





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__			(__lowerCamelCase,       __lowerCamelCase=1.0,       __lowerCamelCase=None,       __lowerCamelCase=None	):
     if rng is None:
          _SCREAMING_SNAKE_CASE       :	str						    =							global_rng

     _SCREAMING_SNAKE_CASE       :	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 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=7    ,						__lowerCamelCase=4_0_0    ,						__lowerCamelCase=2_0_0_0    ,						__lowerCamelCase=2_0_4_8    ,						__lowerCamelCase=1_2_8    ,						__lowerCamelCase=1    ,						__lowerCamelCase=5_1_2    ,						__lowerCamelCase=3_0    ,						__lowerCamelCase=4_4_1_0_0    ,						)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Any						    =							parent
            _SCREAMING_SNAKE_CASE       :	str						    =							batch_size
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							min_seq_length
            _SCREAMING_SNAKE_CASE       :	int						    =							max_seq_length
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							spectrogram_length
            _SCREAMING_SNAKE_CASE       :	int						    =							feature_size
            _SCREAMING_SNAKE_CASE       :	str						    =							num_audio_channels
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							hop_length
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							chunk_length
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							sampling_rate




       def     UpperCamelCase_			(      self							)			->     Tuple:
            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    ,						__lowerCamelCase=False    ,						__lowerCamelCase=False							)			->     Optional[int]:
            def _flatten(__lowerCamelCase							):
                 return list(itertools.chain(*_a							)							)

            if equal_length:
                 _SCREAMING_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
                 _SCREAMING_SNAKE_CASE       :	Union[str, 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:
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[np.asarray(_a							) for x in speech_inputs]
            return speech_inputs





@require_torch
@require_torchaudio
class 			lowerCAmelCase__( UpperCamelCase__  ,     unittest.TestCase    ):






       '''simple docstring'''


       __snake_case 			=    TvltFeatureExtractor




       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	int						    =							TvltFeatureExtractionTester(self							)




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            _SCREAMING_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							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.feature_extraction_class(**self.feat_extract_dict							)

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

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							feat_extract_first.to_dict()
            _SCREAMING_SNAKE_CASE       :	int						    =							feat_extract_second.to_dict()
            _SCREAMING_SNAKE_CASE       :	int						    =							dict_first.pop("mel_filters"							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							dict_second.pop("mel_filters"							)
            self.assertTrue(np.allclose(_a    ,						_a							)							)
            self.assertEqual(_a    ,						_a							)




       def     UpperCamelCase_			(      self							)			->     int:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.feature_extraction_class(**self.feat_extract_dict							)

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

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




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            # Initialize feature_extractor
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.feature_extraction_class(**self.feat_extract_dict							)

            # create three inputs of length 800, 1000, and 1200
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							[floats_list((1, x)							)[0] for x in range(8_0_0    ,						1_4_0_0    ,						2_0_0							)]
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							[np.asarray(_a							) for speech_input in speech_inputs]

            # Test not batched input
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							feature_extractor(np_speech_inputs[0]    ,						return_tensors="np"    ,						sampling_rate=4_4_1_0_0							).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       :	str						    =							feature_extractor(_a    ,						return_tensors="np"    ,						sampling_rate=4_4_1_0_0							).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       :	Optional[Any]						    =							feature_extractor(
                _a    ,						return_tensors="np"    ,						sampling_rate=4_4_1_0_0    ,						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       :	Dict						    =							[floats_list((1, x)							)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							np.asarray(_a							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							feature_extractor(_a    ,						return_tensors="np"    ,						sampling_rate=4_4_1_0_0							).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    ,						__lowerCamelCase							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							load_dataset("hf-internal-testing/librispeech_asr_dummy"    ,						"clean"    ,						split="validation"							)
            # automatic decoding with librispeech
            _SCREAMING_SNAKE_CASE       :	Dict						    =							ds.sort("id"							).select(range(_a							)							)[:num_samples]["""audio"""]

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







       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self._load_datasamples(1							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							TvltFeatureExtractor()
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							feature_extractor(_a    ,						return_tensors="pt"							).audio_values

            self.assertEquals(audio_values.shape    ,						(1, 1, 1_9_2, 1_2_8)							)

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









def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     if a < 0 or b < 0:
          raise ValueError("the value of both inputs must be positive"	)
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							str(bin(__lowerCamelCase	)	)[2:]  # remove the leading "0b"
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							str(bin(__lowerCamelCase	)	)[2:]
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							max(len(__lowerCamelCase	),       len(__lowerCamelCase	)	)
     return "0b" + "".join(
         str(int("1" in (char_a, char_b)	)	)
         for char_a, char_b in zip(a_binary.zfill(__lowerCamelCase	),       b_binary.zfill(__lowerCamelCase	)	)	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()