"""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

__lowerCamelCase											=  random.Random()






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__=1.0       ,    UpperCamelCase__=None       ,    UpperCamelCase__=None				):






							"""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     UpperCamelCase__(			unittest.TestCase ):





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase=7		,__UpperCAmelCase=4_00		,__UpperCAmelCase=20_00		,__UpperCAmelCase=20_48		,__UpperCAmelCase=1_28		,__UpperCAmelCase=1		,__UpperCAmelCase=5_12		,__UpperCAmelCase=30		,__UpperCAmelCase=4_41_00		,)					->				Tuple:
													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 				snake_case__	(       self     )					->				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 				snake_case__	(       self		,__UpperCAmelCase=False		,__UpperCAmelCase=False     )					->				Optional[Any]:

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

													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(__UpperCAmelCase     ) for x in speech_inputs]
													return speech_inputs






@require_torch
@require_torchaudio
class     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						str													=		TvltFeatureExtractor





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							TvltFeatureExtractionTester(self     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							self.feature_extraction_class(**self.feat_extract_dict     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'spectrogram_length'     )     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'feature_size'     )     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'num_audio_channels'     )     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'hop_length'     )     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'chunk_length'     )     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'sampling_rate'     )     )





						def 				snake_case__	(       self     )					->				Any:
													A__	     =							self.feature_extraction_class(**self.feat_extract_dict     )

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

													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(__UpperCAmelCase		,__UpperCAmelCase     )     )
													self.assertEqual(__UpperCAmelCase		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							self.feature_extraction_class(**self.feat_extract_dict     )

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

													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(__UpperCAmelCase		,__UpperCAmelCase     )     )
													self.assertEqual(__UpperCAmelCase		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Tuple:
													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(8_00		,14_00		,2_00     )]
													A__	     =							[np.asarray(__UpperCAmelCase     ) for speech_input in speech_inputs]

													# Test not batched input
													A__	     =							feature_extractor(np_speech_inputs[0]		,return_tensors='np'		,sampling_rate=4_41_00     ).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(__UpperCAmelCase		,return_tensors='np'		,sampling_rate=4_41_00     ).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(
													    __UpperCAmelCase		,return_tensors='np'		,sampling_rate=4_41_00		,mask_audio=__UpperCAmelCase     ).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 (8_00, 8_00, 8_00)]
													A__	     =							np.asarray(__UpperCAmelCase     )
													A__	     =							feature_extractor(__UpperCAmelCase		,return_tensors='np'		,sampling_rate=4_41_00     ).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 				snake_case__	(       self		,__UpperCAmelCase     )					->				int:
													A__	     =							load_dataset('hf-internal-testing/librispeech_asr_dummy'		,'clean'		,split='validation'     )
													# automatic decoding with librispeech
													A__	     =							ds.sort('id'     ).select(range(__UpperCAmelCase     )     )[:num_samples]['audio']

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





						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							self._load_datasamples(1     )
													A__	     =							TvltFeatureExtractor()
													A__	     =							feature_extractor(__UpperCAmelCase		,return_tensors='pt'     ).audio_values

													self.assertEquals(audio_values.shape		,(1, 1, 1_92, 1_28)     )

													A__	     =							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]		,__UpperCAmelCase		,atol=1e-4     )     )






"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__lowerCamelCase											=  logging.get_logger(__name__)
class     UpperCamelCase__(			__A ):





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				None:
													warnings.warn(
													    'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
													    ' Please use ImageGPTImageProcessor instead.'		,__UpperCAmelCase		,)
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )





