'''simple docstring'''





import unittest
from unittest import skip

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed


if is_torch_available():
   import torch

   from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer





@require_torch
class 	_lowercase		(      unittest.TestCase ):
 lowercase         =     (JukeboxModel,) if is_torch_available() else ()
 lowercase         =     'openai/jukebox-1b-lyrics'
 lowercase         =     {
     'artist': 'Zac Brown Band',
     'genres': 'Country',
     'lyrics': 'I met a traveller from an antique land,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
 }
 # fmt: off
 lowercase         =     [
     1_8_6_4, 1_5_3_6, 1_2_1_3, 1_8_7_0, 1_3_5_7, 1_5_3_6, 5_1_9, 8_8_0, 1_3_2_3, 7_8_9, 1_0_8_2, 5_3_4,
     1_0_0_0, 1_4_4_5, 1_1_0_5, 1_1_3_0, 9_6_7, 5_1_5, 1_4_3_4, 1_6_2_0, 5_3_4, 1_4_9_5, 2_8_3, 1_4_4_5,
     3_3_3, 1_3_0_7, 5_3_9, 1_6_3_1, 1_5_2_8, 3_7_5, 1_4_3_4, 6_7_3, 6_2_7, 7_1_0, 7_7_8, 1_8_8_3,
     1_4_0_5, 1_2_7_6, 1_4_5_5, 1_2_2_8
 ]

 lowercase         =     [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]

 lowercase         =     [
     1_1_2_5, 1_7_5_1, 6_9_7, 1_7_7_6, 1_1_4_1, 1_4_7_6, 3_9_1, 6_9_7, 1_1_2_5, 6_8_4, 8_6_7, 4_1_6,
     8_4_4, 1_3_7_2, 1_2_7_4, 7_1_7, 1_2_7_4, 8_4_4, 1_2_9_9, 1_4_1_9, 6_9_7, 1_3_7_0, 3_1_7, 1_1_2_5,
     1_9_1, 1_4_4_0, 1_3_7_0, 1_4_4_0, 1_3_7_0, 2_8_2, 1_6_2_1, 1_3_7_0, 3_6_8, 3_4_9, 8_6_7, 1_8_7_2,
     1_2_6_2, 8_6_9, 1_7_2_8, 7_4_7
 ]
 lowercase         =     [
     4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]

 lowercase         =     [
     1_7_5_5, 8_4_2, 3_0_7, 1_8_4_3, 1_0_2_2, 1_3_9_5, 2_3_4, 1_5_5_4, 8_0_6, 7_3_9, 1_0_2_2, 4_4_2,
     6_1_6, 5_5_6, 2_6_8, 1_4_9_9, 9_3_3, 4_5_7, 1_4_4_0, 1_8_3_7, 7_5_5, 9_8_5, 3_0_8, 9_0_2,
     2_9_3, 1_4_4_3, 1_6_7_1, 1_1_4_1, 1_5_3_3, 5_5_5, 1_5_6_2, 1_0_6_1, 2_8_7, 4_1_7, 1_0_2_2, 2_0_0_8,
     1_1_8_6, 1_0_1_5, 1_7_7_7, 2_6_8
 ]
 lowercase         =     [
     8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2,
     1_8_5, 4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 3_0_7, 8_4_2,
     5_9_1, 8_4_2, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2,
     1_8_5, 8_4_2, 5_9_1, 8_9
 ]

 lowercase         =     [1_0_5_8_3_0_4, 0, 7_8_6_4_3_2, 7_1_6_9, 5_0_7, 7_6, 2_7, 4_0, 3_0, 7_6]

 lowercase         =     [
     3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
     1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
     1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
     1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
 ]
 lowercase         =     [
     1_2_3_6, 1_6_6_8, 1_4_8_4, 1_9_2_0, 1_8_4_8, 1_4_0_9, 1_3_9, 8_6_4, 1_8_2_8, 1_2_7_2, 1_5_9_9, 8_2_4,
     1_6_7_2, 1_3_9, 5_5_5, 1_4_8_4, 8_2_4, 1_9_2_0, 5_5_5, 5_9_6, 1_5_7_9, 1_5_9_9, 1_2_3_1, 1_5_9_9,
     1_6_3_7, 1_4_0_7, 2_1_2, 8_2_4, 1_5_9_9, 1_1_6, 1_4_3_3, 8_2_4, 2_5_8, 1_5_9_9, 1_4_3_3, 1_8_9_5,
     1_0_6_3, 1_4_3_3, 1_4_3_3, 1_5_9_9
 ]
 lowercase         =     [
     1_6_8_4, 1_8_7_3, 1_1_1_9, 1_1_8_9, 3_9_5, 6_1_1, 1_9_0_1, 9_7_2, 8_9_0, 1_3_3_7, 1_3_9_2, 1_9_2_7,
     9_6, 9_7_2, 6_7_2, 7_8_0, 1_1_1_9, 8_9_0, 1_5_8, 7_7_1, 1_0_7_3, 1_9_2_7, 3_5_3, 1_3_3_1,
     1_2_6_9, 1_4_5_9, 1_3_3_3, 1_6_4_5, 8_1_2, 1_5_7_7, 1_3_3_7, 6_0_6, 3_5_3, 9_8_1, 1_4_6_6, 6_1_9,
     1_9_7, 3_9_1, 3_0_2, 1_9_3_0
 ]
 lowercase         =     [
     3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
     1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
     1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
     1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
 ]
 lowercase         =     [
     -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
     -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
     0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
     0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
     0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
 ]
 lowercase         =     [
     0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
     0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
     -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
     -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
 ]
 lowercase         =     [
     0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
     0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
     -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
     -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
 ]
 lowercase         =     [
     7_6, 2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5, 4_5, 7_6, 3_8, 3_1, 3_3,
     4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6, 4_1, 4_0, 3_1, 7_8, 7_6
 ]

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      JukeboxTokenizer.from_pretrained(self.model_id    )
  UpperCamelCase_      :		Optional[Any]				=      tokenizer(**self.metas    )['input_ids']
  return tokens

 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]    )     ->		Tuple:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      JukeboxModel.from_pretrained(self.model_id     ,   min_duration=0    ).eval()
  UpperCamelCase_      :		Any				=      self.prepare_inputs()

  set_seed(0    )
  UpperCamelCase_      :		Any				=      [torch.zeros(1     ,   0     ,   dtype=torch.long    ).cpu() for _ in range(3    )]
  UpperCamelCase_      :		int				=      model._sample(snake_case     ,   snake_case     ,   [0]     ,   sample_length=4_0 * model.priors[0].raw_to_tokens     ,   save_results=snake_case    )
  self.assertIn(zs[0][0].detach().cpu().tolist()     ,   [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]    )

  set_seed(0    )
  UpperCamelCase_      :		str				=      model._sample(snake_case     ,   snake_case     ,   [1]     ,   sample_length=4_0 * model.priors[1].raw_to_tokens     ,   save_results=snake_case    )
  self.assertIn(zs[1][0].detach().cpu().tolist()     ,   [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]    )

  set_seed(0    )
  UpperCamelCase_      :		Tuple				=      model._sample(snake_case     ,   snake_case     ,   [2]     ,   sample_length=4_0 * model.priors[2].raw_to_tokens     ,   save_results=snake_case    )
  self.assertIn(zs[2][0].detach().cpu().tolist()     ,   [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]    )

 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_      :		int				=      False
  UpperCamelCase_      :		str				=      JukeboxModel.from_pretrained(self.model_id     ,   min_duration=0    ).eval()

  UpperCamelCase_      :		Union[str, Any]				=      self.prepare_inputs()
  set_seed(0    )
  UpperCamelCase_      :		List[str]				=      [torch.zeros(1     ,   0     ,   dtype=torch.long    ) for _ in range(3    )]

  UpperCamelCase_      :		List[str]				=      model.priors[0]
  UpperCamelCase_      :		str				=      0
  UpperCamelCase_      :		List[str]				=      top_prior.get_music_tokens_conds(snake_case     ,   start=snake_case     ,   end=start + top_prior.n_ctx    )
  UpperCamelCase_      :		int				=      top_prior.get_metadata(labels[0].clone()     ,   snake_case     ,   1_0_5_8_3_0_4     ,   0    )

  self.assertIsNone(snake_case    )
  self.assertListEqual(metadata.numpy()[0][:1_0].tolist()     ,   self.EXPECTED_Y_COND    )

  UpperCamelCase_      :		Tuple				=      top_prior.get_cond(snake_case     ,   snake_case    )
  torch.testing.assert_allclose(
      audio_conditioning[0][0][:3_0].detach()     ,   torch.tensor(self.EXPECTED_AUDIO_COND    )     ,   atol=1e-4     ,   rtol=1e-4    )
  torch.testing.assert_allclose(
      metadata_conditioning[0][0][:3_0].detach()     ,   torch.tensor(self.EXPECTED_META_COND    )     ,   atol=1e-4     ,   rtol=1e-4    )
  torch.testing.assert_allclose(
      lyric_tokens[0, :3_0].detach()     ,   torch.tensor(self.EXPECTED_LYRIC_COND    )     ,   atol=1e-4     ,   rtol=1e-4    )

 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      False

  UpperCamelCase_      :		Optional[int]				=      JukeboxModel.from_pretrained(self.model_id     ,   min_duration=0    ).eval()
  set_seed(0    )
  UpperCamelCase_      :		List[Any]				=      torch.rand((1, 5_1_2_0, 1)    )
  UpperCamelCase_      :		int				=      list(self.prepare_inputs()    )

  UpperCamelCase_      :		List[str]				=      [model.vqvae.encode(snake_case     ,   start_level=2     ,   bs_chunks=waveform.shape[0]    )[0], None, None]
  UpperCamelCase_      :		Tuple				=      model._sample(
      snake_case     ,   snake_case     ,   sample_levels=[0]     ,   save_results=snake_case     ,   sample_length=4_0 * model.priors[0].raw_to_tokens    )
  torch.testing.assert_allclose(zs[0][0][:4_0]     ,   torch.tensor(self.EXPECTED_PRIMED_0    )    )

  UpperCamelCase_      :		List[str]				=      torch.cat((zs[0], torch.zeros(1     ,   2_0_4_8 - zs[0].shape[-1]    ))     ,   dim=-1    ).long()
  UpperCamelCase_      :		Tuple				=      [upper_a, model.vqvae.encode(snake_case     ,   start_level=1     ,   bs_chunks=waveform.shape[0]    )[0], None]
  UpperCamelCase_      :		List[Any]				=      model._sample(
      snake_case     ,   snake_case     ,   sample_levels=[1]     ,   save_results=snake_case     ,   sample_length=4_0 * model.priors[1].raw_to_tokens    )
  torch.testing.assert_allclose(zs[1][0][:4_0]     ,   torch.tensor(self.EXPECTED_PRIMED_1    )    )

  UpperCamelCase_      :		Dict				=      torch.cat((zs[1], torch.zeros(1     ,   2_0_4_8 - zs[1].shape[-1]    ))     ,   dim=-1    ).long()
  UpperCamelCase_      :		List[str]				=      [upper_a, upper_a, model.vqvae.encode(snake_case     ,   start_level=0     ,   bs_chunks=waveform.shape[0]    )[0]]
  UpperCamelCase_      :		Tuple				=      model._sample(
      snake_case     ,   snake_case     ,   sample_levels=[2]     ,   save_results=snake_case     ,   sample_length=4_0 * model.priors[2].raw_to_tokens    )
  torch.testing.assert_allclose(zs[2][0][:4_0].cpu()     ,   torch.tensor(self.EXPECTED_PRIMED_2    )    )


 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		List[str]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      JukeboxModel.from_pretrained(self.model_id     ,   min_duration=0    ).eval()
  set_seed(0    )
  UpperCamelCase_      :		Optional[Any]				=      torch.rand((1, 5_1_2_0, 1)    )
  with torch.no_grad():
   UpperCamelCase_      :		Union[str, Any]				=      model.vqvae.encode(snake_case     ,   start_level=2     ,   bs_chunks=x.shape[0]    )
  torch.testing.assert_allclose(zs[0][0]     ,   torch.tensor(self.EXPECTED_VQVAE_ENCODE    )    )

  with torch.no_grad():
   UpperCamelCase_      :		Dict				=      model.vqvae.decode(snake_case     ,   start_level=2     ,   bs_chunks=x.shape[0]    )
  torch.testing.assert_allclose(x[0, :4_0, 0]     ,   torch.tensor(self.EXPECTED_VQVAE_DECODE    )     ,   atol=1e-4     ,   rtol=1e-4    )





@require_torch
class 	_lowercase		(      unittest.TestCase ):
 lowercase         =     (JukeboxModel,) if is_torch_available() else ()
 lowercase         =     'openai/jukebox-5b-lyrics'
 lowercase         =     {
     'artist': 'Zac Brown Band',
     'genres': 'Country',
     'lyrics': 'I met a traveller from an antique land,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
 }

 # fmt: off
 lowercase         =     [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     1_4_8_9, 1_4_8_9, 1_4_8_9, 1_4_8_9, 1_1_5_0, 1_8_5_3, 1_5_0_9, 1_1_5_0, 1_3_5_7, 1_5_0_9, 6, 1_2_7_2
 ]
 lowercase         =     [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]

 lowercase         =     [
     1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]
 lowercase         =     [
     4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]

 lowercase         =     [
     1_7_5_5, 1_0_6_1, 2_3_4, 1_7_5_5, 1_0_6_1, 1_7_5_5, 1_8_5, 2_9_0, 3_0_7, 3_0_7, 6_1_6, 6_1_6,
     6_1_6, 6_1_6, 6_1_6, 6_1_6, 3_0_7, 2_9_0, 4_1_7, 1_7_5_5, 2_3_4, 1_7_5_5, 1_8_5, 2_9_0,
     2_9_0, 2_9_0, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0, 2_3_4, 2_3_4, 1_7_5_5,
     2_3_4, 2_3_4, 1_7_5_5, 2_3_4, 1_8_5, 1_8_5, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0,
     1_7_5_5, 1_7_5_5, 1_7_5_5, 2_3_4, 2_3_4, 1_7_5_5, 1_5_7_2, 2_9_0, 3_0_7, 6_1_6, 3_4, 6_1_6
 ]
 lowercase         =     [
     8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2, 1_8_5,
     4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2,
     1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1,
     8_9, 5_9_1, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 4_1_7, 1_3_7_2, 8_4_2, 1_3_7_2, 8_4_2, 3_4, 8_4_2,
     1_8_5, 8_9, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 6_3_2
 ]

 lowercase         =     [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]
 lowercase         =     [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 1_8_5_3, 1_1_7_7, 1_5_3_6, 1_2_2_8,
     7_1_0, 4_7_5, 1_4_8_9, 1_2_2_9, 1_2_2_4, 2_3_1, 1_2_2_4, 2_5_2, 1_4_3_4, 6_5_3, 4_7_5,
     1_1_0_6, 1_8_7_7, 1_5_9_9, 1_2_2_8, 1_6_0_0, 1_6_8_3, 1_1_8_2, 1_8_5_3, 4_7_5, 1_8_6_4,
     2_5_2, 1_2_2_9, 1_4_3_4, 2_0_0_1
 ]

 lowercase         =     [
     1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 1_1_2_5, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]
 lowercase         =     [
     4_9_1, 1_7_5_5, 3_4, 1_6_1_3, 1_7_5_5, 4_1_7, 9_9_2, 1_6_1_3, 2_2_2, 8_4_2, 1_3_5_3, 1_6_1_3,
     8_4_4, 6_3_2, 1_8_5, 1_6_1_3, 8_4_4, 6_3_2, 1_8_5, 1_6_1_3, 1_8_5, 8_4_2, 6_7_7, 1_6_1_3,
     1_8_5, 1_1_4, 1_3_5_3, 1_6_1_3, 3_0_7, 8_9, 8_4_4, 1_6_1_3, 3_0_7, 1_3_3_2, 2_3_4, 1_9_7_9,
     3_0_7, 8_9, 1_3_5_3, 6_1_6, 3_4, 8_4_2, 1_8_5, 8_4_2, 3_4, 8_4_2, 1_8_5, 8_4_2,
     3_0_7, 1_1_4, 1_8_5, 8_9, 3_4, 1_2_6_8, 1_8_5, 8_9, 3_4, 8_4_2, 1_8_5, 8_9
 ]

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							Optional[int]    )     ->		List[str]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      JukeboxTokenizer.from_pretrained(snake_case    )
  UpperCamelCase_      :		Optional[int]				=      tokenizer(**self.metas    )['input_ids']
  return tokens

 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		int				=      JukeboxModel.from_pretrained(self.model_id     ,   min_duration=0    ).eval()
  UpperCamelCase_      :		str				=      self.prepare_inputs(self.model_id    )

  set_seed(0    )
  UpperCamelCase_      :		Optional[Any]				=      [torch.zeros(1     ,   0     ,   dtype=torch.long    ).cpu() for _ in range(3    )]
  UpperCamelCase_      :		Dict				=      model._sample(snake_case     ,   snake_case     ,   [0]     ,   sample_length=6_0 * model.priors[0].raw_to_tokens     ,   save_results=snake_case    )
  self.assertIn(zs[0][0].detach().cpu().tolist()     ,   [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]    )

  set_seed(0    )
  UpperCamelCase_      :		str				=      model._sample(snake_case     ,   snake_case     ,   [1]     ,   sample_length=6_0 * model.priors[1].raw_to_tokens     ,   save_results=snake_case    )
  self.assertIn(zs[1][0].detach().cpu().tolist()     ,   [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]    )

  set_seed(0    )
  UpperCamelCase_      :		Union[str, Any]				=      model._sample(snake_case     ,   snake_case     ,   [2]     ,   sample_length=6_0 * model.priors[2].raw_to_tokens     ,   save_results=snake_case    )
  self.assertIn(zs[2][0].detach().cpu().tolist()     ,   [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]    )

 @slow
 @skip('Not enough GPU memory on CI runners'    )
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      JukeboxModel.from_pretrained(self.model_id     ,   min_duration=0    ).eval()
  UpperCamelCase_      :		List[Any]				=      [i.cuda() for i in self.prepare_inputs(self.model_id    )]

  set_seed(0    )
  model.priors[0].cuda()
  UpperCamelCase_      :		Optional[Any]				=      [torch.zeros(1     ,   0     ,   dtype=torch.long    ).cuda() for _ in range(3    )]
  UpperCamelCase_      :		Union[str, Any]				=      model._sample(snake_case     ,   snake_case     ,   [0]     ,   sample_length=6_0 * model.priors[0].raw_to_tokens     ,   save_results=snake_case    )
  torch.testing.assert_allclose(zs[0][0].cpu()     ,   torch.tensor(self.EXPECTED_GPU_OUTPUTS_2    )    )
  model.priors[0].cpu()

  set_seed(0    )
  model.priors[1].cuda()
  UpperCamelCase_      :		Any				=      model._sample(snake_case     ,   snake_case     ,   [1]     ,   sample_length=6_0 * model.priors[1].raw_to_tokens     ,   save_results=snake_case    )
  torch.testing.assert_allclose(zs[1][0].cpu()     ,   torch.tensor(self.EXPECTED_GPU_OUTPUTS_1    )    )
  model.priors[1].cpu()

  set_seed(0    )
  model.priors[2].cuda()
  UpperCamelCase_      :		str				=      model._sample(snake_case     ,   snake_case     ,   [2]     ,   sample_length=6_0 * model.priors[2].raw_to_tokens     ,   save_results=snake_case    )
  torch.testing.assert_allclose(zs[2][0].cpu()     ,   torch.tensor(self.EXPECTED_GPU_OUTPUTS_0    )    )


 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		int				=      'ArthurZ/jukebox_prior_0'
  UpperCamelCase_      :		Optional[int]				=      JukeboxPrior.from_pretrained(snake_case     ,   min_duration=0    ).eval().half().to('cuda'    )

  UpperCamelCase_      :		Optional[Any]				=      self.prepare_inputs(snake_case    )[0].cuda()
  UpperCamelCase_      :		List[Any]				=      model.get_metadata(snake_case     ,   0     ,   7_6_8_0     ,   0    )
  set_seed(0    )
  UpperCamelCase_      :		Union[str, Any]				=      model.sample(1     ,   metadata=snake_case     ,   sample_tokens=6_0    )
  self.assertIn(outputs[0].cpu().tolist()     ,   [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]    )




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a_					      = logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

a_					      = {
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a_					      = {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a_					      = sorted(arg_to_scheduler.keys())
a_					      = '{' + ', '.join(arg_to_scheduler_choices) + '}'





class 	_lowercase		(      pl.LightningModule ):

 def __init__(				self			:							List[str]     ,   snake_case			:							argparse.Namespace     ,   snake_case			:							Any=None     ,   snake_case			:							str="base"     ,   snake_case			:							Union[str, Any]=None     ,   snake_case			:							Any=None     ,   snake_case			:							str=None     ,   **snake_case			:							str     ,   )     ->		Union[str, Any]:

  """simple docstring"""





  super().__init__()
  # TODO: move to self.save_hyperparameters()
  # self.save_hyperparameters()
  # can also expand arguments into trainer signature for easier reading

  self.save_hyperparameters(snake_case    )
  UpperCamelCase_      :		Tuple				=      0
  UpperCamelCase_      :		Optional[int]				=      Path(self.hparams.output_dir    )
  UpperCamelCase_      :		Any				=      self.hparams.cache_dir if self.hparams.cache_dir else None
  if config is None:
   UpperCamelCase_      :		Optional[int]				=      AutoConfig.from_pretrained(
       self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,   **({'num_labels': num_labels} if num_labels is not None else {})     ,   cache_dir=snake_case     ,   **snake_case     ,   )
  else:
   UpperCamelCase_      :		PretrainedConfig				=      config

  UpperCamelCase_      :		Any				=      ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
  for p in extra_model_params:
   if getattr(self.hparams     ,   snake_case     ,   snake_case    ):
    assert hasattr(self.config     ,   snake_case    ), f"model config doesn't have a `{p}` attribute"
    setattr(self.config     ,   snake_case     ,   getattr(self.hparams     ,   snake_case    )    )

  if tokenizer is None:
   UpperCamelCase_      :		Union[str, Any]				=      AutoTokenizer.from_pretrained(
       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,   cache_dir=snake_case     ,   )
  else:
   UpperCamelCase_      :		PreTrainedTokenizer				=      tokenizer
  UpperCamelCase_      :		Optional[int]				=      MODEL_MODES[mode]
  if model is None:
   UpperCamelCase_      :		Tuple				=      self.model_type.from_pretrained(
       self.hparams.model_name_or_path     ,   from_tf=bool('.ckpt' in self.hparams.model_name_or_path    )     ,   config=self.config     ,   cache_dir=snake_case     ,   )
  else:
   UpperCamelCase_      :		Optional[int]				=      model

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   *snake_case			:							Dict     ,   **snake_case			:							List[str]    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      self.model_type.from_pretrained(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      arg_to_scheduler[self.hparams.lr_scheduler]
  UpperCamelCase_      :		List[str]				=      get_schedule_func(
      self.opt     ,   num_warmup_steps=self.hparams.warmup_steps     ,   num_training_steps=self.total_steps()    )
  UpperCamelCase_      :		Optional[Any]				=      {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
  return scheduler

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      self.model
  UpperCamelCase_      :		int				=      ['bias', 'LayerNorm.weight']
  UpperCamelCase_      :		Optional[int]				=      [
      {
          'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )],
          'weight_decay': self.hparams.weight_decay,
      },
      {
          'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay    )],
          'weight_decay': 0.0,
      },
  ]
  if self.hparams.adafactor:
   UpperCamelCase_      :		Union[str, Any]				=      Adafactor(
       snake_case     ,   lr=self.hparams.learning_rate     ,   scale_parameter=snake_case     ,   relative_step=snake_case    )

  else:
   UpperCamelCase_      :		List[Any]				=      AdamW(
       snake_case     ,   lr=self.hparams.learning_rate     ,   eps=self.hparams.adam_epsilon    )
  UpperCamelCase_      :		List[str]				=      optimizer

  UpperCamelCase_      :		Optional[int]				=      self.get_lr_scheduler()

  return [optimizer], [scheduler]

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							Tuple     ,   snake_case			:							Union[str, Any]    )     ->		Optional[int]:

  """simple docstring"""





  return self.validation_step(snake_case     ,   snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   snake_case			:							str    )     ->		Any:

  """simple docstring"""





  return self.validation_end(snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      max(1     ,   self.hparams.gpus    )  # TODO: consider num_tpu_cores
  UpperCamelCase_      :		List[str]				=      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
  return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   snake_case			:							List[str]    )     ->		Union[str, Any]:

  """simple docstring"""





  if mode == "test":
   UpperCamelCase_      :		List[str]				=      len(self.test_dataloader().dataset    )
  else:
   UpperCamelCase_      :		Optional[Any]				=      self.get_dataloader('train'     ,   self.hparams.train_batch_size     ,   shuffle=snake_case    )
   UpperCamelCase_      :		int				=      len(self.train_dataloader().dataset    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   snake_case			:							str     ,   snake_case			:							int     ,   snake_case			:							bool = False    )     ->		Optional[int]:

  """simple docstring"""





  raise NotImplementedError('You must implement this for your task'    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		Optional[Any]:

  """simple docstring"""





  return self.train_loader

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		Optional[Any]:

  """simple docstring"""





  return self.get_dataloader('dev'     ,   self.hparams.eval_batch_size     ,   shuffle=snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Any:

  """simple docstring"""





  return self.get_dataloader('test'     ,   self.hparams.eval_batch_size     ,   shuffle=snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]     ,   snake_case			:							Dict    )     ->		str:

  """simple docstring"""





  return os.path.join(
      self.hparams.data_dir     ,   'cached_{}_{}_{}'.format(
          snake_case     ,   list(filter(snake_case     ,   self.hparams.model_name_or_path.split('/'    )    )    ).pop()     ,   str(self.hparams.max_seq_length    )     ,   )     ,   )

 @pl.utilities.rank_zero_only
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							Dict[str, Any]    )     ->		None:

  """simple docstring"""





  UpperCamelCase_      :		str				=      self.output_dir.joinpath('best_tfmr'    )
  UpperCamelCase_      :		Dict				=      self.step_count
  self.model.save_pretrained(snake_case    )
  self.tokenizer.save_pretrained(snake_case    )


 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				snake_case			:							Dict     ,   snake_case			:							Dict    )     ->		Any:

  """simple docstring"""





  parser.add_argument(
      '--model_name_or_path'     ,   default=snake_case     ,   type=snake_case     ,   required=snake_case     ,   help='Path to pretrained model or model identifier from huggingface.co/models'     ,   )
  parser.add_argument(
      '--config_name'     ,   default=''     ,   type=snake_case     ,   help='Pretrained config name or path if not the same as model_name'    )
  parser.add_argument(
      '--tokenizer_name'     ,   default=snake_case     ,   type=snake_case     ,   help='Pretrained tokenizer name or path if not the same as model_name'     ,   )
  parser.add_argument(
      '--cache_dir'     ,   default=''     ,   type=snake_case     ,   help='Where do you want to store the pre-trained models downloaded from huggingface.co'     ,   )
  parser.add_argument(
      '--encoder_layerdrop'     ,   type=snake_case     ,   help='Encoder layer dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument(
      '--decoder_layerdrop'     ,   type=snake_case     ,   help='Decoder layer dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument(
      '--dropout'     ,   type=snake_case     ,   help='Dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument(
      '--attention_dropout'     ,   type=snake_case     ,   help='Attention dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument('--learning_rate'     ,   default=5e-5     ,   type=snake_case     ,   help='The initial learning rate for Adam.'    )
  parser.add_argument(
      '--lr_scheduler'     ,   default='linear'     ,   choices=snake_case     ,   metavar=snake_case     ,   type=snake_case     ,   help='Learning rate scheduler'     ,   )
  parser.add_argument('--weight_decay'     ,   default=0.0     ,   type=snake_case     ,   help='Weight decay if we apply some.'    )
  parser.add_argument('--adam_epsilon'     ,   default=1e-8     ,   type=snake_case     ,   help='Epsilon for Adam optimizer.'    )
  parser.add_argument('--warmup_steps'     ,   default=0     ,   type=snake_case     ,   help='Linear warmup over warmup_steps.'    )
  parser.add_argument('--num_workers'     ,   default=4     ,   type=snake_case     ,   help='kwarg passed to DataLoader'    )
  parser.add_argument('--num_train_epochs'     ,   dest='max_epochs'     ,   default=3     ,   type=snake_case    )
  parser.add_argument('--train_batch_size'     ,   default=3_2     ,   type=snake_case    )
  parser.add_argument('--eval_batch_size'     ,   default=3_2     ,   type=snake_case    )
  parser.add_argument('--adafactor'     ,   action='store_true'    )





class 	_lowercase		(      pl.Callback ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							Any    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      trainer.lr_schedulers[0]['scheduler']
  UpperCamelCase_      :		int				=      {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
  pl_module.logger.log_metrics(snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							pl.Trainer     ,   snake_case			:							pl.LightningModule    )     ->		List[Any]:

  """simple docstring"""





  rank_zero_info('***** Validation results *****'    )
  UpperCamelCase_      :		Any				=      trainer.callback_metrics
  # Log results
  for key in sorted(snake_case    ):
   if key not in ["log", "progress_bar"]:
    rank_zero_info('{} = {}\n'.format(snake_case     ,   str(metrics[key]    )    )    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							pl.Trainer     ,   snake_case			:							pl.LightningModule    )     ->		int:

  """simple docstring"""





  rank_zero_info('***** Test results *****'    )
  UpperCamelCase_      :		Optional[int]				=      trainer.callback_metrics
  # Log and save results to file
  UpperCamelCase_      :		Union[str, Any]				=      os.path.join(pl_module.hparams.output_dir     ,   'test_results.txt'    )
  with open(snake_case     ,   'w'    ) as writer:
   for key in sorted(snake_case    ):
    if key not in ["log", "progress_bar"]:
     rank_zero_info('{} = {}\n'.format(snake_case     ,   str(metrics[key]    )    )    )
     writer.write('{} = {}\n'.format(snake_case     ,   str(metrics[key]    )    )    )





def      __lowercase  (		lowerCamelCase      :  Optional[Any]						,						lowerCamelCase      :  Any			):
 #  To allow all pl args uncomment the following line
 #  parser = pl.Trainer.add_argparse_args(parser)
 parser.add_argument(
     '--output_dir'						,						default=lowerCamelCase						,						type=lowerCamelCase						,						required=lowerCamelCase						,						help='The output directory where the model predictions and checkpoints will be written.'						,						)
 parser.add_argument(
     '--fp16'						,						action='store_true'						,						help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'						,						)

 parser.add_argument(
     '--fp16_opt_level'						,						type=lowerCamelCase						,						default='O2'						,						help=(
         'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
         'See details at https://nvidia.github.io/apex/amp.html'
     )						,						)
 parser.add_argument('--n_tpu_cores'						,						dest='tpu_cores'						,						type=lowerCamelCase			)
 parser.add_argument('--max_grad_norm'						,						dest='gradient_clip_val'						,						default=1.0						,						type=lowerCamelCase						,						help='Max gradient norm'			)
 parser.add_argument('--do_train'						,						action='store_true'						,						help='Whether to run training.'			)
 parser.add_argument('--do_predict'						,						action='store_true'						,						help='Whether to run predictions on the test set.'			)
 parser.add_argument(
     '--gradient_accumulation_steps'						,						dest='accumulate_grad_batches'						,						type=lowerCamelCase						,						default=1						,						help='Number of updates steps to accumulate before performing a backward/update pass.'						,						)
 parser.add_argument('--seed'						,						type=lowerCamelCase						,						default=42						,						help='random seed for initialization'			)
 parser.add_argument(
     '--data_dir'						,						default=lowerCamelCase						,						type=lowerCamelCase						,						required=lowerCamelCase						,						help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'						,						)






def      __lowercase  (		lowerCamelCase      :  BaseTransformer						,						lowerCamelCase      :  argparse.Namespace						,						lowerCamelCase      :  int=None						,						lowerCamelCase      :  Tuple=True						,						lowerCamelCase      :  Union[str, Any]=[]						,						lowerCamelCase      :  Any=None						,						lowerCamelCase      :  Optional[Any]=None						,						**lowerCamelCase      :  int						,						):
 pl.seed_everything(args.seed			)

 # init model
 UpperCamelCase_      :		int				=      Path(model.hparams.output_dir			)
 odir.mkdir(exist_ok=lowerCamelCase			)

 # add custom checkpoints
 if checkpoint_callback is None:
  UpperCamelCase_      :		Any				=      pl.callbacks.ModelCheckpoint(
      filepath=args.output_dir						,						prefix='checkpoint'						,						monitor='val_loss'						,						mode='min'						,						save_top_k=1			)
 if early_stopping_callback:
  extra_callbacks.append(lowerCamelCase			)
 if logging_callback is None:
  UpperCamelCase_      :		str				=      LoggingCallback()

 UpperCamelCase_      :		Union[str, Any]				=      {}

 # TODO: remove with PyTorch 1.6 since pl uses native amp
 if args.fpaa:
  UpperCamelCase_      :		Optional[Any]				=      16
  UpperCamelCase_      :		Optional[int]				=      args.fpaa_opt_level

 if args.gpus > 1:
  UpperCamelCase_      :		Optional[int]				=      'ddp'

 UpperCamelCase_      :		Optional[int]				=      args.accumulate_grad_batches
 UpperCamelCase_      :		str				=      extra_train_kwargs.get('accelerator'						,						lowerCamelCase			)
 UpperCamelCase_      :		List[Any]				=      extra_train_kwargs.get('profiler'						,						lowerCamelCase			)

 UpperCamelCase_      :		Optional[Any]				=      pl.Trainer.from_argparse_args(
     lowerCamelCase						,						weights_summary=lowerCamelCase						,						callbacks=[logging_callback] + extra_callbacks						,						logger=lowerCamelCase						,						checkpoint_callback=lowerCamelCase						,						**lowerCamelCase						,						)

 if args.do_train:
  trainer.fit(lowerCamelCase			)

 return trainer



