






'''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

UpperCamelCase      					=random.Random()



def 				snake_case       (  a_				:			Any     , a_				:			List[str]=1.0     , a_				:			int=None     , a_				:			str=None						)							->					Union[str, Any]:





      """simple docstring"""

      if rng is None:
            UpperCamelCase_					: Optional[Any]            =				global_rng

      UpperCamelCase_					: 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    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=7      ,  __lowerCAmelCase=4_00      ,  __lowerCAmelCase=20_00      ,  __lowerCAmelCase=20_48      ,  __lowerCAmelCase=1_28      ,  __lowerCAmelCase=1      ,  __lowerCAmelCase=5_12      ,  __lowerCAmelCase=30      ,  __lowerCAmelCase=4_41_00      ,  ):
       UpperCamelCase_					: Tuple            =				parent
       UpperCamelCase_					: str            =				batch_size
       UpperCamelCase_					: str            =				min_seq_length
       UpperCamelCase_					: List[str]            =				max_seq_length
       UpperCamelCase_					: List[Any]            =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       UpperCamelCase_					: Any            =				spectrogram_length
       UpperCamelCase_					: List[Any]            =				feature_size
       UpperCamelCase_					: Union[str, Any]            =				num_audio_channels
       UpperCamelCase_					: List[Any]            =				hop_length
       UpperCamelCase_					: str            =				chunk_length
       UpperCamelCase_					: int            =				sampling_rate
 def 	_UpperCAmelCase							(				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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=False			):
       def _flatten(__lowerCAmelCase			):
             return list(itertools.chain(*__lowerCAmelCase			)			)

       if equal_length:
             UpperCamelCase_					: Dict            =				[floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
       else:
             # make sure that inputs increase in size
             UpperCamelCase_					: 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:
             UpperCamelCase_					: Optional[int]            =				[np.asarray(__lowerCAmelCase			) for x in speech_inputs]
       return speech_inputs






@require_torch
@require_torchaudio
class      A     (   SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    str			  =    TvltFeatureExtractor
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				TvltFeatureExtractionTester(self			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				self.feature_extraction_class(**self.feat_extract_dict			)
       self.assertTrue(hasattr(__lowerCAmelCase      ,  """spectrogram_length"""			)			)
       self.assertTrue(hasattr(__lowerCAmelCase      ,  """feature_size"""			)			)
       self.assertTrue(hasattr(__lowerCAmelCase      ,  """num_audio_channels"""			)			)
       self.assertTrue(hasattr(__lowerCAmelCase      ,  """hop_length"""			)			)
       self.assertTrue(hasattr(__lowerCAmelCase      ,  """chunk_length"""			)			)
       self.assertTrue(hasattr(__lowerCAmelCase      ,  """sampling_rate"""			)			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				self.feature_extraction_class(**self.feat_extract_dict			)

       with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCamelCase_					: Optional[int]            =				feat_extract_first.save_pretrained(__lowerCAmelCase			)[0]
             check_json_file_has_correct_format(__lowerCAmelCase			)
             UpperCamelCase_					: int            =				self.feature_extraction_class.from_pretrained(__lowerCAmelCase			)

       UpperCamelCase_					: Optional[Any]            =				feat_extract_first.to_dict()
       UpperCamelCase_					: Any            =				feat_extract_second.to_dict()
       UpperCamelCase_					: Optional[int]            =				dict_first.pop("""mel_filters"""			)
       UpperCamelCase_					: Dict            =				dict_second.pop("""mel_filters"""			)
       self.assertTrue(np.allclose(__lowerCAmelCase      ,  __lowerCAmelCase			)			)
       self.assertEqual(__lowerCAmelCase      ,  __lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[int]            =				self.feature_extraction_class(**self.feat_extract_dict			)

       with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCamelCase_					: int            =				os.path.join(__lowerCAmelCase      ,  """feat_extract.json"""			)
             feat_extract_first.to_json_file(__lowerCAmelCase			)
             UpperCamelCase_					: List[Any]            =				self.feature_extraction_class.from_json_file(__lowerCAmelCase			)

       UpperCamelCase_					: List[Any]            =				feat_extract_first.to_dict()
       UpperCamelCase_					: Optional[int]            =				feat_extract_second.to_dict()
       UpperCamelCase_					: Tuple            =				dict_first.pop("""mel_filters"""			)
       UpperCamelCase_					: List[str]            =				dict_second.pop("""mel_filters"""			)
       self.assertTrue(np.allclose(__lowerCAmelCase      ,  __lowerCAmelCase			)			)
       self.assertEqual(__lowerCAmelCase      ,  __lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       # Initialize feature_extractor
       UpperCamelCase_					: List[str]            =				self.feature_extraction_class(**self.feat_extract_dict			)

       # create three inputs of length 800, 1000, and 1200
       UpperCamelCase_					: Optional[Any]            =				[floats_list((1, x)			)[0] for x in range(8_00      ,  14_00      ,  2_00			)]
       UpperCamelCase_					: str            =				[np.asarray(__lowerCAmelCase			) for speech_input in speech_inputs]

       # Test not batched input
       UpperCamelCase_					: str            =				feature_extractor(np_speech_inputs[0]      ,  return_tensors="""np"""      ,  sampling_rate=4_41_00			).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
       UpperCamelCase_					: List[Any]            =				feature_extractor(__lowerCAmelCase      ,  return_tensors="""np"""      ,  sampling_rate=4_41_00			).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
       UpperCamelCase_					: Dict            =				feature_extractor(
           __lowerCAmelCase      ,  return_tensors="""np"""      ,  sampling_rate=4_41_00      ,  mask_audio=__lowerCAmelCase			).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.
       UpperCamelCase_					: List[Any]            =				[floats_list((1, x)			)[0] for x in (8_00, 8_00, 8_00)]
       UpperCamelCase_					: int            =				np.asarray(__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				feature_extractor(__lowerCAmelCase      ,  return_tensors="""np"""      ,  sampling_rate=4_41_00			).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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: List[str]            =				load_dataset("""hf-internal-testing/librispeech_asr_dummy"""      ,  """clean"""      ,  split="""validation"""			)
       # automatic decoding with librispeech
       UpperCamelCase_					: Tuple            =				ds.sort("""id"""			).select(range(__lowerCAmelCase			)			)[:num_samples]["""audio"""]

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





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				self._load_datasamples(1			)
       UpperCamelCase_					: Optional[int]            =				TvltFeatureExtractor()
       UpperCamelCase_					: List[str]            =				feature_extractor(__lowerCAmelCase      ,  return_tensors="""pt"""			).audio_values

       self.assertEquals(audio_values.shape      ,  (1, 1, 1_92, 1_28)			)

       UpperCamelCase_					: Dict            =				torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]			)
       self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      ,  __lowerCAmelCase      ,  atol=1E-4			)			)














'''simple docstring'''

from __future__ import annotations



def 				snake_case       (  a_				:			list[int]     , a_				:			list[int]     , a_				:			list[int]     , a_				:			list[list[str]]     , a_				:			int     , )							->					None:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				len(a_						)

      # If row is equal to the size of the board it means there are a queen in each row in
      # the current board (possible_board)
      if row == n:
            # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
            # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
            boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]						)
            return

      # We iterate each column in the row to find all possible results in each row
      for col in range(a_						):
            # We apply that we learned previously. First we check that in the current board
            # (possible_board) there are not other same value because if there is it means
            # that there are a collision in vertical. Then we apply the two formulas we
            # learned before:
            #
            # 45º: y - x = b or 45: row - col = b
            # 135º: y + x = b or row + col = b.
            #
            # And we verify if the results of this two formulas not exist in their variables
            # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
            #
            # If any or these are True it means there is a collision so we continue to the
            # next value in the for loop.
            if (
                col in possible_board
                or row - col in diagonal_right_collisions
                or row + col in diagonal_left_collisions
            ):
                  continue

            # If it is False we call dfs function again and we update the inputs
            depth_first_search(
                [*possible_board, col]     , [*diagonal_right_collisions, row - col]     , [*diagonal_left_collisions, row + col]     , a_     , a_     , )



def 				snake_case       (  a_				:			int						)							->					None:





      """simple docstring"""

      UpperCamelCase_					: list[list[str]]            =				[]
      depth_first_search([]     , []     , []     , a_     , a_						)

      # Print all the boards
      for board in boards:
            for column in board:
                  print(a_						)
            print(""""""						)

      print(len(a_						)     , """solutions were found."""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      n_queens_solution(4)






