





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 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		(JukeboxModel,) if is_torch_available() else ()
	__UpperCamelCase        =		'''openai/jukebox-1b-lyrics'''
	__UpperCamelCase        =		{
	    '''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
    ''',
	}
	# fmt: off
	__UpperCamelCase        =		[
	    18_64, 15_36, 12_13, 18_70, 13_57, 15_36, 5_19, 8_80, 13_23, 7_89, 10_82, 5_34,
	    10_00, 14_45, 11_05, 11_30, 9_67, 5_15, 14_34, 16_20, 5_34, 14_95, 2_83, 14_45,
	    3_33, 13_07, 5_39, 16_31, 15_28, 3_75, 14_34, 6_73, 6_27, 7_10, 7_78, 18_83,
	    14_05, 12_76, 14_55, 12_28
	]

	__UpperCamelCase        =		[
	    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53
	]

	__UpperCamelCase        =		[
	    11_25, 17_51, 6_97, 17_76, 11_41, 14_76, 3_91, 6_97, 11_25, 6_84, 8_67, 4_16,
	    8_44, 13_72, 12_74, 7_17, 12_74, 8_44, 12_99, 14_19, 6_97, 13_70, 3_17, 11_25,
	    1_91, 14_40, 13_70, 14_40, 13_70, 2_82, 16_21, 13_70, 3_68, 3_49, 8_67, 18_72,
	    12_62, 8_69, 17_28, 7_47
	]
	__UpperCamelCase        =		[
	    4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16
	]

	__UpperCamelCase        =		[
	    17_55, 8_42, 3_07, 18_43, 10_22, 13_95, 2_34, 15_54, 8_06, 7_39, 10_22, 4_42,
	    6_16, 5_56, 2_68, 14_99, 9_33, 4_57, 14_40, 18_37, 7_55, 9_85, 3_08, 9_02,
	    2_93, 14_43, 16_71, 11_41, 15_33, 5_55, 15_62, 10_61, 2_87, 4_17, 10_22, 20_08,
	    11_86, 10_15, 17_77, 2_68
	]
	__UpperCamelCase        =		[
	    8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42,
	    1_85, 4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 3_07, 8_42,
	    5_91, 8_42, 13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42,
	    1_85, 8_42, 5_91, 89
	]

	__UpperCamelCase        =		[1_05_83_04, 0, 78_64_32, 71_69, 5_07, 76, 27, 40, 30, 76]

	__UpperCamelCase        =		[
	    3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
	    10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
	    15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
	    19_07, 17_88, 5_96, 16_26
	]
	__UpperCamelCase        =		[
	    12_36, 16_68, 14_84, 19_20, 18_48, 14_09, 1_39, 8_64, 18_28, 12_72, 15_99, 8_24,
	    16_72, 1_39, 5_55, 14_84, 8_24, 19_20, 5_55, 5_96, 15_79, 15_99, 12_31, 15_99,
	    16_37, 14_07, 2_12, 8_24, 15_99, 1_16, 14_33, 8_24, 2_58, 15_99, 14_33, 18_95,
	    10_63, 14_33, 14_33, 15_99
	]
	__UpperCamelCase        =		[
	    16_84, 18_73, 11_19, 11_89, 3_95, 6_11, 19_01, 9_72, 8_90, 13_37, 13_92, 19_27,
	    96, 9_72, 6_72, 7_80, 11_19, 8_90, 1_58, 7_71, 10_73, 19_27, 3_53, 13_31,
	    12_69, 14_59, 13_33, 16_45, 8_12, 15_77, 13_37, 6_06, 3_53, 9_81, 14_66, 6_19,
	    1_97, 3_91, 3_02, 19_30
	]
	__UpperCamelCase        =		[
	    3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
	    10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
	    15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
	    19_07, 17_88, 5_96, 16_26
	]
	__UpperCamelCase        =		[
	    -0.0_4_9_2, -0.0_5_2_4, -0.0_5_6_5, -0.0_6_4_0, -0.0_6_8_6, -0.0_6_8_4, -0.0_6_7_7, -0.0_6_6_4,
	    -0.0_6_0_5, -0.0_4_9_0, -0.0_3_3_0, -0.0_1_6_8, -0.0_0_8_3, -0.0_0_7_5, -0.0_0_5_1, 0.0_0_2_5,
	    0.0_1_3_6, 0.0_2_6_1, 0.0_3_8_6, 0.0_4_9_7, 0.0_5_8_0, 0.0_5_9_9, 0.0_5_8_3, 0.0_6_1_4,
	    0.0_7_4_0, 0.0_8_8_9, 0.1_0_2_3, 0.1_1_6_2, 0.1_2_1_1, 0.1_2_1_2, 0.1_2_5_1, 0.1_3_3_6,
	    0.1_5_0_2, 0.1_6_8_6, 0.1_8_8_3, 0.2_1_4_8, 0.2_3_6_3, 0.2_4_5_8, 0.2_5_0_7, 0.2_5_3_1
	]
	__UpperCamelCase        =		[
	    0.0_2_5_6, -0.0_5_4_4, 0.1_6_0_0, -0.0_0_3_2, 0.1_0_6_6, 0.0_8_2_5, -0.0_0_1_3, 0.3_4_4_0,
	    0.0_2_1_0, 0.0_4_1_2, -0.1_7_7_7, -0.0_8_9_2, -0.0_1_6_4, 0.0_2_8_5, -0.0_6_1_3, -0.0_6_1_7,
	    -0.0_1_3_7, -0.0_2_0_1, -0.0_1_7_5, 0.0_2_1_5, -0.0_6_2_7, 0.0_5_2_0, -0.0_7_3_0, 0.0_9_7_0,
	    -0.0_1_0_0, 0.0_4_4_2, -0.0_5_8_6, 0.0_2_0_7, -0.0_0_1_5, -0.0_0_8_2
	]
	__UpperCamelCase        =		[
	    0.0_4_1_5, 0.0_8_7_7, 0.0_0_2_2, -0.0_0_5_5, 0.0_7_5_1, 0.0_3_3_4, 0.0_3_2_4, -0.0_0_6_8,
	    0.0_0_1_1, 0.0_0_1_7, -0.0_6_7_6, 0.0_6_5_5, -0.0_1_4_3, 0.0_3_9_9, 0.0_3_0_3, 0.0_7_4_3,
	    -0.0_1_6_8, -0.0_3_9_4, -0.1_1_1_3, 0.0_1_2_4, 0.0_4_4_2, 0.0_2_6_7, -0.0_0_0_3, -0.1_5_3_6,
	    -0.0_1_1_6, -0.1_8_3_7, -0.0_1_8_0, -0.1_0_2_6, -0.0_7_7_7, -0.0_4_5_6
	]
	__UpperCamelCase        =		[
	    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
	]




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

			'''simple docstring'''




			__lowercase					=    JukeboxTokenizer.from_pretrained(self.model_id      )
			__lowercase					=    tokenizer(**self.metas      )['input_ids']
			return tokens




	@slow
	def       UpperCAmelCase (	self       :    Any      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    JukeboxModel.from_pretrained(self.model_id				,			min_duration=0      ).eval()
			__lowercase					=    self.prepare_inputs()

			set_seed(0      )
			__lowercase					=    [torch.zeros(1				,			0				,			dtype=torch.long      ).cpu() for _ in range(3      )]
			__lowercase					=    model._sample(__lowerCamelCase				,			__lowerCamelCase				,			[0]				,			sample_length=40 * model.priors[0].raw_to_tokens				,			save_results=__lowerCamelCase      )
			self.assertIn(zs[0][0].detach().cpu().tolist()				,			[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]      )

			set_seed(0      )
			__lowercase					=    model._sample(__lowerCamelCase				,			__lowerCamelCase				,			[1]				,			sample_length=40 * model.priors[1].raw_to_tokens				,			save_results=__lowerCamelCase      )
			self.assertIn(zs[1][0].detach().cpu().tolist()				,			[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]      )

			set_seed(0      )
			__lowercase					=    model._sample(__lowerCamelCase				,			__lowerCamelCase				,			[2]				,			sample_length=40 * model.priors[2].raw_to_tokens				,			save_results=__lowerCamelCase      )
			self.assertIn(zs[2][0].detach().cpu().tolist()				,			[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]      )




	@slow
	def       UpperCAmelCase (	self       :    Any      )					->   int:

			'''simple docstring'''




			__lowercase					=    False
			__lowercase					=    JukeboxModel.from_pretrained(self.model_id				,			min_duration=0      ).eval()

			__lowercase					=    self.prepare_inputs()
			set_seed(0      )
			__lowercase					=    [torch.zeros(1				,			0				,			dtype=torch.long      ) for _ in range(3      )]

			__lowercase					=    model.priors[0]
			__lowercase					=    0
			__lowercase					=    top_prior.get_music_tokens_conds(__lowerCamelCase				,			start=__lowerCamelCase				,			end=start + top_prior.n_ctx      )
			__lowercase					=    top_prior.get_metadata(labels[0].clone()				,			__lowerCamelCase				,			1_058_304				,			0      )

			self.assertIsNone(__lowerCamelCase      )
			self.assertListEqual(metadata.numpy()[0][:10].tolist()				,			self.EXPECTED_Y_COND      )

			__lowercase	,			__lowercase	,			__lowercase					=    top_prior.get_cond(__lowerCamelCase				,			__lowerCamelCase      )
			torch.testing.assert_allclose(
			    audio_conditioning[0][0][:30].detach()				,			torch.tensor(self.EXPECTED_AUDIO_COND      )				,			atol=1E-4				,			rtol=1E-4      )
			torch.testing.assert_allclose(
			    metadata_conditioning[0][0][:30].detach()				,			torch.tensor(self.EXPECTED_META_COND      )				,			atol=1E-4				,			rtol=1E-4      )
			torch.testing.assert_allclose(
			    lyric_tokens[0, :30].detach()				,			torch.tensor(self.EXPECTED_LYRIC_COND      )				,			atol=1E-4				,			rtol=1E-4      )




	@slow
	def       UpperCAmelCase (	self       :    Dict      )					->   int:

			'''simple docstring'''




			__lowercase					=    False

			__lowercase					=    JukeboxModel.from_pretrained(self.model_id				,			min_duration=0      ).eval()
			set_seed(0      )
			__lowercase					=    torch.rand((1, 5_120, 1)      )
			__lowercase					=    list(self.prepare_inputs()      )

			__lowercase					=    [model.vqvae.encode(__lowerCamelCase				,			start_level=2				,			bs_chunks=waveform.shape[0]      )[0], None, None]
			__lowercase					=    model._sample(
			    __lowerCamelCase				,			__lowerCamelCase				,			sample_levels=[0]				,			save_results=__lowerCamelCase				,			sample_length=40 * model.priors[0].raw_to_tokens      )
			torch.testing.assert_allclose(zs[0][0][:40]				,			torch.tensor(self.EXPECTED_PRIMED_0      )      )

			__lowercase					=    torch.cat((zs[0], torch.zeros(1				,			2_048 - zs[0].shape[-1]      ))				,			dim=-1      ).long()
			__lowercase					=    [upper_a, model.vqvae.encode(__lowerCamelCase				,			start_level=1				,			bs_chunks=waveform.shape[0]      )[0], None]
			__lowercase					=    model._sample(
			    __lowerCamelCase				,			__lowerCamelCase				,			sample_levels=[1]				,			save_results=__lowerCamelCase				,			sample_length=40 * model.priors[1].raw_to_tokens      )
			torch.testing.assert_allclose(zs[1][0][:40]				,			torch.tensor(self.EXPECTED_PRIMED_1      )      )

			__lowercase					=    torch.cat((zs[1], torch.zeros(1				,			2_048 - zs[1].shape[-1]      ))				,			dim=-1      ).long()
			__lowercase					=    [upper_a, upper_a, model.vqvae.encode(__lowerCamelCase				,			start_level=0				,			bs_chunks=waveform.shape[0]      )[0]]
			__lowercase					=    model._sample(
			    __lowerCamelCase				,			__lowerCamelCase				,			sample_levels=[2]				,			save_results=__lowerCamelCase				,			sample_length=40 * model.priors[2].raw_to_tokens      )
			torch.testing.assert_allclose(zs[2][0][:40].cpu()				,			torch.tensor(self.EXPECTED_PRIMED_2      )      )






	@slow
	def       UpperCAmelCase (	self       :    List[Any]      )					->   Optional[int]:

			'''simple docstring'''




			__lowercase					=    JukeboxModel.from_pretrained(self.model_id				,			min_duration=0      ).eval()
			set_seed(0      )
			__lowercase					=    torch.rand((1, 5_120, 1)      )
			with torch.no_grad():
					__lowercase					=    model.vqvae.encode(__lowerCamelCase				,			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():
					__lowercase					=    model.vqvae.decode(__lowerCamelCase				,			start_level=2				,			bs_chunks=x.shape[0]      )
			torch.testing.assert_allclose(x[0, :40, 0]				,			torch.tensor(self.EXPECTED_VQVAE_DECODE      )				,			atol=1E-4				,			rtol=1E-4      )





@require_torch
class 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		(JukeboxModel,) if is_torch_available() else ()
	__UpperCamelCase        =		'''openai/jukebox-5b-lyrics'''
	__UpperCamelCase        =		{
	    '''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
    ''',
	}

	# fmt: off
	__UpperCamelCase        =		[
	    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    14_89, 14_89, 14_89, 14_89, 11_50, 18_53, 15_09, 11_50, 13_57, 15_09, 6, 12_72
	]
	__UpperCamelCase        =		[
	    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
	]

	__UpperCamelCase        =		[
	    11_25, 4_16, 11_25, 11_25, 11_25, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
	]
	__UpperCamelCase        =		[
	    4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
	]

	__UpperCamelCase        =		[
	    17_55, 10_61, 2_34, 17_55, 10_61, 17_55, 1_85, 2_90, 3_07, 3_07, 6_16, 6_16,
	    6_16, 6_16, 6_16, 6_16, 3_07, 2_90, 4_17, 17_55, 2_34, 17_55, 1_85, 2_90,
	    2_90, 2_90, 3_07, 6_16, 6_16, 6_16, 6_16, 6_16, 2_90, 2_34, 2_34, 17_55,
	    2_34, 2_34, 17_55, 2_34, 1_85, 1_85, 3_07, 6_16, 6_16, 6_16, 6_16, 2_90,
	    17_55, 17_55, 17_55, 2_34, 2_34, 17_55, 15_72, 2_90, 3_07, 6_16, 34, 6_16
	]
	__UpperCamelCase        =		[
	    8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42, 1_85,
	    4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 1_85, 8_42, 5_91, 8_42,
	    13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42, 1_85, 8_42, 5_91,
	    89, 5_91, 8_42, 5_91, 8_42, 5_91, 4_17, 13_72, 8_42, 13_72, 8_42, 34, 8_42,
	    1_85, 89, 5_91, 8_42, 1_85, 8_42, 5_91, 6_32
	]

	__UpperCamelCase        =		[
	    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
	]
	__UpperCamelCase        =		[
	    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
	    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 18_53, 11_77, 15_36, 12_28,
	    7_10, 4_75, 14_89, 12_29, 12_24, 2_31, 12_24, 2_52, 14_34, 6_53, 4_75,
	    11_06, 18_77, 15_99, 12_28, 16_00, 16_83, 11_82, 18_53, 4_75, 18_64,
	    2_52, 12_29, 14_34, 20_01
	]

	__UpperCamelCase        =		[
	    11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 4_16, 11_25, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
	    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
	]
	__UpperCamelCase        =		[
	    4_91, 17_55, 34, 16_13, 17_55, 4_17, 9_92, 16_13, 2_22, 8_42, 13_53, 16_13,
	    8_44, 6_32, 1_85, 16_13, 8_44, 6_32, 1_85, 16_13, 1_85, 8_42, 6_77, 16_13,
	    1_85, 1_14, 13_53, 16_13, 3_07, 89, 8_44, 16_13, 3_07, 13_32, 2_34, 19_79,
	    3_07, 89, 13_53, 6_16, 34, 8_42, 1_85, 8_42, 34, 8_42, 1_85, 8_42,
	    3_07, 1_14, 1_85, 89, 34, 12_68, 1_85, 89, 34, 8_42, 1_85, 89
	]




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    List[Any]      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    JukeboxTokenizer.from_pretrained(__lowerCamelCase      )
			__lowercase					=    tokenizer(**self.metas      )['input_ids']
			return tokens




	@slow
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			__lowercase					=    JukeboxModel.from_pretrained(self.model_id				,			min_duration=0      ).eval()
			__lowercase					=    self.prepare_inputs(self.model_id      )

			set_seed(0      )
			__lowercase					=    [torch.zeros(1				,			0				,			dtype=torch.long      ).cpu() for _ in range(3      )]
			__lowercase					=    model._sample(__lowerCamelCase				,			__lowerCamelCase				,			[0]				,			sample_length=60 * model.priors[0].raw_to_tokens				,			save_results=__lowerCamelCase      )
			self.assertIn(zs[0][0].detach().cpu().tolist()				,			[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]      )

			set_seed(0      )
			__lowercase					=    model._sample(__lowerCamelCase				,			__lowerCamelCase				,			[1]				,			sample_length=60 * model.priors[1].raw_to_tokens				,			save_results=__lowerCamelCase      )
			self.assertIn(zs[1][0].detach().cpu().tolist()				,			[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]      )

			set_seed(0      )
			__lowercase					=    model._sample(__lowerCamelCase				,			__lowerCamelCase				,			[2]				,			sample_length=60 * model.priors[2].raw_to_tokens				,			save_results=__lowerCamelCase      )
			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       UpperCAmelCase (	self       :    List[Any]      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    JukeboxModel.from_pretrained(self.model_id				,			min_duration=0      ).eval()
			__lowercase					=    [i.cuda() for i in self.prepare_inputs(self.model_id      )]

			set_seed(0      )
			model.priors[0].cuda()
			__lowercase					=    [torch.zeros(1				,			0				,			dtype=torch.long      ).cuda() for _ in range(3      )]
			__lowercase					=    model._sample(__lowerCamelCase				,			__lowerCamelCase				,			[0]				,			sample_length=60 * model.priors[0].raw_to_tokens				,			save_results=__lowerCamelCase      )
			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()
			__lowercase					=    model._sample(__lowerCamelCase				,			__lowerCamelCase				,			[1]				,			sample_length=60 * model.priors[1].raw_to_tokens				,			save_results=__lowerCamelCase      )
			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()
			__lowercase					=    model._sample(__lowerCamelCase				,			__lowerCamelCase				,			[2]				,			sample_length=60 * model.priors[2].raw_to_tokens				,			save_results=__lowerCamelCase      )
			torch.testing.assert_allclose(zs[2][0].cpu()				,			torch.tensor(self.EXPECTED_GPU_OUTPUTS_0      )      )






	@slow
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			__lowercase					=    'ArthurZ/jukebox_prior_0'
			__lowercase					=    JukeboxPrior.from_pretrained(__lowerCamelCase				,			min_duration=0      ).eval().half().to('cuda'      )

			__lowercase					=    self.prepare_inputs(__lowerCamelCase      )[0].cuda()
			__lowercase					=    model.get_metadata(__lowerCamelCase				,			0				,			7_680				,			0      )
			set_seed(0      )
			__lowercase					=    model.sample(1				,			metadata=__lowerCamelCase				,			sample_tokens=60      )
			self.assertIn(outputs[0].cpu().tolist()				,			[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]      )











# Usage:
# ./gen-card-allenai-wmt19.py

import os
from pathlib import Path



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						Optional[Any]:
		__lowercase					=    {
		    'en': 'Machine learning is great, isn\'t it?',
		    'ru': 'Машинное обучение - это здорово, не так ли?',
		    'de': 'Maschinelles Lernen ist großartig, nicht wahr?',
		}

		# BLUE scores as follows:
		# "pair": [fairseq, transformers]
		__lowercase					=    {
		    'wmt19-de-en-6-6-base': [0, 38.37],
		    'wmt19-de-en-6-6-big': [0, 39.90],
		}
		__lowercase					=    F"{src_lang}-{tgt_lang}"

		__lowercase					=    F"\n---\n\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\n2 models are available:\n\n* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)\n* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   |  transformers\n-------|---------\n{model_name}  |  {scores[model_name][1]}\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"
		model_card_dir.mkdir(parents=snake_case	,      exist_ok=snake_case       )
		__lowercase					=    os.path.join(snake_case	,      'README.md'       )
		print(F"Generating {path}"       )
		with open(snake_case	,      'w'	,      encoding='utf-8'       ) as f:
				f.write(snake_case       )


# make sure we are under the root of the project
SCREAMING_SNAKE_CASE_      :			Tuple		  =      Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_      :			Any		  =      repo_dir / '''model_cards'''

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
		SCREAMING_SNAKE_CASE_      :			Any		  =      model_cards_dir / '''allenai''' / model_name
		write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)




