
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		: Dict								=     random.Random()




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	List[str]=1.0		,   SCREAMING_SNAKE_CASE_	:	str=None		,   SCREAMING_SNAKE_CASE_	:	Tuple=None       )  ->     Optional[int]:


						if rng is None:
												_lowercase					       =					global_rng

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

						return values



class    a_ (			unittest.TestCase    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=7				,			__UpperCamelCase=400				,			__UpperCamelCase=2_000				,			__UpperCamelCase=2_048				,			__UpperCamelCase=128				,			__UpperCamelCase=1				,			__UpperCamelCase=512				,			__UpperCamelCase=30				,			__UpperCamelCase=44_100				,			):
									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					min_seq_length
									_lowercase					       =					max_seq_length
									_lowercase					       =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									_lowercase					       =					spectrogram_length
									_lowercase					       =					feature_size
									_lowercase					       =					num_audio_channels
									_lowercase					       =					hop_length
									_lowercase					       =					chunk_length
									_lowercase					       =					sampling_rate






			def    UpperCamelCase_				(      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    UpperCamelCase_				(      self				,			__UpperCamelCase=False				,			__UpperCamelCase=False  ):
									def _flatten(__UpperCamelCase  ):
															return list(itertools.chain(*__snake_case  )  )

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







@require_torch
@require_torchaudio
class    a_ (			snake_case_					,			unittest.TestCase    ):
			a	:			Union[str, Any]					   =							TvltFeatureExtractor






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					TvltFeatureExtractionTester(self  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					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    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )

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

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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )

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

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






			def    UpperCamelCase_				(      self  ):
									# Initialize feature_extractor
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )

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

									# Test not batched input
									_lowercase					       =					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
									_lowercase					       =					feature_extractor(__snake_case				,			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
									_lowercase					       =					feature_extractor(
									    __snake_case				,			return_tensors="""np"""				,			sampling_rate=44_100				,			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.
									_lowercase					       =					[floats_list((1, x)  )[0] for x in (800, 800, 800)]
									_lowercase					       =					np.asarray(__snake_case  )
									_lowercase					       =					feature_extractor(__snake_case				,			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				,			__UpperCamelCase  ):
									_lowercase					       =					load_dataset("""hf-internal-testing/librispeech_asr_dummy"""				,			"""clean"""				,			split="""validation"""  )
									# automatic decoding with librispeech
									_lowercase					       =					ds.sort("""id"""  ).select(range(__snake_case  )  )[:num_samples]["""audio"""]

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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self._load_datasamples(1  )
									_lowercase					       =					TvltFeatureExtractor()
									_lowercase					       =					feature_extractor(__snake_case				,			return_tensors="""pt"""  ).audio_values

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

									_lowercase					       =					torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]  )
									self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]				,			__snake_case				,			atol=1E-4  )  )






'''simple docstring'''






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream




class 					UpperCAmelCase	(     snake_case_  ):




      def __init__(     self     :					Union[str, Any]     ,     __snake_case     :					Callable     ,     __snake_case     :					Optional[Features] = None     ,     __snake_case     :					str = None     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional[dict] = None     ,     __snake_case     :					Optional[int] = None     ,     **__snake_case     :					Optional[Any]     ,     )							-> Union[str, Any]:
            super().__init__(
                features=__snake_case     ,     cache_dir=__snake_case     ,     keep_in_memory=__snake_case     ,     streaming=__snake_case     ,     num_proc=__snake_case     ,     **__snake_case     ,     )
            _lowerCAmelCase					=				Generator(
                cache_dir=__snake_case     ,     features=__snake_case     ,     generator=__snake_case     ,     gen_kwargs=__snake_case     ,     **__snake_case     ,     )




      def 							lowercase__       (     self     :					str    )							-> Dict:
            # Build iterable dataset
            if self.streaming:
                  _lowerCAmelCase					=				self.builder.as_streaming_dataset(split="""train"""    )
            # Build regular (map-style) dataset
            else:
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None

                  self.builder.download_and_prepare(
                      download_config=__snake_case     ,     download_mode=__snake_case     ,     verification_mode=__snake_case     ,     base_path=__snake_case     ,     num_proc=self.num_proc     ,     )
                  _lowerCAmelCase					=				self.builder.as_dataset(
                      split="""train"""     ,     verification_mode=__snake_case     ,     in_memory=self.keep_in_memory    )
            return dataset






