



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


A__  :  str       = logging.get_logger(__name__)

A__  :  List[str]       = {
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}



class     __snake_case						(					UpperCamelCase_						):
							_a      				=			'''mbart'''
							_a      				=			['''past_key_values''']
							_a      				=			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




							def __init__(	self					:	Optional[int]    ,    A_					:	Dict=5_0_2_6_5    ,    A_					:	Dict=1_0_2_4    ,    A_					:	Optional[int]=1_2    ,    A_					:	List[Any]=4_0_9_6    ,    A_					:	Optional[int]=1_6    ,    A_					:	List[str]=1_2    ,    A_					:	str=4_0_9_6    ,    A_					:	List[str]=1_6    ,    A_					:	List[Any]=0.0    ,    A_					:	List[str]=0.0    ,    A_					:	Tuple=True    ,    A_					:	Optional[Any]=True    ,    A_					:	List[Any]="gelu"    ,    A_					:	List[str]=1_0_2_4    ,    A_					:	Optional[int]=0.1    ,    A_					:	Union[str, Any]=0.0    ,    A_					:	Optional[Any]=0.0    ,    A_					:	List[Any]=0.02    ,    A_					:	Tuple=0.0    ,    A_					:	int=False    ,    A_					:	str=1    ,    A_					:	Optional[int]=0    ,    A_					:	str=2    ,    A_					:	int=2    ,    **A_					:	Tuple    ,    ):
									lowerCAmelCase_      :					int     =			vocab_size
									lowerCAmelCase_      :					List[Any]     =			max_position_embeddings
									lowerCAmelCase_      :					Tuple     =			d_model
									lowerCAmelCase_      :					Optional[Any]     =			encoder_ffn_dim
									lowerCAmelCase_      :					Dict     =			encoder_layers
									lowerCAmelCase_      :					int     =			encoder_attention_heads
									lowerCAmelCase_      :					int     =			decoder_ffn_dim
									lowerCAmelCase_      :					Optional[int]     =			decoder_layers
									lowerCAmelCase_      :					int     =			decoder_attention_heads
									lowerCAmelCase_      :					Optional[int]     =			dropout
									lowerCAmelCase_      :					Optional[Any]     =			attention_dropout
									lowerCAmelCase_      :					str     =			activation_dropout
									lowerCAmelCase_      :					int     =			activation_function
									lowerCAmelCase_      :					Any     =			init_std
									lowerCAmelCase_      :					Tuple     =			encoder_layerdrop
									lowerCAmelCase_      :					Any     =			decoder_layerdrop
									lowerCAmelCase_      :					List[str]     =			classifier_dropout
									lowerCAmelCase_      :					Any     =			use_cache
									lowerCAmelCase_      :					List[str]     =			encoder_layers
									lowerCAmelCase_      :					List[str]     =			scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=A_    ,    bos_token_id=A_    ,    eos_token_id=A_    ,    is_encoder_decoder=A_    ,    forced_eos_token_id=A_    ,    **A_    ,    )





class     __snake_case						(					UpperCamelCase_						):




							@property
							def        UpperCAmelCase__      (	self					:	str):
									if self.task in ["default", "seq2seq-lm"]:
											lowerCAmelCase_      :					List[Any]     =			OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											    ])

											if self.use_past:
													lowerCAmelCase_      :					List[Any]     =			{0: '''batch'''}
													lowerCAmelCase_      :					Optional[Any]     =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
													lowerCAmelCase_      :					List[str]     =			{0: '''batch''', 1: '''decoder_sequence'''}
													lowerCAmelCase_      :					Optional[int]     =			{0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
													self.fill_with_past_key_values_(A_    ,    direction='''inputs''')
									elif self.task == "causal-lm":
											# TODO: figure this case out.
											lowerCAmelCase_      :					List[str]     =			OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											    ])
											if self.use_past:
													lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[Any]     =			self.num_layers
													for i in range(A_):
															lowerCAmelCase_      :					str     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
															lowerCAmelCase_      :					List[Any]     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
									else:
											lowerCAmelCase_      :					List[Any]     =			OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
											        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
											    ])

									return common_inputs




							@property
							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									if self.task in ["default", "seq2seq-lm"]:
											lowerCAmelCase_      :					Any     =			super().outputs
									else:
											lowerCAmelCase_      :					Tuple     =			super(A_    ,    self).outputs
											if self.use_past:
													lowerCAmelCase_ ,  lowerCAmelCase_      :					str     =			self.num_layers
													for i in range(A_):
															lowerCAmelCase_      :					Union[str, Any]     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
															lowerCAmelCase_      :					str     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
									return common_outputs




							def        UpperCAmelCase__      (	self					:	Optional[int]    ,    A_					:	PreTrainedTokenizer    ,    A_					:	int = -1    ,    A_					:	int = -1    ,    A_					:	bool = False    ,    A_					:	Optional[TensorType] = None    ,    ):
									lowerCAmelCase_      :					int     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    A_    ,    A_    ,    A_    ,    A_    ,    A_)

									# Generate decoder inputs
									lowerCAmelCase_      :					Tuple     =			seq_length if not self.use_past else 1
									lowerCAmelCase_      :					str     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    A_    ,    A_    ,    A_    ,    A_    ,    A_)
									lowerCAmelCase_      :					Optional[int]     =			{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									lowerCAmelCase_      :					Dict     =			dict(**A_    ,    **A_)

									if self.use_past:
											if not is_torch_available():
													raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
											else:
													import torch
											lowerCAmelCase_ ,  lowerCAmelCase_      :					Dict     =			common_inputs['''input_ids'''].shape
											lowerCAmelCase_      :					Union[str, Any]     =			common_inputs['''decoder_input_ids'''].shape[1]
											lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[int]     =			self.num_attention_heads
											lowerCAmelCase_      :					str     =			(
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											lowerCAmelCase_      :					Tuple     =			decoder_seq_length + 3
											lowerCAmelCase_      :					Tuple     =			(
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											lowerCAmelCase_      :					int     =			torch.cat(
											    [common_inputs['''decoder_attention_mask'''], torch.ones(A_    ,    A_)]    ,    dim=1)

											lowerCAmelCase_      :					Optional[Any]     =			[]
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											lowerCAmelCase_ ,  lowerCAmelCase_      :					Union[str, Any]     =			self.num_layers
											lowerCAmelCase_      :					List[Any]     =			min(A_    ,    A_)
											lowerCAmelCase_      :					int     =			max(A_    ,    A_) - min_num_layers
											lowerCAmelCase_      :					List[str]     =			'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

											for _ in range(A_):
													common_inputs["past_key_values"].append(
													    (
													        torch.zeros(A_),
													        torch.zeros(A_),
													        torch.zeros(A_),
													        torch.zeros(A_),
													    ))
											# TODO: test this.
											lowerCAmelCase_      :					Any     =			encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
											for _ in range(A_    ,    A_):
													common_inputs["past_key_values"].append((torch.zeros(A_), torch.zeros(A_)))
									return common_inputs




							def        UpperCAmelCase__      (	self					:	List[str]    ,    A_					:	PreTrainedTokenizer    ,    A_					:	int = -1    ,    A_					:	int = -1    ,    A_					:	bool = False    ,    A_					:	Optional[TensorType] = None    ,    ):
									lowerCAmelCase_      :					List[Any]     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    A_    ,    A_    ,    A_    ,    A_    ,    A_)

									if self.use_past:
											if not is_torch_available():
													raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
											else:
													import torch
											lowerCAmelCase_ ,  lowerCAmelCase_      :					int     =			common_inputs['''input_ids'''].shape
											# Not using the same length for past_key_values
											lowerCAmelCase_      :					str     =			seqlen + 2
											lowerCAmelCase_ ,  lowerCAmelCase_      :					int     =			self.num_layers
											lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[int]     =			self.num_attention_heads
											lowerCAmelCase_      :					Optional[Any]     =			(
											    batch,
											    num_encoder_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)

											lowerCAmelCase_      :					Optional[int]     =			common_inputs['''attention_mask'''].dtype
											lowerCAmelCase_      :					List[str]     =			torch.cat(
											    [common_inputs['''attention_mask'''], torch.ones(A_    ,    A_    ,    dtype=A_)]    ,    dim=1)
											lowerCAmelCase_      :					Any     =			[
											    (torch.zeros(A_), torch.zeros(A_)) for _ in range(A_)
											]
									return common_inputs




							def        UpperCAmelCase__      (	self					:	int    ,    A_					:	PreTrainedTokenizer    ,    A_					:	int = -1    ,    A_					:	int = -1    ,    A_					:	bool = False    ,    A_					:	Optional[TensorType] = None    ,    ):
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									lowerCAmelCase_      :					Any     =			compute_effective_axis_dimension(
									    A_    ,    fixed_dimension=OnnxConfig.default_fixed_batch    ,    num_token_to_add=0)

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									lowerCAmelCase_      :					Optional[int]     =			tokenizer.num_special_tokens_to_add(A_)
									lowerCAmelCase_      :					Any     =			compute_effective_axis_dimension(
									    A_    ,    fixed_dimension=OnnxConfig.default_fixed_sequence    ,    num_token_to_add=A_)

									# Generate dummy inputs according to compute batch and sequence
									lowerCAmelCase_      :					str     =			[''' '''.join([tokenizer.unk_token]) * seq_length] * batch_size
									lowerCAmelCase_      :					int     =			dict(tokenizer(A_    ,    return_tensors=A_))
									return common_inputs




							def        UpperCAmelCase__      (	self					:	List[str]    ,    A_					:	PreTrainedTokenizer    ,    A_					:	int = -1    ,    A_					:	int = -1    ,    A_					:	bool = False    ,    A_					:	Optional[TensorType] = None    ,    ):
									if self.task in ["default", "seq2seq-lm"]:
											lowerCAmelCase_      :					Tuple     =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    A_    ,    batch_size=A_    ,    seq_length=A_    ,    is_pair=A_    ,    framework=A_)

									elif self.task == "causal-lm":
											lowerCAmelCase_      :					Optional[int]     =			self._generate_dummy_inputs_for_causal_lm(
											    A_    ,    batch_size=A_    ,    seq_length=A_    ,    is_pair=A_    ,    framework=A_)
									else:
											lowerCAmelCase_      :					Dict     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    A_    ,    batch_size=A_    ,    seq_length=A_    ,    is_pair=A_    ,    framework=A_)

									return common_inputs







							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	List[str]    ,    A_					:	int    ,    A_					:	List[Any]    ,    A_					:	Optional[Any]):
									if self.task in ["default", "seq2seq-lm"]:
											lowerCAmelCase_      :					List[Any]     =			super()._flatten_past_key_values_(A_    ,    A_    ,    A_    ,    A_)
									else:
											lowerCAmelCase_      :					Any     =			super(A_    ,    self)._flatten_past_key_values_(
											    A_    ,    A_    ,    A_    ,    A_)










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						):
							_a      				=			(JukeboxModel,) if is_torch_available() else ()
							_a      				=			'''openai/jukebox-1b-lyrics'''
							_a      				=			{
							    '''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
							_a      				=			[
							    1_864, 1_536, 1_213, 1_870, 1_357, 1_536, 519, 880, 1_323, 789, 1_082, 534,
							    1_000, 1_445, 1_105, 1_130, 967, 515, 1_434, 1_620, 534, 1_495, 283, 1_445,
							    333, 1_307, 539, 1_631, 1_528, 375, 1_434, 673, 627, 710, 778, 1_883,
							    1_405, 1_276, 1_455, 1_228
							]

							_a      				=			[
							    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653
							]

							_a      				=			[
							    1_125, 1_751, 697, 1_776, 1_141, 1_476, 391, 697, 1_125, 684, 867, 416,
							    844, 1_372, 1_274, 717, 1_274, 844, 1_299, 1_419, 697, 1_370, 317, 1_125,
							    191, 1_440, 1_370, 1_440, 1_370, 282, 1_621, 1_370, 368, 349, 867, 1_872,
							    1_262, 869, 1_728, 747
							]
							_a      				=			[
							    416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416
							]

							_a      				=			[
							    1_755, 842, 307, 1_843, 1_022, 1_395, 234, 1_554, 806, 739, 1_022, 442,
							    616, 556, 268, 1_499, 933, 457, 1_440, 1_837, 755, 985, 308, 902,
							    293, 1_443, 1_671, 1_141, 1_533, 555, 1_562, 1_061, 287, 417, 1_022, 2_008,
							    1_186, 1_015, 1_777, 268
							]
							_a      				=			[
							    854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842,
							    185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
							    591, 842, 1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
							    185, 842, 591, 89
							]

							_a      				=			[1_058_304, 0, 786_432, 7_169, 507, 76, 27, 40, 30, 76]

							_a      				=			[
							    390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
							    1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
							    1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
							    1_907, 1_788, 596, 1_626
							]
							_a      				=			[
							    1_236, 1_668, 1_484, 1_920, 1_848, 1_409, 139, 864, 1_828, 1_272, 1_599, 824,
							    1_672, 139, 555, 1_484, 824, 1_920, 555, 596, 1_579, 1_599, 1_231, 1_599,
							    1_637, 1_407, 212, 824, 1_599, 116, 1_433, 824, 258, 1_599, 1_433, 1_895,
							    1_063, 1_433, 1_433, 1_599
							]
							_a      				=			[
							    1_684, 1_873, 1_119, 1_189, 395, 611, 1_901, 972, 890, 1_337, 1_392, 1_927,
							    96, 972, 672, 780, 1_119, 890, 158, 771, 1_073, 1_927, 353, 1_331,
							    1_269, 1_459, 1_333, 1_645, 812, 1_577, 1_337, 606, 353, 981, 1_466, 619,
							    197, 391, 302, 1_930
							]
							_a      				=			[
							    390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
							    1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
							    1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
							    1_907, 1_788, 596, 1_626
							]
							_a      				=			[
							    -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
							]
							_a      				=			[
							    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
							]
							_a      				=			[
							    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
							]
							_a      				=			[
							    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					:	Tuple):
									lowerCAmelCase_      :					Any     =			JukeboxTokenizer.from_pretrained(self.model_id)
									lowerCAmelCase_      :					Union[str, Any]     =			tokenizer(**self.metas)['''input_ids''']
									return tokens




							@slow
							def        UpperCAmelCase__      (	self					:	Any):
									lowerCAmelCase_      :					Tuple     =			JukeboxModel.from_pretrained(self.model_id    ,    min_duration=0).eval()
									lowerCAmelCase_      :					Tuple     =			self.prepare_inputs()

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

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

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




							@slow
							def        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_      :					int     =			False
									lowerCAmelCase_      :					Any     =			JukeboxModel.from_pretrained(self.model_id    ,    min_duration=0).eval()

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

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

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

									lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_      :					List[str]     =			top_prior.get_cond(A_    ,    A_)
									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        UpperCAmelCase__      (	self					:	Union[str, Any]):
									lowerCAmelCase_      :					int     =			False

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

									lowerCAmelCase_      :					List[str]     =			[model.vqvae.encode(A_    ,    start_level=2    ,    bs_chunks=waveform.shape[0])[0], None, None]
									lowerCAmelCase_      :					Tuple     =			model._sample(
									    A_    ,    A_    ,    sample_levels=[0]    ,    save_results=A_    ,    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))

									lowerCAmelCase_      :					Dict     =			torch.cat((zs[0], torch.zeros(1    ,    2_0_4_8 - zs[0].shape[-1]))    ,    dim=-1).long()
									lowerCAmelCase_      :					Optional[int]     =			[upper_a, model.vqvae.encode(A_    ,    start_level=1    ,    bs_chunks=waveform.shape[0])[0], None]
									lowerCAmelCase_      :					List[Any]     =			model._sample(
									    A_    ,    A_    ,    sample_levels=[1]    ,    save_results=A_    ,    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))

									lowerCAmelCase_      :					Dict     =			torch.cat((zs[1], torch.zeros(1    ,    2_0_4_8 - zs[1].shape[-1]))    ,    dim=-1).long()
									lowerCAmelCase_      :					Tuple     =			[upper_a, upper_a, model.vqvae.encode(A_    ,    start_level=0    ,    bs_chunks=waveform.shape[0])[0]]
									lowerCAmelCase_      :					Optional[Any]     =			model._sample(
									    A_    ,    A_    ,    sample_levels=[2]    ,    save_results=A_    ,    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        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_      :					str     =			JukeboxModel.from_pretrained(self.model_id    ,    min_duration=0).eval()
									set_seed(0)
									lowerCAmelCase_      :					List[Any]     =			torch.rand((1, 5_1_2_0, 1))
									with torch.no_grad():
											lowerCAmelCase_      :					Dict     =			model.vqvae.encode(A_    ,    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():
											lowerCAmelCase_      :					Dict     =			model.vqvae.decode(A_    ,    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     __snake_case						(					unittest.TestCase						):
							_a      				=			(JukeboxModel,) if is_torch_available() else ()
							_a      				=			'''openai/jukebox-5b-lyrics'''
							_a      				=			{
							    '''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
							_a      				=			[
							    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    1_489, 1_489, 1_489, 1_489, 1_150, 1_853, 1_509, 1_150, 1_357, 1_509, 6, 1_272
							]
							_a      				=			[
							    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
							]

							_a      				=			[
							    1_125, 416, 1_125, 1_125, 1_125, 1_125, 1_125, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
							]
							_a      				=			[
							    416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416
							]

							_a      				=			[
							    1_755, 1_061, 234, 1_755, 1_061, 1_755, 185, 290, 307, 307, 616, 616,
							    616, 616, 616, 616, 307, 290, 417, 1_755, 234, 1_755, 185, 290,
							    290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1_755,
							    234, 234, 1_755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
							    1_755, 1_755, 1_755, 234, 234, 1_755, 1_572, 290, 307, 616, 34, 616
							]
							_a      				=			[
							    854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842, 185,
							    417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
							    1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
							    89, 591, 842, 591, 842, 591, 417, 1_372, 842, 1_372, 842, 34, 842,
							    185, 89, 591, 842, 185, 842, 591, 632
							]

							_a      				=			[
							    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
							]
							_a      				=			[
							    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
							    653, 653, 653, 653, 653, 653, 653, 1_853, 1_177, 1_536, 1_228,
							    710, 475, 1_489, 1_229, 1_224, 231, 1_224, 252, 1_434, 653, 475,
							    1_106, 1_877, 1_599, 1_228, 1_600, 1_683, 1_182, 1_853, 475, 1_864,
							    252, 1_229, 1_434, 2_001
							]

							_a      				=			[
							    1_125, 1_125, 416, 1_125, 1_125, 416, 1_125, 1_125, 416, 416, 1_125, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
							    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
							]
							_a      				=			[
							    491, 1_755, 34, 1_613, 1_755, 417, 992, 1_613, 222, 842, 1_353, 1_613,
							    844, 632, 185, 1_613, 844, 632, 185, 1_613, 185, 842, 677, 1_613,
							    185, 114, 1_353, 1_613, 307, 89, 844, 1_613, 307, 1_332, 234, 1_979,
							    307, 89, 1_353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
							    307, 114, 185, 89, 34, 1_268, 185, 89, 34, 842, 185, 89
							]




							def        UpperCAmelCase__      (	self					:	int    ,    A_					:	Union[str, Any]):
									lowerCAmelCase_      :					Tuple     =			JukeboxTokenizer.from_pretrained(A_)
									lowerCAmelCase_      :					Any     =			tokenizer(**self.metas)['''input_ids''']
									return tokens




							@slow
							def        UpperCAmelCase__      (	self					:	Dict):
									lowerCAmelCase_      :					Optional[Any]     =			JukeboxModel.from_pretrained(self.model_id    ,    min_duration=0).eval()
									lowerCAmelCase_      :					Union[str, Any]     =			self.prepare_inputs(self.model_id)

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

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

									set_seed(0)
									lowerCAmelCase_      :					Tuple     =			model._sample(A_    ,    A_    ,    [2]    ,    sample_length=6_0 * model.priors[2].raw_to_tokens    ,    save_results=A_)
									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[str]):
									lowerCAmelCase_      :					str     =			JukeboxModel.from_pretrained(self.model_id    ,    min_duration=0).eval()
									lowerCAmelCase_      :					Optional[Any]     =			[i.cuda() for i in self.prepare_inputs(self.model_id)]

									set_seed(0)
									model.priors[0].cuda()
									lowerCAmelCase_      :					Tuple     =			[torch.zeros(1    ,    0    ,    dtype=torch.long).cuda() for _ in range(3)]
									lowerCAmelCase_      :					Any     =			model._sample(A_    ,    A_    ,    [0]    ,    sample_length=6_0 * model.priors[0].raw_to_tokens    ,    save_results=A_)
									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()
									lowerCAmelCase_      :					Any     =			model._sample(A_    ,    A_    ,    [1]    ,    sample_length=6_0 * model.priors[1].raw_to_tokens    ,    save_results=A_)
									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()
									lowerCAmelCase_      :					Union[str, Any]     =			model._sample(A_    ,    A_    ,    [2]    ,    sample_length=6_0 * model.priors[2].raw_to_tokens    ,    save_results=A_)
									torch.testing.assert_allclose(zs[2][0].cpu()    ,    torch.tensor(self.EXPECTED_GPU_OUTPUTS_0))







							@slow
							def        UpperCAmelCase__      (	self					:	Optional[int]):
									lowerCAmelCase_      :					Tuple     =			'''ArthurZ/jukebox_prior_0'''
									lowerCAmelCase_      :					Any     =			JukeboxPrior.from_pretrained(A_    ,    min_duration=0).eval().half().to('''cuda''')

									lowerCAmelCase_      :					Any     =			self.prepare_inputs(A_)[0].cuda()
									lowerCAmelCase_      :					Any     =			model.get_metadata(A_    ,    0    ,    7_6_8_0    ,    0)
									set_seed(0)
									lowerCAmelCase_      :					Optional[int]     =			model.sample(1    ,    metadata=A_    ,    sample_tokens=6_0)
									self.assertIn(outputs[0].cpu().tolist()    ,    [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2])





