






'''simple docstring'''

import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCamelCase      					=random.Random()

if is_torch_available():
      import torch



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





      """simple docstring"""

      if rng is None:
            UpperCamelCase_					: Any            =				global_rng

      UpperCamelCase_					: Tuple            =				[]
      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=1      ,  __lowerCAmelCase=0.0      ,  __lowerCAmelCase=1_60_00      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=True      ,  ):
       UpperCamelCase_					: Tuple            =				parent
       UpperCamelCase_					: Tuple            =				batch_size
       UpperCamelCase_					: Optional[int]            =				min_seq_length
       UpperCamelCase_					: Dict            =				max_seq_length
       UpperCamelCase_					: Dict            =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       UpperCamelCase_					: Any            =				feature_size
       UpperCamelCase_					: Tuple            =				padding_value
       UpperCamelCase_					: Dict            =				sampling_rate
       UpperCamelCase_					: str            =				return_attention_mask
       UpperCamelCase_					: Union[str, Any]            =				do_normalize
 def 	_UpperCAmelCase							(				self			):
       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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=False			):
       def _flatten(__lowerCAmelCase			):
             return list(itertools.chain(*__lowerCAmelCase			)			)

       if equal_length:
             UpperCamelCase_					: str            =				floats_list((self.batch_size, self.max_seq_length)			)
       else:
             # make sure that inputs increase in size
             UpperCamelCase_					: Union[str, Any]            =				[
                 _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:
             UpperCamelCase_					: 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      :    int			  =    ASTFeatureExtractor
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				ASTFeatureExtractionTester(self			)
 def 	_UpperCAmelCase							(				self			):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       UpperCamelCase_					: str            =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
       # create three inputs of length 800, 1000, and 1200
       UpperCamelCase_					: Dict            =				[floats_list((1, x)			)[0] for x in range(8_00      ,  14_00      ,  2_00			)]
       UpperCamelCase_					: int            =				[np.asarray(__lowerCAmelCase			) for speech_input in speech_inputs]

       # Test not batched input
       UpperCamelCase_					: Any            =				feat_extract(speech_inputs[0]      ,  return_tensors="""np"""			).input_values
       UpperCamelCase_					: Union[str, Any]            =				feat_extract(np_speech_inputs[0]      ,  return_tensors="""np"""			).input_values
       self.assertTrue(np.allclose(__lowerCAmelCase      ,  __lowerCAmelCase      ,  atol=1E-3			)			)

       # Test batched
       UpperCamelCase_					: Union[str, Any]            =				feat_extract(__lowerCAmelCase      ,  padding=__lowerCAmelCase      ,  return_tensors="""np"""			).input_values
       UpperCamelCase_					: Union[str, Any]            =				feat_extract(__lowerCAmelCase      ,  padding=__lowerCAmelCase      ,  return_tensors="""np"""			).input_values
       for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase      ,  __lowerCAmelCase			):
             self.assertTrue(np.allclose(__lowerCAmelCase      ,  __lowerCAmelCase      ,  atol=1E-3			)			)

       # Test 2-D numpy arrays are batched.
       UpperCamelCase_					: Dict            =				[floats_list((1, x)			)[0] for x in (8_00, 8_00, 8_00)]
       UpperCamelCase_					: Optional[Any]            =				np.asarray(__lowerCAmelCase			)
       UpperCamelCase_					: Union[str, Any]            =				feat_extract(__lowerCAmelCase      ,  return_tensors="""np"""			).input_values
       UpperCamelCase_					: List[Any]            =				feat_extract(__lowerCAmelCase      ,  return_tensors="""np"""			).input_values
       for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase      ,  __lowerCAmelCase			):
             self.assertTrue(np.allclose(__lowerCAmelCase      ,  __lowerCAmelCase      ,  atol=1E-3			)			)
 @require_torch
 def 	_UpperCAmelCase							(				self			):
       import torch

       UpperCamelCase_					: int            =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
       UpperCamelCase_					: List[str]            =				np.random.rand(1_00			).astype(np.floataa			)
       UpperCamelCase_					: Optional[int]            =				np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
             UpperCamelCase_					: Any            =				feature_extractor.pad([{"""input_values""": inputs}]      ,  return_tensors="""np"""			)
             self.assertTrue(np_processed.input_values.dtype == np.floataa			)
             UpperCamelCase_					: Dict            =				feature_extractor.pad([{"""input_values""": inputs}]      ,  return_tensors="""pt"""			)
             self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       from datasets import load_dataset

       UpperCamelCase_					: Union[str, Any]            =				load_dataset("""hf-internal-testing/librispeech_asr_dummy"""      ,  """clean"""      ,  split="""validation"""			)
       # automatic decoding with librispeech
       UpperCamelCase_					: List[Any]            =				ds.sort("""id"""			).select(range(__lowerCAmelCase			)			)[:num_samples]["""audio"""]

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





 @require_torch
 def 	_UpperCAmelCase							(				self			):
       # fmt: off
       UpperCamelCase_					: str            =				torch.tensor(
           [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
            -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
            -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
            -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]			)
       # fmt: on

       UpperCamelCase_					: Optional[int]            =				self._load_datasamples(1			)
       UpperCamelCase_					: Tuple            =				ASTFeatureExtractor()
       UpperCamelCase_					: Any            =				feature_extractor(__lowerCAmelCase      ,  return_tensors="""pt"""			).input_values
       self.assertEquals(input_values.shape      ,  (1, 10_24, 1_28)			)
       self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,  __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)






