



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

lowerCamelCase		       =      random.Random()






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=1.0	,				__UpperCamelCase=None	,				__UpperCamelCase=None )	->						Union[str, Any]:
				if rng is None:
								a__	:							Any      						= global_rng

				a__	:							List[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 ,					__UpperCAmelCase ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=400 ,					__UpperCAmelCase=2000 ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=128 ,					__UpperCAmelCase=1 ,					__UpperCAmelCase=512 ,					__UpperCAmelCase=30 ,					__UpperCAmelCase=4_4100 ,					):







											"""simple docstring"""



											a__	:							Dict      						= parent
											a__	:							Union[str, Any]      						= batch_size
											a__	:							Any      						= min_seq_length
											a__	:							List[Any]      						= max_seq_length
											a__	:							List[Any]      						= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											a__	:							Any      						= spectrogram_length
											a__	:							Any      						= feature_size
											a__	:							List[str]      						= num_audio_channels
											a__	:							Dict      						= hop_length
											a__	:							Optional[Any]      						= chunk_length
											a__	:							Optional[Any]      						= sampling_rate



							def 		_A	(							self				):







											"""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 		_A	(							self ,					__UpperCAmelCase=False ,					__UpperCAmelCase=False				):







											"""simple docstring"""



											def _flatten(__UpperCAmelCase				):
															return list(itertools.chain(*SCREAMING_SNAKE_CASE_				)				)

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





@require_torch
@require_torchaudio
class 				_a						(       _UpperCAmelCase       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Dict =       TvltFeatureExtractor



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= TvltFeatureExtractionTester(self				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self.feature_extraction_class(**self.feat_extract_dict				)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,					"spectrogram_length"				)				)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,					"feature_size"				)				)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,					"num_audio_channels"				)				)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,					"hop_length"				)				)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,					"chunk_length"				)				)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,					"sampling_rate"				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= self.feature_extraction_class(**self.feat_extract_dict				)

											with tempfile.TemporaryDirectory() as tmpdirname:
															a__	:							List[str]      						= feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE_				)[0]
															check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_				)
															a__	:							List[Any]      						= self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE_				)

											a__	:							Tuple      						= feat_extract_first.to_dict()
											a__	:							Optional[int]      						= feat_extract_second.to_dict()
											a__	:							Union[str, Any]      						= dict_first.pop("mel_filters"				)
											a__	:							List[Any]      						= dict_second.pop("mel_filters"				)
											self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_				)				)
											self.assertEqual(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= self.feature_extraction_class(**self.feat_extract_dict				)

											with tempfile.TemporaryDirectory() as tmpdirname:
															a__	:							int      						= os.path.join(SCREAMING_SNAKE_CASE_ ,					"feat_extract.json"				)
															feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE_				)
															a__	:							Any      						= self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE_				)

											a__	:							Dict      						= feat_extract_first.to_dict()
											a__	:							Optional[Any]      						= feat_extract_second.to_dict()
											a__	:							Union[str, Any]      						= dict_first.pop("mel_filters"				)
											a__	:							str      						= dict_second.pop("mel_filters"				)
											self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_				)				)
											self.assertEqual(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.feature_extraction_class(**self.feat_extract_dict				)

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

											# Test not batched input
											a__	:							Union[str, Any]      						= feature_extractor(np_speech_inputs[0] ,					return_tensors="np" ,					sampling_rate=4_4100				).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
											a__	:							Any      						= feature_extractor(SCREAMING_SNAKE_CASE_ ,					return_tensors="np" ,					sampling_rate=4_4100				).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
											a__	:							Dict      						= feature_extractor(
											    SCREAMING_SNAKE_CASE_ ,					return_tensors="np" ,					sampling_rate=4_4100 ,					mask_audio=SCREAMING_SNAKE_CASE_				).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.
											a__	:							Optional[Any]      						= [floats_list((1, x)				)[0] for x in (800, 800, 800)]
											a__	:							int      						= np.asarray(SCREAMING_SNAKE_CASE_				)
											a__	:							Dict      						= feature_extractor(SCREAMING_SNAKE_CASE_ ,					return_tensors="np" ,					sampling_rate=4_4100				).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 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



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

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




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self._load_datasamples(1				)
											a__	:							List[Any]      						= TvltFeatureExtractor()
											a__	:							List[str]      						= feature_extractor(SCREAMING_SNAKE_CASE_ ,					return_tensors="pt"				).audio_values

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

											a__	:							str      						= 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] ,					SCREAMING_SNAKE_CASE_ ,					atol=1E-4				)				)






'''simple docstring'''



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 1_00				)	->						int:
     __lowerCamelCase  :      Union[str, Any]														=						n * (n + 1) * (2 * n + 1) / 6
     __lowerCamelCase  :      Union[str, Any]														=						(n * (n + 1) / 2) ** 2
     return int(square_of_sum - sum_of_squares				)


if __name__ == "__main__":
      print(f'''{solution() = }''')






