





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							:			str         =			random.Random()





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=1.0							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE=None				)							-> Union[str, Any]:




    """simple docstring"""




    if rng is None:
        _A     =							global_rng

    _A     =							[]
    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				,     snake_case_				,     snake_case_=7				,     snake_case_=400				,     snake_case_=2000				,     snake_case_=2048				,     snake_case_=128				,     snake_case_=1				,     snake_case_=512				,     snake_case_=30				,     snake_case_=4_4100				,     ):
         _A     =							parent
         _A     =							batch_size
         _A     =							min_seq_length
         _A     =							max_seq_length
         _A     =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         _A     =							spectrogram_length
         _A     =							feature_size
         _A     =							num_audio_channels
         _A     =							hop_length
         _A     =							chunk_length
         _A     =							sampling_rate




     def        lowerCAmelCase__		(    self							):
         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				,     snake_case_=False				,     snake_case_=False							):
         def _flatten(snake_case_							):
             return list(itertools.chain(*snake_case_							)							)

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







@require_torch
@require_torchaudio
class      lowerCamelCase(						__snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			TvltFeatureExtractor




     def        lowerCAmelCase__		(    self							):
         _A     =							TvltFeatureExtractionTester(self							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)
         self.assertTrue(hasattr(snake_case_				,     'spectrogram_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'feature_size'							)							)
         self.assertTrue(hasattr(snake_case_				,     'num_audio_channels'							)							)
         self.assertTrue(hasattr(snake_case_				,     'hop_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'chunk_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'sampling_rate'							)							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

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

         _A     =							feat_extract_first.to_dict()
         _A     =							feat_extract_second.to_dict()
         _A     =							dict_first.pop('mel_filters'							)
         _A     =							dict_second.pop('mel_filters'							)
         self.assertTrue(np.allclose(snake_case_				,     snake_case_							)							)
         self.assertEqual(snake_case_				,     snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
             _A     =							os.path.join(snake_case_				,     'feat_extract.json'							)
             feat_extract_first.to_json_file(snake_case_							)
             _A     =							self.feature_extraction_class.from_json_file(snake_case_							)

         _A     =							feat_extract_first.to_dict()
         _A     =							feat_extract_second.to_dict()
         _A     =							dict_first.pop('mel_filters'							)
         _A     =							dict_second.pop('mel_filters'							)
         self.assertTrue(np.allclose(snake_case_				,     snake_case_							)							)
         self.assertEqual(snake_case_				,     snake_case_							)




     def        lowerCAmelCase__		(    self							):
         # Initialize feature_extractor
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

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

         # Test not batched input
         _A     =							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     =							feature_extractor(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     =							feature_extractor(
             snake_case_				,     return_tensors='np'				,     sampling_rate=4_4100				,     mask_audio=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     =							[floats_list((1, x)							)[0] for x in (800, 800, 800)]
         _A     =							np.asarray(snake_case_							)
         _A     =							feature_extractor(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        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							load_dataset('hf-internal-testing/librispeech_asr_dummy'				,     'clean'				,     split='validation'							)
         # automatic decoding with librispeech
         _A     =							ds.sort('id'							).select(range(snake_case_							)							)[:num_samples]['audio']

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




     def        lowerCAmelCase__		(    self							):
         _A     =							self._load_datasamples(1							)
         _A     =							TvltFeatureExtractor()
         _A     =							feature_extractor(snake_case_				,     return_tensors='pt'							).audio_values

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

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












from ... import PretrainedConfig


__A							:			Optional[Any]         =			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
     __magic_name__   =			'nezha'




     def __init__(    self				,     snake_case_=2_1128				,     snake_case_=768				,     snake_case_=12				,     snake_case_=12				,     snake_case_=3072				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=64				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=1E-12				,     snake_case_=0.1				,     snake_case_=0				,     snake_case_=2				,     snake_case_=3				,     snake_case_=True				,     **snake_case_				,     ):
         super().__init__(pad_token_id=snake_case_				,     bos_token_id=snake_case_				,     eos_token_id=snake_case_				,     **snake_case_							)

         _A     =							vocab_size
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							hidden_act
         _A     =							intermediate_size
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							max_position_embeddings
         _A     =							max_relative_position
         _A     =							type_vocab_size
         _A     =							initializer_range
         _A     =							layer_norm_eps
         _A     =							classifier_dropout
         _A     =							use_cache





