

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a_     =			logging.get_logger(__name__)






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							=['input_features']
			def __init__(	self							:       Optional[Any]					,					a							:       str=80					,					a							:       Union[str, Any]=1_6000					,					a							:       List[str]=160					,					a							:       Any=30					,					a							:       Dict=400					,					a							:       Optional[int]=0.0					,					a							:       Union[str, Any]=False					,					**a							:       Optional[int]					,					)							->				List[str]:

				"""simple docstring"""

				super().__init__(
				    feature_size=a					,					sampling_rate=a					,					padding_value=a					,					return_attention_mask=a					,					**a					,					)
				SCREAMING_SNAKE_CASE      :					Tuple    =				n_fft
				SCREAMING_SNAKE_CASE      :					Tuple    =				hop_length
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				chunk_length
				SCREAMING_SNAKE_CASE      :					List[str]    =				chunk_length * sampling_rate
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.n_samples // hop_length
				SCREAMING_SNAKE_CASE      :					Dict    =				sampling_rate
				SCREAMING_SNAKE_CASE      :					List[str]    =				mel_filter_bank(
				    num_frequency_bins=1 + n_fft // 2					,					num_mel_filters=a					,					min_frequency=0.0					,					max_frequency=8000.0					,					sampling_rate=a					,					norm="slaney"					,					mel_scale="slaney"					,					)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]					,					a							:       np.array		)							->				np.ndarray:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				spectrogram(
				    a					,					window_function(self.n_fft					,					"hann"		)					,					frame_length=self.n_fft					,					hop_length=self.hop_length					,					power=2.0					,					mel_filters=self.mel_filters					,					log_mel="log10"					,					)
				SCREAMING_SNAKE_CASE      :					str    =				log_spec[:, :-1]
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				np.maximum(a					,					log_spec.max() - 8.0		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				(log_spec + 4.0) / 4.0
				return log_spec
			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def 							__UpperCamelCase       (	a							:       List[np.ndarray]					,					a							:       List[np.ndarray]					,					a							:       float = 0.0		)							->				List[np.ndarray]:

				"""simple docstring"""

				if attention_mask is not None:
					SCREAMING_SNAKE_CASE      :					str    =				np.array(a					,					np.intaa		)
					SCREAMING_SNAKE_CASE      :					int    =				[]

					for vector, length in zip(a					,					attention_mask.sum(-1		)		):
						SCREAMING_SNAKE_CASE      :					int    =				(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7		)
						if length < normed_slice.shape[0]:
							SCREAMING_SNAKE_CASE      :					List[str]    =				padding_value

						normed_input_values.append(a		)
				else:
					SCREAMING_SNAKE_CASE      :					List[str]    =				[(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

				return normed_input_values
			def __call__(	self							:       List[Any]					,					a							:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,					a							:       bool = True					,					a							:       Optional[int] = None					,					a							:       Optional[Union[str, TensorType]] = None					,					a							:       Optional[bool] = None					,					a							:       Optional[str] = "max_length"					,					a							:       Optional[int] = None					,					a							:       Optional[int] = None					,					a							:       Optional[bool] = None					,					**a							:       Union[str, Any]					,					)							->				BatchFeature:

				"""simple docstring"""

				if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
						raise ValueError(
						    F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
						    F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
						    F" was sampled with {self.sampling_rate} and not {sampling_rate}."		)
				else:
					logger.warning(
					    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
					    "Failing to do so can result in silent errors that might be hard to debug."		)

				SCREAMING_SNAKE_CASE      :					int    =				isinstance(a					,					np.ndarray		) and len(raw_speech.shape		) > 1
				if is_batched_numpy and len(raw_speech.shape		) > 2:
					raise ValueError(F"Only mono-channel audio is supported for input to {self}"		)
				SCREAMING_SNAKE_CASE      :					int    =				is_batched_numpy or (
				    isinstance(a					,					(list, tuple)		) and (isinstance(raw_speech[0]					,					(np.ndarray, tuple, list)		))
				)

				if is_batched:
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				[np.asarray([speech]					,					dtype=np.floataa		).T for speech in raw_speech]
				elif not is_batched and not isinstance(a					,					np.ndarray		):
					SCREAMING_SNAKE_CASE      :					Dict    =				np.asarray(a					,					dtype=np.floataa		)
				elif isinstance(a					,					np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				raw_speech.astype(np.floataa		)

				# always return batch
				if not is_batched:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[np.asarray([raw_speech]		).T]

				SCREAMING_SNAKE_CASE      :					Any    =				BatchFeature({"input_features": raw_speech}		)

				# convert into correct format for padding

				SCREAMING_SNAKE_CASE      :					Tuple    =				self.pad(
				    a					,					padding=a					,					max_length=max_length if max_length else self.n_samples					,					truncation=a					,					pad_to_multiple_of=a					,					return_attention_mask=return_attention_mask or do_normalize					,					)

				# zero-mean and unit-variance normalization
				if do_normalize:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.zero_mean_unit_var_norm(
					    padded_inputs["input_features"]					,					attention_mask=padded_inputs["attention_mask"]					,					padding_value=self.padding_value					,					)
					SCREAMING_SNAKE_CASE      :					Any    =				np.stack(padded_inputs["input_features"]					,					axis=0		)

				# make sure list is in array format
				SCREAMING_SNAKE_CASE      :					Dict    =				padded_inputs.get("input_features"		).transpose(2					,					0					,					1		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				[self._np_extract_fbank_features(a		) for waveform in input_features[0]]

				if isinstance(input_features[0]					,					a		):
					SCREAMING_SNAKE_CASE      :					int    =				[np.asarray(a					,					dtype=np.floataa		) for feature in input_features]
				else:
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				input_features

				if return_attention_mask:
					# rescale from sample (48000) to feature (3000)
					SCREAMING_SNAKE_CASE      :					Tuple    =				padded_inputs["attention_mask"][:, :: self.hop_length]

				if return_tensors is not None:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				padded_inputs.convert_to_tensors(a		)

				return padded_inputs






			def 							__UpperCamelCase       (	self							:       int		)							->				Dict[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				copy.deepcopy(self.__dict__		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.__class__.__name__
				if "mel_filters" in output:
					del output["mel_filters"]
				return output



"""simple docstring"""



import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch







class _lowerCAmelCase				(      unittest.TestCase	):
   __UpperCAmelCase :       Union[str, Any]							  =  JukeboxTokenizer
   __UpperCAmelCase :       Union[str, Any]							  =  {
       '''artist''': '''Zac Brown Band''',
       '''genres''': '''Country''',
       '''lyrics''': '''I met a traveller from an antique land,
        Who said "Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        ''',
   }







   @require_torch
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       import torch

       snake_case    :      Any      					=    JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics"						)
       snake_case    :      Optional[Any]      					=    tokenizer(**self.metas						)["input_ids"]
       # fmt: off
       snake_case    :      Optional[int]      					=    [
           torch.tensor([[
               0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
               76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
               44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
               47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
               76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
               30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
               27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
               45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
               41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
               76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
               76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
               64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
               30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
               27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
               34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
               27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
               41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
               76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
               44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
               76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
               32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
               40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
               45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
               31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
               45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
               76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
               34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
               31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
               40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
               38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
               76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
               41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
               27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
               46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
               76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
               41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
               46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
               41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
               40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
               27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
               76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
               76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
               41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
               76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
               27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
               34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
               44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
               40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
               46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
               38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
               40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
               27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
               76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
               76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
               76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
               76, 76]]						),
           torch.tensor([[0, 0, 0, 1069, 11]]						),
           torch.tensor([[0, 0, 0, 1069, 11]]						),
       ]
       # fmt: on
       self.assertTrue(torch.allclose(tokens[0]						,	EXPECTED_OUTPUT[0]						)						)
       self.assertTrue(torch.allclose(tokens[1]						,	EXPECTED_OUTPUT[1]						)						)
       self.assertTrue(torch.allclose(tokens[2]						,	EXPECTED_OUTPUT[2]						)						)







   @require_torch
   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       import torch

       snake_case    :      Tuple      					=    JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics"						)
       snake_case    :      Optional[Any]      					=    tokenizer(**self.metas						)["input_ids"]
       # fmt: off
       snake_case    :      List[Any]      					=    [
           torch.tensor([[
               0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
               31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
               31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
               40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
               79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
               77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
               27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
               37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
               32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
               77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
               77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
               46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
               77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
               77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
               77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
               77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
               64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
               40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
               40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
               38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
               31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
               41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
               46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
               41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
               31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
               31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
               23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
               44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
               31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
               38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
               40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
               27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
               31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
               34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
               31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
               1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
               31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
               45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
               31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
               15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
               11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
               45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
               41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
               44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
               46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
               27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
               35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
               77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
               31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
               41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
               77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
               40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
               77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
               27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
               77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
               77, 77, 77, 77, 77, 77]]						),
           torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]						),
           torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]						),
       ]
       # fmt: on
       self.assertTrue(torch.allclose(tokens[0]						,	EXPECTED_OUTPUT[0]						)						)
       self.assertTrue(torch.allclose(tokens[1]						,	EXPECTED_OUTPUT[1]						)						)
       self.assertTrue(torch.allclose(tokens[2]						,	EXPECTED_OUTPUT[2]						)						)





