



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       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






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

       UpperCAmelCase_     :       str  		=					torch.tensor(
           [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]  ,device=lowerCamelCase_  ,dtype=torch.long  ,)

       with torch.no_grad():
           UpperCAmelCase_     :       str  		=					model(lowerCamelCase_							).logits

       UpperCAmelCase_     :       Union[str, Any]  		=					torch.Size((1, 9, 50257)							)
       self.assertEqual(output.shape  ,lowerCamelCase_							)

       UpperCAmelCase_     :       Any  		=					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=lowerCamelCase_  ,dtype=torch.half  ,)

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








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						= True
						__lowerCamelCase						= ''
except Exception as e:
						__lowerCamelCase						= False
						__lowerCamelCase						= (
						    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_							(				UpperCamelCase ,       UpperCamelCase       ):
						lowercase									=  '''mel_config.json'''




						@register_to_config
						def __init__(    self		,	lowercase = 256		,	lowercase = 256		,	lowercase = 22_050		,	lowercase = 2_048		,	lowercase = 512		,	lowercase = 80		,	lowercase = 32		,	)			->						Optional[Any]:
													_a  :    Tuple			=						hop_length
													_a  :    Dict			=						sample_rate
													_a  :    Dict			=						n_fft
													_a  :    List[str]			=						top_db
													_a  :    Optional[int]			=						n_iter
													self.set_resolution(lowercase		,	lowercase       )
													_a  :    Optional[int]			=						None

													if not _librosa_can_be_imported:
																				raise ValueError(_import_error       )




						def      snake_case__(    self		,	lowercase		,	lowercase       )			->						Any:
													_a  :    Any			=						x_res
													_a  :    Optional[Any]			=						y_res
													_a  :    List[Any]			=						self.y_res
													_a  :    Tuple			=						self.x_res * self.hop_length - 1




						def      snake_case__(    self		,	lowercase = None		,	lowercase = None       )			->						Union[str, Any]:
													if audio_file is not None:
																				_a		,					_a  :    str			=						librosa.load(lowercase		,	mono=lowercase		,	sr=self.sr       )
													else:
																				_a  :    Any			=						raw_audio

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




						def      snake_case__(    self       )			->						int:
													return len(self.audio       ) // self.slice_size




						def      snake_case__(    self		,	lowercase = 0       )			->						np.ndarray:
													return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




						def      snake_case__(    self       )			->						int:
													return self.sr




						def      snake_case__(    self		,	lowercase       )			->						Image.Image:
													_a  :    int			=						librosa.feature.melspectrogram(
													    y=self.get_audio_slice(lowercase       )		,	sr=self.sr		,	n_fft=self.n_fft		,	hop_length=self.hop_length		,	n_mels=self.n_mels       )
													_a  :    List[str]			=						librosa.power_to_db(lowercase		,	ref=np.max		,	top_db=self.top_db       )
													_a  :    List[str]			=						(((log_S + self.top_db) * 255 / self.top_db).clip(0		,	255       ) + 0.5).astype(np.uinta       )
													_a  :    Any			=						Image.fromarray(lowercase       )
													return image







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