





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	:       Any    	=							random.Random()







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None)       ->   Any:
		if rng is None:
				__snake_case:						Dict			  =						global_rng

		__snake_case:						str			  =						[]
		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      ):





							'''simple docstring'''







							def __init__(  self			:   int							,    A			:   List[str]							,    A			:   List[Any]=7							,    A			:   Optional[int]=400							,    A			:   List[Any]=2_000							,    A			:   Dict=2_048							,    A			:   Tuple=128							,    A			:   List[Any]=1							,    A			:   Tuple=512							,    A			:   str=30							,    A			:   Optional[Any]=44_100							,    ):
									__snake_case:						Dict			  =						parent
									__snake_case:						Optional[Any]			  =						batch_size
									__snake_case:						Optional[int]			  =						min_seq_length
									__snake_case:						Optional[Any]			  =						max_seq_length
									__snake_case:						List[str]			  =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									__snake_case:						Any			  =						spectrogram_length
									__snake_case:						Any			  =						feature_size
									__snake_case:						Union[str, Any]			  =						num_audio_channels
									__snake_case:						Any			  =						hop_length
									__snake_case:						List[str]			  =						chunk_length
									__snake_case:						Any			  =						sampling_rate




							def 							UpperCAmelCase__			(  self			:   List[Any]		):
									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			:   List[str]							,    A			:   str=False							,    A			:   int=False		):
									def _flatten(A			:   Dict		):
											return list(itertools.chain(*A		)		)

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







@require_torch
@require_torchaudio
class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





							'''simple docstring'''







							lowerCAmelCase__						=					TvltFeatureExtractor




							def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
									__snake_case:						str			  =						TvltFeatureExtractionTester(self		)




							def 							UpperCAmelCase__			(  self			:   int		):
									__snake_case:						Tuple			  =						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 							UpperCAmelCase__			(  self			:   Any		):
									__snake_case:						Optional[Any]			  =						self.feature_extraction_class(**self.feat_extract_dict		)

									with tempfile.TemporaryDirectory() as tmpdirname:
											__snake_case:						Tuple			  =						feat_extract_first.save_pretrained(A		)[0]
											check_json_file_has_correct_format(A		)
											__snake_case:						int			  =						self.feature_extraction_class.from_pretrained(A		)

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




							def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
									__snake_case:						str			  =						self.feature_extraction_class(**self.feat_extract_dict		)

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

									__snake_case:						Dict			  =						feat_extract_first.to_dict()
									__snake_case:						Any			  =						feat_extract_second.to_dict()
									__snake_case:						int			  =						dict_first.pop("""mel_filters"""		)
									__snake_case:						int			  =						dict_second.pop("""mel_filters"""		)
									self.assertTrue(np.allclose(A							,    A		)		)
									self.assertEqual(A							,    A		)




							def 							UpperCAmelCase__			(  self			:   Any		):
									# Initialize feature_extractor
									__snake_case:						Union[str, Any]			  =						self.feature_extraction_class(**self.feat_extract_dict		)

									# create three inputs of length 800, 1000, and 1200
									__snake_case:						Dict			  =						[floats_list((1, x)		)[0] for x in range(800							,    1_400							,    200		)]
									__snake_case:						str			  =						[np.asarray(A		) for speech_input in speech_inputs]

									# Test not batched input
									__snake_case:						int			  =						feature_extractor(np_speech_inputs[0]							,    return_tensors="""np"""							,    sampling_rate=44_100		).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
									__snake_case:						Optional[int]			  =						feature_extractor(A							,    return_tensors="""np"""							,    sampling_rate=44_100		).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
									__snake_case:						Union[str, Any]			  =						feature_extractor(
									    A							,    return_tensors="""np"""							,    sampling_rate=44_100							,    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.
									__snake_case:						Any			  =						[floats_list((1, x)		)[0] for x in (800, 800, 800)]
									__snake_case:						Union[str, Any]			  =						np.asarray(A		)
									__snake_case:						List[Any]			  =						feature_extractor(A							,    return_tensors="""np"""							,    sampling_rate=44_100		).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			:   Union[str, Any]							,    A			:   List[str]		):
									__snake_case:						Tuple			  =						load_dataset("""hf-internal-testing/librispeech_asr_dummy"""							,    """clean"""							,    split="""validation"""		)
									# automatic decoding with librispeech
									__snake_case:						List[Any]			  =						ds.sort("""id"""		).select(range(A		)		)[:num_samples]["""audio"""]

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






							def 							UpperCAmelCase__			(  self			:   Optional[int]		):
									__snake_case:						Dict			  =						self._load_datasamples(1		)
									__snake_case:						Optional[int]			  =						TvltFeatureExtractor()
									__snake_case:						Optional[Any]			  =						feature_extractor(A							,    return_tensors="""pt"""		).audio_values

									self.assertEquals(audio_values.shape							,    (1, 1, 192, 128)		)

									__snake_case:						str			  =						torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
									self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]							,    A							,    atol=1E-4		)		)













from __future__ import annotations

from decimal import Decimal

from numpy import array







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   list[list[float]]:
  __snake_case:						Any			  =						Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(SCREAMING_SNAKE_CASE__) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
    # Calculate the determinant of the matrix
    __snake_case:						Tuple			  =						float(
        d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creates a copy of the matrix with swapped positions of the elements
    __snake_case:						Optional[int]			  =						[[0.0, 0.0], [0.0, 0.0]]
    __snake_case  ,       __snake_case:						Optional[Any]			  =						matrix[1][1], matrix[0][0]
    __snake_case  ,       __snake_case:						Union[str, Any]			  =						-matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(SCREAMING_SNAKE_CASE__)) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(SCREAMING_SNAKE_CASE__) == 3
      and len(matrix[0]) == 3
      and len(matrix[1]) == 3
      and len(matrix[2]) == 3
  ):
    # Calculate the determinant of the matrix using Sarrus rule
    __snake_case:						Any			  =						float(
        (
            (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
            + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
            + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
        )
        - (
            (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
            + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
            + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
        ))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creating cofactor matrix
    __snake_case:						Tuple			  =						[
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
    ]
    __snake_case:						Dict			  =						(d(matrix[1][1]) * d(matrix[2][2])) - (
        d(matrix[1][2]) * d(matrix[2][1])
    )
    __snake_case:						Tuple			  =						-(
        (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[1][0]) * d(matrix[2][1])) - (
        d(matrix[1][1]) * d(matrix[2][0])
    )
    __snake_case:						Union[str, Any]			  =						-(
        (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
    )
    __snake_case:						str			  =						(d(matrix[0][0]) * d(matrix[2][2])) - (
        d(matrix[0][2]) * d(matrix[2][0])
    )
    __snake_case:						List[Any]			  =						-(
        (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[Any]			  =						(d(matrix[0][1]) * d(matrix[1][2])) - (
        d(matrix[0][2]) * d(matrix[1][1])
    )
    __snake_case:						List[str]			  =						-(
        (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[0][0]) * d(matrix[1][1])) - (
        d(matrix[0][1]) * d(matrix[1][0])
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        __snake_case:						Tuple			  =						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        inverse_matrix[i][j] /= d(SCREAMING_SNAKE_CASE__)

        # Calculate the inverse of the matrix
    return [[float(d(SCREAMING_SNAKE_CASE__)) or 0.0 for n in row] for row in inverse_matrix]
  raise ValueError("""Please provide a matrix of size 2x2 or 3x3.""")






