         =		max_seq_length
							lowerCAmelCase__      :      Any         =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							lowerCAmelCase__      :      Dict         =		feature_size
							lowerCAmelCase__      :      Dict         =		padding_value
							lowerCAmelCase__      :      Optional[Any]         =		sampling_rate
							lowerCAmelCase__      :      List[str]         =		return_attention_mask
							lowerCAmelCase__      :      Optional[Any]         =		do_normalize






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}






				def 							_lowerCamelCase		(						self					:      List[str]    ,     a					:      Optional[int]=False    ,     a					:      List[str]=False   ):






							'''simple docstring'''







							def _flatten(a					:      Tuple   ):
										return list(itertools.chain(*a   )   )

							if equal_length:
										lowerCAmelCase__      :      List[str]         =		floats_list((self.batch_size, self.max_seq_length)   )
							else:
										# make sure that inputs increase in size
										lowerCAmelCase__      :      List[str]         =		[
										    _flatten(floats_list((x, self.feature_size)   )   )
										    for x in range(self.min_seq_length    ,     self.max_seq_length    ,     self.seq_length_diff   )
										]

							if numpify:
										lowerCAmelCase__      :      Dict         =		[np.asarray(a   ) for x in speech_inputs]

							return speech_inputs







class      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  WavaVecaFeatureExtractor






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		WavaVecaFeatureExtractionTester(self   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      Optional[int]   ):






							'''simple docstring'''







							self.assertTrue(np.all(np.mean(a    ,     axis=0   ) < 1E-3   )   )
							self.assertTrue(np.all(np.abs(np.var(a    ,     axis=0   ) - 1   ) < 1E-3   )   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							# create three inputs of length 800, 1000, and 1200
							lowerCAmelCase__      :      int         =		[floats_list((1, x)   )[0] for x in range(800    ,     1_400    ,     200   )]
							lowerCAmelCase__      :      Any         =		[np.asarray(a   ) for speech_input in speech_inputs]

							# Test not batched input
							lowerCAmelCase__      :      Tuple         =		feat_extract(speech_inputs[0]    ,     return_tensors='np'   ).input_values
							lowerCAmelCase__      :      Optional[int]         =		feat_extract(np_speech_inputs[0]    ,     return_tensors='np'   ).input_values
							self.assertTrue(np.allclose(a    ,     a    ,     atol=1E-3   )   )

							# Test batched
							lowerCAmelCase__      :      int         =		feat_extract(a    ,     return_tensors='np'   ).input_values
							lowerCAmelCase__      :      Union[str, Any]         =		feat_extract(a    ,     return_tensors='np'   ).input_values
							for enc_seq_a, enc_seq_a in zip(a    ,     a   ):
										self.assertTrue(np.allclose(a    ,     a    ,     atol=1E-3   )   )

							# Test 2-D numpy arrays are batched.
							lowerCAmelCase__      :      List[Any]         =		[floats_list((1, x)   )[0] for x in (800, 800, 800)]
							lowerCAmelCase__      :      Any         =		np.asarray(a   )
							lowerCAmelCase__      :      List[str]         =		feat_extract(a    ,     return_tensors='np'   ).input_values
							lowerCAmelCase__      :      Optional[int]         =		feat_extract(a    ,     return_tensors='np'   ).input_values
							for enc_seq_a, enc_seq_a in zip(a    ,     a   ):
										self.assertTrue(np.allclose(a    ,     a    ,     atol=1E-3   )   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							lowerCAmelCase__      :      Optional[Any]         =		[floats_list((1, x)   )[0] for x in range(800    ,     1_400    ,     200   )]

							lowerCAmelCase__      :      Union[str, Any]         =		['longest', 'max_length', 'do_not_pad']
							lowerCAmelCase__      :      List[str]         =		[None, 1_600, None]
							for max_length, padding in zip(a    ,     a   ):
										lowerCAmelCase__      :      List[str]         =		feat_extract(a    ,     padding=a    ,     max_length=a    ,     return_tensors='np'   )
										lowerCAmelCase__      :      int         =		processed.input_values

										self._check_zero_mean_unit_variance(input_values[0][:800]   )
										self.assertTrue(input_values[0][800:].sum() < 1E-6   )
										self._check_zero_mean_unit_variance(input_values[1][:1_000]   )
										self.assertTrue(input_values[0][1_000:].sum() < 1E-6   )
										self._check_zero_mean_unit_variance(input_values[2][:1_200]   )






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							lowerCAmelCase__      :      List[str]         =		range(800    ,     1_400    ,     200   )
							lowerCAmelCase__      :      Optional[int]         =		[floats_list((1, x)   )[0] for x in lengths]

							lowerCAmelCase__      :      List[Any]         =		['longest', 'max_length', 'do_not_pad']
							lowerCAmelCase__      :      Optional[int]         =		[None, 1_600, None]

							for max_length, padding in zip(a    ,     a   ):
										lowerCAmelCase__      :      Dict         =		feat_extract(a    ,     max_length=a    ,     padding=a   )
										lowerCAmelCase__      :      Tuple         =		processed.input_values

										self._check_zero_mean_unit_variance(input_values[0][:800]   )
										self._check_zero_mean_unit_variance(input_values[1][:1_000]   )
										self._check_zero_mean_unit_variance(input_values[2][:1_200]   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							lowerCAmelCase__      :      List[Any]         =		[floats_list((1, x)   )[0] for x in range(800    ,     1_400    ,     200   )]
							lowerCAmelCase__      :      Dict         =		feat_extract(
							    a    ,     truncation=a    ,     max_length=1_000    ,     padding='max_length'    ,     return_tensors='np'   )
							lowerCAmelCase__      :      int         =		processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]   )
							self._check_zero_mean_unit_variance(input_values[1]   )
							self._check_zero_mean_unit_variance(input_values[2]   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							lowerCAmelCase__      :      List[Any]         =		[floats_list((1, x)   )[0] for x in range(800    ,     1_400    ,     200   )]
							lowerCAmelCase__      :      Tuple         =		feat_extract(
							    a    ,     truncation=a    ,     max_length=1_000    ,     padding='longest'    ,     return_tensors='np'   )
							lowerCAmelCase__      :      List[Any]         =		processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]   )
							self._check_zero_mean_unit_variance(input_values[1, :1_000]   )
							self._check_zero_mean_unit_variance(input_values[2]   )

							# make sure that if max_length < longest -> then pad to max_length
							self.assertTrue(input_values.shape == (3, 1_000)   )

							lowerCAmelCase__      :      Any         =		[floats_list((1, x)   )[0] for x in range(800    ,     1_400    ,     200   )]
							lowerCAmelCase__      :      Any         =		feat_extract(
							    a    ,     truncation=a    ,     max_length=2_000    ,     padding='longest'    ,     return_tensors='np'   )
							lowerCAmelCase__      :      Any         =		processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]   )
							self._check_zero_mean_unit_variance(input_values[1, :1_000]   )
							self._check_zero_mean_unit_variance(input_values[2]   )

							# make sure that if max_length > longest -> then pad to longest
							self.assertTrue(input_values.shape == (3, 1_200)   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							import torch

							lowerCAmelCase__      :      Union[str, Any]         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							lowerCAmelCase__      :      int         =		np.random.rand(100   ).astype(np.floataa   )
							lowerCAmelCase__      :      Union[str, Any]         =		np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
										lowerCAmelCase__      :      str         =		feature_extractor.pad([{'input_values': inputs}]    ,     return_tensors='np'   )
										self.assertTrue(np_processed.input_values.dtype == np.floataa   )
										lowerCAmelCase__      :      Optional[Any]         =		feature_extractor.pad([{'input_values': inputs}]    ,     return_tensors='pt'   )
										self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )






				@slow
				@require_torch
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
										lowerCAmelCase__      :      Optional[Any]         =		WavaVecaConfig.from_pretrained(a   )
										lowerCAmelCase__      :      Tuple         =		WavaVecaFeatureExtractor.from_pretrained(a   )

										# only "layer" feature extraction norm should make use of
										# attention_mask
										self.assertEqual(feat_extract.return_attention_mask    ,     config.feat_extract_norm == 'layer'   )

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.""")