




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						:							int						      =  random.Random()
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=1.0					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=None   ):
			'''simple docstring'''
			if rng is None:
						SCREAMING_SNAKE_CASE           =					global_rng

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

			return values




class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



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





							def    _UpperCAmelCase     (					self       :	str			)			->					Optional[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       :	int       ,     a       :	Tuple=False       ,     a       :	int=False			)			->					Optional[Any]:
										def _flatten(a       :	Union[str, Any]			):
													return list(itertools.chain(*__A			)			)

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





@require_torch
@require_torchaudio
class 			UpperCAmelCase_      (   UpperCamelCase_		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     TvltFeatureExtractor





							def    _UpperCAmelCase     (					self       :	Any			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					TvltFeatureExtractionTester(self			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					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       :	Dict			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**self.feat_extract_dict			)

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

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





							def    _UpperCAmelCase     (					self       :	int			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**self.feat_extract_dict			)

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

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





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					List[Any]:
										# Initialize feature_extractor
										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**self.feat_extract_dict			)

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

										# Test not batched input
										SCREAMING_SNAKE_CASE           =					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
										SCREAMING_SNAKE_CASE           =					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
										SCREAMING_SNAKE_CASE           =					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.
										SCREAMING_SNAKE_CASE           =					[floats_list((1, x)			)[0] for x in (800, 800, 800)]
										SCREAMING_SNAKE_CASE           =					np.asarray(__A			)
										SCREAMING_SNAKE_CASE           =					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       :	Optional[int]       ,     a       :	Dict			)			->					Any:
										SCREAMING_SNAKE_CASE           =					load_dataset("""hf-internal-testing/librispeech_asr_dummy"""       ,     """clean"""       ,     split="""validation"""			)
										# automatic decoding with librispeech
										SCREAMING_SNAKE_CASE           =					ds.sort("""id"""			).select(range(__A			)			)[:num_samples]["audio"]

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







							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self._load_datasamples(1			)
										SCREAMING_SNAKE_CASE           =					TvltFeatureExtractor()
										SCREAMING_SNAKE_CASE           =					feature_extractor(__A       ,     return_tensors="""pt"""			).audio_values

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

										SCREAMING_SNAKE_CASE           =					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

import typing
from collections.abc import Iterable

import numpy as np

__A						:							Dict							=  typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__A						:							Tuple							=  typing.Union[np.floataa, int, float]  # noqa: UP007
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return np.sqrt(np.sum((np.asarray(SCREAMING_SNAKE_CASE   ) - np.asarray(SCREAMING_SNAKE_CASE   )) ** 2   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return sum((va - va) ** 2 for va, va in zip(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )   ) ** (1 / 2)


if __name__ == "__main__":
		def 					lowerCamelCase_    (    ):
					'''simple docstring'''
					from timeit import timeit

					print("""Without Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )
					print("""With Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )




		benchmark()






