


"""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

snake_case__		:     int    				=				random.Random()


def      _snake_case	(						_snake_case				:			List[Any] ,   _snake_case				:			str=1.0 ,   _snake_case				:			Dict=None ,   _snake_case				:			Tuple=None			):
      if rng is None:
            lowerCAmelCase					:	Optional[int]       =   global_rng

      lowerCAmelCase					:	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 		snake_case_(       unittest.TestCase			):






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






     def      lowerCamelCase__     (	self  :					str				):
           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  :					int					,				UpperCamelCase_  :					Tuple=False					,				UpperCamelCase_  :					int=False				):
           def _flatten(UpperCamelCase_  :					Union[str, Any]				):
                 return list(itertools.chain(*__A				)				)

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



@require_torch
@require_torchaudio
class 		snake_case_(       UpperCamelCase_							,					unittest.TestCase			):
     __UpperCamelCase  =					TvltFeatureExtractor






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Dict       =   TvltFeatureExtractionTester(self				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	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      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	int       =   self.feature_extraction_class(**self.feat_extract_dict				)

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

           lowerCAmelCase					:	List[str]       =   feat_extract_first.to_dict()
           lowerCAmelCase					:	str       =   feat_extract_second.to_dict()
           lowerCAmelCase					:	List[Any]       =   dict_first.pop('''mel_filters'''				)
           lowerCAmelCase					:	Any       =   dict_second.pop('''mel_filters'''				)
           self.assertTrue(np.allclose(__A					,				__A				)				)
           self.assertEqual(__A					,				__A				)






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Dict       =   self.feature_extraction_class(**self.feat_extract_dict				)

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

           lowerCAmelCase					:	Dict       =   feat_extract_first.to_dict()
           lowerCAmelCase					:	Any       =   feat_extract_second.to_dict()
           lowerCAmelCase					:	List[str]       =   dict_first.pop('''mel_filters'''				)
           lowerCAmelCase					:	int       =   dict_second.pop('''mel_filters'''				)
           self.assertTrue(np.allclose(__A					,				__A				)				)
           self.assertEqual(__A					,				__A				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           # Initialize feature_extractor
           lowerCAmelCase					:	Union[str, Any]       =   self.feature_extraction_class(**self.feat_extract_dict				)

           # create three inputs of length 800, 1000, and 1200
           lowerCAmelCase					:	Dict       =   [floats_list((1, x)				)[0] for x in range(8_0_0					,				1_4_0_0					,				2_0_0				)]
           lowerCAmelCase					:	Union[str, Any]       =   [np.asarray(__A				) for speech_input in speech_inputs]

           # Test not batched input
           lowerCAmelCase					:	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
           lowerCAmelCase					:	Dict       =   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
           lowerCAmelCase					:	Union[str, 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.
           lowerCAmelCase					:	Any       =   [floats_list((1, x)				)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
           lowerCAmelCase					:	str       =   np.asarray(__A				)
           lowerCAmelCase					:	Tuple       =   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      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Dict				):
           lowerCAmelCase					:	List[Any]       =   load_dataset('''hf-internal-testing/librispeech_asr_dummy'''					,				'''clean'''					,				split='''validation'''				)
           # automatic decoding with librispeech
           lowerCAmelCase					:	Tuple       =   ds.sort('''id'''				).select(range(__A				)				)[:num_samples]["audio"]

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






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Dict       =   self._load_datasamples(1				)
           lowerCAmelCase					:	Any       =   TvltFeatureExtractor()
           lowerCAmelCase					:	List[str]       =   feature_extractor(__A					,				return_tensors='''pt'''				).audio_values

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

           lowerCAmelCase					:	Optional[int]       =   torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]				)
           self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]					,				__A					,				atol=1E-4				)				)






"""simple docstring"""





from torch import nn



class 		snake_case_(       nn.Module			):






     def __init__(	self  :					int					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					int				):
           super().__init__()
           lowerCAmelCase					:	str       =   class_size
           lowerCAmelCase					:	Dict       =   embed_size
           # self.mlp1 = nn.Linear(embed_size, embed_size)
           # self.mlp2 = (nn.Linear(embed_size, class_size))
           lowerCAmelCase					:	Any       =   nn.Linear(UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					Tuple				):
           # hidden_state = nn.functional.relu(self.mlp1(hidden_state))
           # hidden_state = self.mlp2(hidden_state)
           lowerCAmelCase					:	int       =   self.mlp(UpperCamelCase_				)
           return logits


