


import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
				import librosa  # noqa: E402

				lowerCAmelCase__ :			List[str]         =True
				lowerCAmelCase__ :			Union[str, Any]         =''
except Exception as e:
				lowerCAmelCase__ :			str         =False
				lowerCAmelCase__ :			Dict         =(
				    F"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
				)


from PIL import Image  # noqa: E402







class   __lowercase						(__UpperCAmelCase  ,					__UpperCAmelCase     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	"""mel_config.json"""






							@register_to_config
							def __init__(			self ,	lowerCAmelCase__ = 2_5_6 ,	lowerCAmelCase__ = 2_5_6 ,	lowerCAmelCase__ = 2_2_0_5_0 ,	lowerCAmelCase__ = 2_0_4_8 ,	lowerCAmelCase__ = 5_1_2 ,	lowerCAmelCase__ = 8_0 ,	lowerCAmelCase__ = 3_2 ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Dict 		=				hop_length
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				sample_rate
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				n_fft
										SCREAMING_SNAKE_CASE_		:    int 		=				top_db
										SCREAMING_SNAKE_CASE_		:    int 		=				n_iter
										self.set_resolution(lowerCamelCase__ ,	lowerCamelCase__					)
										SCREAMING_SNAKE_CASE_		:    Dict 		=				None

										if not _librosa_can_be_imported:
													raise ValueError(_import_error					)






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    str 		=				x_res
										SCREAMING_SNAKE_CASE_		:    str 		=				y_res
										SCREAMING_SNAKE_CASE_		:    Any 		=				self.y_res
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				self.x_res * self.hop_length - 1






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = None					):





										"""simple docstring"""




										if audio_file is not None:
													SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				librosa.load(lowerCamelCase__ ,	mono=lowerCamelCase__ ,	sr=self.sr					)
										else:
													SCREAMING_SNAKE_CASE_		:    List[str] 		=				raw_audio

										# Pad with silence if necessary.
										if len(self.audio					) < self.x_res * self.hop_length:
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio					),)					)]					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return len(self.audio					) // self.slice_size






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ = 0					):





										"""simple docstring"""




										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return self.sr






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				librosa.feature.melspectrogram(
										    y=self.get_audio_slice(lowerCamelCase__					) ,	sr=self.sr ,	n_fft=self.n_fft ,	hop_length=self.hop_length ,	n_mels=self.n_mels					)
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				librosa.power_to_db(lowerCamelCase__ ,	ref=np.max ,	top_db=self.top_db					)
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				(((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0 ,	2_5_5					) + 0.5).astype(np.uinta					)
										SCREAMING_SNAKE_CASE_		:    int 		=				Image.fromarray(lowerCamelCase__					)
										return image






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				np.frombuffer(image.tobytes() ,	dtype='uint8'					).reshape((image.height, image.width)					)
										SCREAMING_SNAKE_CASE_		:    Dict 		=				bytedata.astype('float'					) * self.top_db / 2_5_5 - self.top_db
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				librosa.db_to_power(lowerCamelCase__					)
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				librosa.feature.inverse.mel_to_audio(
										    lowerCamelCase__ ,	sr=self.sr ,	n_fft=self.n_fft ,	hop_length=self.hop_length ,	n_iter=self.n_iter					)
										return audio









from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										return True






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'					)
										SCREAMING_SNAKE_CASE_		:    int 		=				'Hugging Face大法好，谁用谁知道。'
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				'Hugging Face大法好,谁用谁知道。<unk>'
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				tokenizer.tokenize(lowerCAmelCase__					)
										self.assertListEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tokens + [tokenizer.unk_token]

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				[1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__					) ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tokenizer.decode(lowerCAmelCase__					)
										self.assertEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)





