


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

A      = random.Random()





def __UpperCAmelCase       (		__A ,						__A=1.0 ,						__A=None ,						__A=None			)						->					str:



       '''simple docstring'''





       if rng is None:
              UpperCAmelCase__										=     global_rng

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

       return values


class     lowercase__  (					unittest.TestCase		):






  def __init__(					self      :					str		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Tuple=7		,						_lowercase      :					str=4_00		,						_lowercase      :					Tuple=20_00		,						_lowercase      :					List[Any]=20_48		,						_lowercase      :					int=1_28		,						_lowercase      :					int=1		,						_lowercase      :					List[Any]=5_12		,						_lowercase      :					Optional[Any]=30		,						_lowercase      :					Tuple=4_41_00		,						):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     batch_size
         UpperCAmelCase__										=     min_seq_length
         UpperCAmelCase__										=     max_seq_length
         UpperCAmelCase__										=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         UpperCAmelCase__										=     spectrogram_length
         UpperCAmelCase__										=     feature_size
         UpperCAmelCase__										=     num_audio_channels
         UpperCAmelCase__										=     hop_length
         UpperCAmelCase__										=     chunk_length
         UpperCAmelCase__										=     sampling_rate






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         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      :					Union[str, Any]		,						_lowercase      :					Tuple=False		,						_lowercase      :					Tuple=False ):




         """simple docstring"""







         def _flatten(_lowercase      :					str ):
                return list(itertools.chain(*_lowercase ) )

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




@require_torch
@require_torchaudio
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  TvltFeatureExtractor






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     TvltFeatureExtractionTester(self )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.feature_extraction_class(**self.feat_extract_dict )
         self.assertTrue(hasattr(_lowercase		,						"spectrogram_length" ) )
         self.assertTrue(hasattr(_lowercase		,						"feature_size" ) )
         self.assertTrue(hasattr(_lowercase		,						"num_audio_channels" ) )
         self.assertTrue(hasattr(_lowercase		,						"hop_length" ) )
         self.assertTrue(hasattr(_lowercase		,						"chunk_length" ) )
         self.assertTrue(hasattr(_lowercase		,						"sampling_rate" ) )






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.feature_extraction_class(**self.feat_extract_dict )

         with tempfile.TemporaryDirectory() as tmpdirname:
                UpperCAmelCase__										=     feat_extract_first.save_pretrained(_lowercase )[0]
                check_json_file_has_correct_format(_lowercase )
                UpperCAmelCase__										=     self.feature_extraction_class.from_pretrained(_lowercase )

         UpperCAmelCase__										=     feat_extract_first.to_dict()
         UpperCAmelCase__										=     feat_extract_second.to_dict()
         UpperCAmelCase__										=     dict_first.pop("mel_filters" )
         UpperCAmelCase__										=     dict_second.pop("mel_filters" )
         self.assertTrue(np.allclose(_lowercase		,						_lowercase ) )
         self.assertEqual(_lowercase		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.feature_extraction_class(**self.feat_extract_dict )

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

         UpperCAmelCase__										=     feat_extract_first.to_dict()
         UpperCAmelCase__										=     feat_extract_second.to_dict()
         UpperCAmelCase__										=     dict_first.pop("mel_filters" )
         UpperCAmelCase__										=     dict_second.pop("mel_filters" )
         self.assertTrue(np.allclose(_lowercase		,						_lowercase ) )
         self.assertEqual(_lowercase		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.feature_extraction_class(**self.feat_extract_dict )

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

         # Test not batched input
         UpperCAmelCase__										=     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__										=     feature_extractor(_lowercase		,						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__										=     feature_extractor(
             _lowercase		,						return_tensors="np"		,						sampling_rate=4_41_00		,						mask_audio=_lowercase ).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__										=     [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
         UpperCAmelCase__										=     np.asarray(_lowercase )
         UpperCAmelCase__										=     feature_extractor(_lowercase		,						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      :					Optional[Any]		,						_lowercase      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     load_dataset("hf-internal-testing/librispeech_asr_dummy"		,						"clean"		,						split="validation" )
         # automatic decoding with librispeech
         UpperCAmelCase__										=     ds.sort("id" ).select(range(_lowercase ) )[:num_samples]["audio"]

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






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self._load_datasamples(1 )
         UpperCAmelCase__										=     TvltFeatureExtractor()
         UpperCAmelCase__										=     feature_extractor(_lowercase		,						return_tensors="pt" ).audio_values

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

         UpperCAmelCase__										=     torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]] )
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]		,						_lowercase		,						atol=1E-4 ) )









from __future__ import annotations


class     lowercase__  :






  def __init__(					self      :					int		,						_lowercase      :					list[list[int]] ):




         """simple docstring"""







         UpperCAmelCase__										=     TypeError(
             "Matrices must be formed from a list of zero or more lists containing at "
             "least one and the same number of values, each of which must be of type "
             "int or float." )
         if len(_lowercase ) != 0:
                UpperCAmelCase__										=     len(rows[0] )
                if cols == 0:
                       raise error
                for row in rows:
                       if len(_lowercase ) != cols:
                              raise error
                       for value in row:
                              if not isinstance(_lowercase		,						(int, float) ):
                                     raise error
                UpperCAmelCase__										=     rows
         else:
                UpperCAmelCase__										=     []






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]






  @property
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         return len(self.rows )






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         return len(self.rows[0] )






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         return (self.num_rows, self.num_columns)






  @property
  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         return self.order[0] == self.order[1]






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     [
             [0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
             for row_num in range(self.num_rows )
         ]
         return Matrix(_lowercase )






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         if not self.is_square:
                return 0
         if self.order == (0, 0):
                return 1
         if self.order == (1, 1):
                return int(self.rows[0][0] )
         if self.order == (2, 2):
                return int(
                    (self.rows[0][0] * self.rows[1][1])
                    - (self.rows[0][1] * self.rows[1][0]) )
         else:
                return sum(
                    self.rows[0][column] * self.cofactors().rows[0][column]
                    for column in range(self.num_columns ) )






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         return bool(self.determinant() )






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					int		,						_lowercase      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     [
             [
                 self.rows[other_row][other_column]
                 for other_column in range(self.num_columns )
                 if other_column != column
             ]
             for other_row in range(self.num_rows )
             if other_row != row
         ]
         return Matrix(_lowercase ).determinant()






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					int		,						_lowercase      :					int ):




         """simple docstring"""







         if (row + column) % 2 == 0:
                return self.get_minor(_lowercase		,						_lowercase )
         return -1 * self.get_minor(_lowercase		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         return Matrix(
             [
                 [self.get_minor(_lowercase		,						_lowercase ) for column in range(self.num_columns )]
                 for row in range(self.num_rows )
             ] )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         return Matrix(
             [
                 [
                     self.minors().rows[row][column]
                     if (row + column) % 2 == 0
                     else self.minors().rows[row][column] * -1
                     for column in range(self.minors().num_columns )
                 ]
                 for row in range(self.minors().num_rows )
             ] )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     [
             [self.cofactors().rows[column][row] for column in range(self.num_columns )]
             for row in range(self.num_rows )
         ]
         return Matrix(_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.determinant()
         if not determinant:
                raise TypeError("Only matrices with a non-zero determinant have an inverse" )
         return self.adjugate() * (1 / determinant)






  def __repr__(					self      :					Dict ):




         """simple docstring"""







         return str(self.rows )






  def __str__(					self      :					Tuple ):




         """simple docstring"""







         if self.num_rows == 0:
                return "[]"
         if self.num_rows == 1:
                return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
         return (
             "["
             + "\n ".join(
                 [
                     "[" + ". ".join([str(_lowercase ) for value in row] ) + ".]"
                     for row in self.rows
                 ] )
             + "]"
         )






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					list[int]		,						_lowercase      :					int | None = None ):




         """simple docstring"""







         UpperCAmelCase__										=     TypeError("Row must be a list containing all ints and/or floats" )
         if not isinstance(_lowercase		,						_lowercase ):
                raise type_error
         for value in row:
                if not isinstance(_lowercase		,						(int, float) ):
                       raise type_error
         if len(_lowercase ) != self.num_columns:
                raise ValueError(
                    "Row must be equal in length to the other rows in the matrix" )
         if position is None:
                self.rows.append(_lowercase )
         else:
                UpperCAmelCase__										=     self.rows[0:position] + [row] + self.rows[position:]






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					list[int]		,						_lowercase      :					int | None = None ):




         """simple docstring"""







         UpperCAmelCase__										=     TypeError(
             "Column must be a list containing all ints and/or floats" )
         if not isinstance(_lowercase		,						_lowercase ):
                raise type_error
         for value in column:
                if not isinstance(_lowercase		,						(int, float) ):
                       raise type_error
         if len(_lowercase ) != self.num_rows:
                raise ValueError(
                    "Column must be equal in length to the other columns in the matrix" )
         if position is None:
                UpperCAmelCase__										=     [self.rows[i] + [column[i]] for i in range(self.num_rows )]
         else:
                UpperCAmelCase__										=     [
                    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                    for i in range(self.num_rows )
                ]






  def __eq__(					self      :					Dict		,						_lowercase      :					object ):




         """simple docstring"""







         if not isinstance(_lowercase		,						_lowercase ):
                return NotImplemented
         return self.rows == other.rows






  def __ne__(					self      :					List[Any]		,						_lowercase      :					object ):




         """simple docstring"""







         return not self == other






  def __neg__(					self      :					int ):




         """simple docstring"""







         return self * -1






  def __add__(					self      :					List[Any]		,						_lowercase      :					Matrix ):




         """simple docstring"""







         if self.order != other.order:
                raise ValueError("Addition requires matrices of the same order" )
         return Matrix(
             [
                 [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
                 for i in range(self.num_rows )
             ] )






  def __sub__(					self      :					Optional[int]		,						_lowercase      :					Matrix ):




         """simple docstring"""







         if self.order != other.order:
                raise ValueError("Subtraction requires matrices of the same order" )
         return Matrix(
             [
                 [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
                 for i in range(self.num_rows )
             ] )






  def __mul__(					self      :					int		,						_lowercase      :					Matrix | int | float ):




         """simple docstring"""







         if isinstance(_lowercase		,						(int, float) ):
                return Matrix(
                    [[int(element * other ) for element in row] for row in self.rows] )
         elif isinstance(_lowercase		,						_lowercase ):
                if self.num_columns != other.num_rows:
                       raise ValueError(
                           "The number of columns in the first matrix must "
                           "be equal to the number of rows in the second" )
                return Matrix(
                    [
                        [Matrix.dot_product(_lowercase		,						_lowercase ) for column in other.columns()]
                        for row in self.rows
                    ] )
         else:
                raise TypeError(
                    "A Matrix can only be multiplied by an int, float, or another matrix" )






  def __pow__(					self      :					Optional[Any]		,						_lowercase      :					int ):




         """simple docstring"""







         if not isinstance(_lowercase		,						_lowercase ):
                raise TypeError("A Matrix can only be raised to the power of an int" )
         if not self.is_square:
                raise ValueError("Only square matrices can be raised to a power" )
         if other == 0:
                return self.identity()
         if other < 0:
                if self.is_invertable():
                       return self.inverse() ** (-other)
                raise ValueError(
                    "Only invertable matrices can be raised to a negative power" )
         UpperCAmelCase__										=     self
         for _ in range(other - 1 ):
                result *= self
         return result






  @classmethod
  def 			_UpperCAmelCase  (					cls      :					List[Any]		,						_lowercase      :					list[int]		,						_lowercase      :					list[int] ):




         """simple docstring"""







         return sum(row[i] * column[i] for i in range(len(_lowercase ) ) )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





