
import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					import torch

					from transformers import GPTaLMHeadModel







@require_torch
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	@slow
	@unittest.skip('''Model is not available.'''		)
	def 	__snake_case					(      self		):
					A__	:						int								=				'''nvidia/megatron-gpt2-345m/'''
					if "MYDIR" in os.environ:
									A__	:						Union[str, Any]								=				os.path.join(os.environ['''MYDIR''']					,							UpperCamelCase__		)
					A__	:						Union[str, Any]								=				GPTaLMHeadModel.from_pretrained(UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.half()

					A__	:						Dict								=				torch.tensor(
					    [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]					,							device=UpperCamelCase__					,							dtype=torch.long					,							)

					with torch.no_grad():
									A__	:						str								=				model(UpperCamelCase__		).logits

					A__	:						int								=				torch.Size((1, 9, 5_0257)		)
					self.assertEqual(output.shape					,							UpperCamelCase__		)

					A__	:						Dict								=				torch.tensor(
					    [
					        4.9_4_1_4,
					        -0.2_9_2_0,
					        -1.2_1_4_8,
					        -4.0_2_7_3,
					        -0.5_1_6_1,
					        -5.2_1_0_9,
					        -1.2_4_1_2,
					        -1.8_3_0_1,
					        -1.7_7_3_4,
					        -4.7_1_4_8,
					        -0.2_3_1_7,
					        -1.0_8_1_1,
					        -2.1_7_7_7,
					        0.4_1_4_1,
					        -3.7_9_6_9,
					        -4.0_5_8_6,
					        -2.5_3_3_2,
					        -3.3_8_0_9,
					        4.3_8_6_7,
					    ]					,							device=UpperCamelCase__					,							dtype=torch.half					,							)

					for i in range(19		):
									A__							,				A__	:						int								=				8 * i // 17, 2792 * i  # along the diagonal
									A__							,				A__	:						Optional[Any]								=				output[0, r, c], expected_diag[i]
									A__	:						Dict								=				F"row={r} col={c} computed={computed} expected={expected}"
									self.assertAlmostEqual(UpperCamelCase__					,							UpperCamelCase__					,							delta=1e-4					,							msg=UpperCamelCase__		)

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

					_SCREAMING_SNAKE_CASE     :   Tuple							 =      True
					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      ''
except Exception as e:
					_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      False
					_SCREAMING_SNAKE_CASE     :   Any							 =      (
					    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 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					"mel_config.json"





	@register_to_config
	def __init__(      self					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 2_2050					,							UpperCamelCase__ = 2048					,							UpperCamelCase__ = 512					,							UpperCamelCase__ = 80					,							UpperCamelCase__ = 32					,							):
					A__	:						Any								=				hop_length
					A__	:						List[Any]								=				sample_rate
					A__	:						Union[str, Any]								=				n_fft
					A__	:						Optional[int]								=				top_db
					A__	:						Optional[Any]								=				n_iter
					self.set_resolution(UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						str								=				None

					if not _librosa_can_be_imported:
									raise ValueError(_import_error		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						int								=				x_res
					A__	:						List[Any]								=				y_res
					A__	:						List[str]								=				self.y_res
					A__	:						int								=				self.x_res * self.hop_length - 1





	def 	__snake_case					(      self					,							UpperCamelCase__ = None					,							UpperCamelCase__ = None		):
					if audio_file is not None:
									A__							,				A__	:						Union[str, Any]								=				librosa.load(UpperCamelCase__					,							mono=UpperCamelCase__					,							sr=self.sr		)
					else:
									A__	:						Tuple								=				raw_audio

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





	def 	__snake_case					(      self		):
					return len(self.audio		) // self.slice_size





	def 	__snake_case					(      self					,							UpperCamelCase__ = 0		):
					return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]





	def 	__snake_case					(      self		):
					return self.sr





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						Optional[Any]								=				librosa.feature.melspectrogram(
					    y=self.get_audio_slice(UpperCamelCase__		)					,							sr=self.sr					,							n_fft=self.n_fft					,							hop_length=self.hop_length					,							n_mels=self.n_mels		)
					A__	:						str								=				librosa.power_to_db(UpperCamelCase__					,							ref=np.max					,							top_db=self.top_db		)
					A__	:						str								=				(((log_S + self.top_db) * 255 / self.top_db).clip(0					,							255		) + 0.5).astype(np.uinta		)
					A__	:						Dict								=				Image.fromarray(UpperCamelCase__		)
					return image





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						List[Any]								=				np.frombuffer(image.tobytes()					,							dtype='''uint8'''		).reshape((image.height, image.width)		)
					A__	:						Dict								=				bytedata.astype('''float'''		) * self.top_db / 255 - self.top_db
					A__	:						Union[str, Any]								=				librosa.db_to_power(UpperCamelCase__		)
					A__	:						Dict								=				librosa.feature.inverse.mel_to_audio(
					    UpperCamelCase__					,							sr=self.sr					,							n_fft=self.n_fft					,							hop_length=self.hop_length					,							n_iter=self.n_iter		)
					return audio