

'''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 				_lowerCAmelCase			(   __magic_name__						:     Any   , __magic_name__						:     Union[str, Any]=1.0   , __magic_name__						:     Union[str, Any]=None   , __magic_name__						:     Tuple=None						)				->  Union[str, Any]:
						if rng is None:
												lowercase :							List[Any]    		=global_rng

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

						return values





class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Union[str, Any]=7		,					UpperCAmelCase__     :		Optional[Any]=400		,					UpperCAmelCase__     :		str=2000		,					UpperCAmelCase__     :		Dict=2048		,					UpperCAmelCase__     :		Union[str, Any]=128		,					UpperCAmelCase__     :		List[str]=1		,					UpperCAmelCase__     :		List[str]=512		,					UpperCAmelCase__     :		int=30		,					UpperCAmelCase__     :		Dict=44100		,					):
										'''simple docstring'''
										lowercase :							List[str]    		=parent
										lowercase :							Dict    		=batch_size
										lowercase :							List[Any]    		=min_seq_length
										lowercase :							List[Any]    		=max_seq_length
										lowercase :							List[str]    		=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										lowercase :							Dict    		=spectrogram_length
										lowercase :							str    		=feature_size
										lowercase :							List[Any]    		=num_audio_channels
										lowercase :							Optional[int]    		=hop_length
										lowercase :							Optional[int]    		=chunk_length
										lowercase :							Tuple    		=sampling_rate







				def  lowerCamelCase_	(       self     :		str   ):
										'''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  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		Union[str, Any]=False		,					UpperCAmelCase__     :		Union[str, Any]=False   ):
										'''simple docstring'''
										def _flatten(UpperCAmelCase__     :		Optional[Any]   ):
																return list(itertools.chain(*UpperCAmelCase__   )   )

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





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







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=TvltFeatureExtractionTester(self   )







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							str    		=self.feature_extraction_class(**self.feat_extract_dict   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''spectrogram_length'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''feature_size'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''num_audio_channels'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''hop_length'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''chunk_length'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''sampling_rate'''   )   )







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.feature_extraction_class(**self.feat_extract_dict   )

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

										lowercase :							Any    		=feat_extract_first.to_dict()
										lowercase :							Union[str, Any]    		=feat_extract_second.to_dict()
										lowercase :							Tuple    		=dict_first.pop('''mel_filters'''   )
										lowercase :							str    		=dict_second.pop('''mel_filters'''   )
										self.assertTrue(np.allclose(UpperCAmelCase__		,					UpperCAmelCase__   )   )
										self.assertEqual(UpperCAmelCase__		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=self.feature_extraction_class(**self.feat_extract_dict   )

										with tempfile.TemporaryDirectory() as tmpdirname:
																lowercase :							str    		=os.path.join(UpperCAmelCase__		,					'''feat_extract.json'''   )
																feat_extract_first.to_json_file(UpperCAmelCase__   )
																lowercase :							Union[str, Any]    		=self.feature_extraction_class.from_json_file(UpperCAmelCase__   )

										lowercase :							List[Any]    		=feat_extract_first.to_dict()
										lowercase :							int    		=feat_extract_second.to_dict()
										lowercase :							Optional[int]    		=dict_first.pop('''mel_filters'''   )
										lowercase :							Tuple    		=dict_second.pop('''mel_filters'''   )
										self.assertTrue(np.allclose(UpperCAmelCase__		,					UpperCAmelCase__   )   )
										self.assertEqual(UpperCAmelCase__		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										# Initialize feature_extractor
										lowercase :							List[Any]    		=self.feature_extraction_class(**self.feat_extract_dict   )

										# create three inputs of length 800, 1000, and 1200
										lowercase :							Any    		=[floats_list((1, x)   )[0] for x in range(800		,					1400		,					200   )]
										lowercase :							Any    		=[np.asarray(UpperCAmelCase__   ) for speech_input in speech_inputs]

										# Test not batched input
										lowercase :							Dict    		=feature_extractor(np_speech_inputs[0]		,					return_tensors='''np'''		,					sampling_rate=44100   ).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
										lowercase :							str    		=feature_extractor(UpperCAmelCase__		,					return_tensors='''np'''		,					sampling_rate=44100   ).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
										lowercase :							Union[str, Any]    		=feature_extractor(
										    UpperCAmelCase__		,					return_tensors='''np'''		,					sampling_rate=44100		,					mask_audio=UpperCAmelCase__   ).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.
										lowercase :							Optional[int]    		=[floats_list((1, x)   )[0] for x in (800, 800, 800)]
										lowercase :							Dict    		=np.asarray(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=feature_extractor(UpperCAmelCase__		,					return_tensors='''np'''		,					sampling_rate=44100   ).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  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Dict    		=load_dataset('''hf-internal-testing/librispeech_asr_dummy'''		,					'''clean'''		,					split='''validation'''   )
										# automatic decoding with librispeech
										lowercase :							Any    		=ds.sort('''id'''   ).select(range(UpperCAmelCase__   )   )[:num_samples]['''audio''']

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







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							str    		=self._load_datasamples(1   )
										lowercase :							List[str]    		=TvltFeatureExtractor()
										lowercase :							Any    		=feature_extractor(UpperCAmelCase__		,					return_tensors='''pt'''   ).audio_values

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

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









'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			list:






       """simple docstring"""







       lowercase__      =					len(A			)
       lowercase__      =					[[0] * n for i in range(A			)]
       for i in range(A			):
              lowercase__      =					y_points[i]

       for i in range(2    ,					A			):
              for j in range(A    ,					A			):
                     lowercase__      =					(
                         (xa - x_points[j - i + 1]) * q[j][i - 1]
                         - (xa - x_points[j]) * q[j - 1][i - 1]
                     ) / (x_points[j] - x_points[j - i + 1])

       return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




