
import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 _snake_case      = '''platform'''
 import jax
 import jax.numpy as jnp
 import numpy as np

 from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class       _lowerCAmelCase  :

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=PegasusConfig
 SCREAMING_SNAKE_CASE_				:					List[Any]       						={}
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						="gelu"

 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			List[str]=13					, SCREAMING_SNAKE_CASE__			:			Any=7					, SCREAMING_SNAKE_CASE__			:			Any=True					, SCREAMING_SNAKE_CASE__			:			List[Any]=False					, SCREAMING_SNAKE_CASE__			:			Dict=99					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=32					, SCREAMING_SNAKE_CASE__			:			int=5					, SCREAMING_SNAKE_CASE__			:			Any=4					, SCREAMING_SNAKE_CASE__			:			int=37					, SCREAMING_SNAKE_CASE__			:			Tuple=0.1					, SCREAMING_SNAKE_CASE__			:			int=0.1					, SCREAMING_SNAKE_CASE__			:			Dict=20					, SCREAMING_SNAKE_CASE__			:			Any=2					, SCREAMING_SNAKE_CASE__			:			int=1					, SCREAMING_SNAKE_CASE__			:			List[Any]=0					, ):






      """simple docstring"""



      UpperCamelCase						    =					parent
      UpperCamelCase						    =					batch_size
      UpperCamelCase						    =					seq_length
      UpperCamelCase						    =					is_training
      UpperCamelCase						    =					use_labels
      UpperCamelCase						    =					vocab_size
      UpperCamelCase						    =					hidden_size
      UpperCamelCase						    =					num_hidden_layers
      UpperCamelCase						    =					num_attention_heads
      UpperCamelCase						    =					intermediate_size

      UpperCamelCase						    =					hidden_dropout_prob
      UpperCamelCase						    =					attention_probs_dropout_prob
      UpperCamelCase						    =					max_position_embeddings
      UpperCamelCase						    =					eos_token_id
      UpperCamelCase						    =					pad_token_id
      UpperCamelCase						    =					bos_token_id

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					ids_tensor([self.batch_size, self.seq_length - 1]					, self.vocab_size   ).clip(3					, self.vocab_size   )
      UpperCamelCase						    =					np.expand_dims(np.array([self.eos_token_id] * self.batch_size   )					, 1   )
      UpperCamelCase						    =					np.concatenate([input_ids, eos_tensor]					, axis=1   )

      UpperCamelCase						    =					ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size   )

      UpperCamelCase						    =					self.config_cls(
          vocab_size=self.vocab_size					, d_model=self.hidden_size					, encoder_layers=self.num_hidden_layers					, decoder_layers=self.num_hidden_layers					, encoder_attention_heads=self.num_attention_heads					, decoder_attention_heads=self.num_attention_heads					, encoder_ffn_dim=self.intermediate_size					, decoder_ffn_dim=self.intermediate_size					, dropout=self.hidden_dropout_prob					, attention_dropout=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, eos_token_ids=[2]					, bos_token_id=self.bos_token_id					, pad_token_id=self.pad_token_id					, decoder_start_token_id=self.pad_token_id					, **self.config_updates					, )
      UpperCamelCase						    =					prepare_pegasus_inputs_dict(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
      return config, inputs_dict

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					20
      UpperCamelCase						    =					model_class_name(SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					model.encode(inputs_dict['input_ids']   )

      UpperCamelCase    ,		UpperCamelCase						    =					(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      UpperCamelCase						    =					model.init_cache(decoder_input_ids.shape[0]					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					, dtype='i4'   )

      UpperCamelCase						    =					jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]					, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					, )
      UpperCamelCase						    =					model.decode(
          decoder_input_ids[:, :-1]					, SCREAMING_SNAKE_CASE__					, decoder_attention_mask=SCREAMING_SNAKE_CASE__					, past_key_values=SCREAMING_SNAKE_CASE__					, decoder_position_ids=SCREAMING_SNAKE_CASE__					, )

      UpperCamelCase						    =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					, dtype='i4'   )
      UpperCamelCase						    =					model.decode(
          decoder_input_ids[:, -1:]					, SCREAMING_SNAKE_CASE__					, decoder_attention_mask=SCREAMING_SNAKE_CASE__					, past_key_values=outputs_cache.past_key_values					, decoder_position_ids=SCREAMING_SNAKE_CASE__					, )

      UpperCamelCase						    =					model.decode(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
      self.parent.assertTrue(diff < 1e-3					, msg=F'Max diff is {diff}'   )






 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					20
      UpperCamelCase						    =					model_class_name(SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					model.encode(inputs_dict['input_ids']   )

      UpperCamelCase    ,		UpperCamelCase						    =					(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      UpperCamelCase						    =					jnp.concatenate(
          [
              decoder_attention_mask,
              jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
          ]					, axis=-1					, )

      UpperCamelCase						    =					model.init_cache(decoder_input_ids.shape[0]					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]					, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					, )

      UpperCamelCase						    =					model.decode(
          decoder_input_ids[:, :-1]					, SCREAMING_SNAKE_CASE__					, decoder_attention_mask=SCREAMING_SNAKE_CASE__					, past_key_values=SCREAMING_SNAKE_CASE__					, decoder_position_ids=SCREAMING_SNAKE_CASE__					, )
      UpperCamelCase						    =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					, dtype='i4'   )
      UpperCamelCase						    =					model.decode(
          decoder_input_ids[:, -1:]					, SCREAMING_SNAKE_CASE__					, past_key_values=outputs_cache.past_key_values					, decoder_attention_mask=SCREAMING_SNAKE_CASE__					, decoder_position_ids=SCREAMING_SNAKE_CASE__					, )

      UpperCamelCase						    =					model.decode(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, decoder_attention_mask=SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
      self.parent.assertTrue(diff < 1e-3					, msg=F'Max diff is {diff}'   )




def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=None						,     _lowercase=None						,     )       ->      int:
     if attention_mask is None:
          UpperCamelCase						    =					np.not_equal(_lowercase						,     config.pad_token_id			).astype(np.inta			)
     if decoder_attention_mask is None:
          UpperCamelCase						    =					np.concatenate(
              [
                  np.ones(decoder_input_ids[:, :1].shape						,     dtype=np.inta			),
                  np.not_equal(decoder_input_ids[:, 1:]						,     config.pad_token_id			).astype(np.inta			),
              ]						,     axis=-1						,     )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
     }







@require_flax
class       _lowerCAmelCase  (		__magic_name__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Any       						=(
     (
         FlaxPegasusForConditionalGeneration,
         FlaxPegasusModel,
     )
     if is_flax_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_				:					List[str]       						=(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
 SCREAMING_SNAKE_CASE_				:					List[str]       						=True
 SCREAMING_SNAKE_CASE_				:					Any       						=False
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=False
 SCREAMING_SNAKE_CASE_				:					int       						=False

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					FlaxPegasusModelTester(self   )
      UpperCamelCase						    =					ConfigTester(self					, config_class=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      self.config_tester.run_common_tests()

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__   ):
                UpperCamelCase						    =					self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )

                @jax.jit
                def encode_jitted(SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Tuple=None					, **SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):
                     return model.encode(input_ids=SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__   )

                with self.subTest('JIT Enabled'   ):
                     UpperCamelCase						    =					encode_jitted(**SCREAMING_SNAKE_CASE__   ).to_tuple()

                with self.subTest('JIT Disabled'   ):
                     with jax.disable_jit():
                          UpperCamelCase						    =					encode_jitted(**SCREAMING_SNAKE_CASE__   ).to_tuple()

                self.assertEqual(len(SCREAMING_SNAKE_CASE__   )					, len(SCREAMING_SNAKE_CASE__   )   )
                for jitted_output, output in zip(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ):
                     self.assertEqual(jitted_output.shape					, output.shape   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__   ):
                UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					model.encode(inputs_dict['input_ids']					, inputs_dict['attention_mask']   )

                UpperCamelCase						    =					{
                    'decoder_input_ids': inputs_dict['decoder_input_ids'],
                    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                    'encoder_outputs': encoder_outputs,
                }

                @jax.jit
                def decode_jitted(SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Any   ):
                     return model.decode(
                         decoder_input_ids=SCREAMING_SNAKE_CASE__					, decoder_attention_mask=SCREAMING_SNAKE_CASE__					, encoder_outputs=SCREAMING_SNAKE_CASE__					, )

                with self.subTest('JIT Enabled'   ):
                     UpperCamelCase						    =					decode_jitted(**SCREAMING_SNAKE_CASE__   ).to_tuple()

                with self.subTest('JIT Disabled'   ):
                     with jax.disable_jit():
                          UpperCamelCase						    =					decode_jitted(**SCREAMING_SNAKE_CASE__   ).to_tuple()

                self.assertEqual(len(SCREAMING_SNAKE_CASE__   )					, len(SCREAMING_SNAKE_CASE__   )   )
                for jitted_output, output in zip(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ):
                     self.assertEqual(jitted_output.shape					, output.shape   )

 @slow
 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      for model_class_name in self.all_model_classes:
           UpperCamelCase						    =					model_class_name.from_pretrained('google/pegasus-large'					, from_pt=SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					np.ones((1, 1)   )
           UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__   )
           self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )






 @slow
 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'   )
      UpperCamelCase						    =					PegasusTokenizer.from_pretrained('google/pegasus-xsum'   )

      UpperCamelCase						    =					[
          ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
          ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
      ]

      UpperCamelCase						    =					[
          'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
          'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
      ]

      UpperCamelCase						    =					tokenizer(SCREAMING_SNAKE_CASE__					, return_tensors='np'					, truncation=SCREAMING_SNAKE_CASE__					, max_length=5_12					, padding=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					model.generate(**SCREAMING_SNAKE_CASE__					, num_beams=2   ).sequences
      UpperCamelCase						    =					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					, skip_special_tokens=SCREAMING_SNAKE_CASE__   )
      assert tgt_text == decoded




import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=JukeboxTokenizer
 SCREAMING_SNAKE_CASE_				:					str       						={
     "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        ",
 }

 @require_torch
 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      import torch

      UpperCamelCase						    =					JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics'   )
      UpperCamelCase						    =					tokenizer(**self.metas   )['input_ids']
      # fmt: off
      UpperCamelCase						    =					[
          torch.tensor([[
              0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
              76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
              44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
              47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
              76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
              30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 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, 76, 76, 76, 76, 76, 76, 76,
              19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
              76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
              76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
              64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
              30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
              27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
              34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
              27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
              41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
              76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
              44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
              76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
              32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
              40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
              20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
              45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
              31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
              45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
              76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
              34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
              31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
              40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
              38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
              76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
              41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
              27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
              46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
              76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
              41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
              46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
              41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
              40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
              27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
              76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
              76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
              41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
              76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
              27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
              34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
              44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
              40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
              46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
              38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
              40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
              27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
              20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
              76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
              76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
              76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
              76, 76]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11]]   ),
      ]
      # fmt: on
      self.assertTrue(torch.allclose(tokens[0]					, EXPECTED_OUTPUT[0]   )   )
      self.assertTrue(torch.allclose(tokens[1]					, EXPECTED_OUTPUT[1]   )   )
      self.assertTrue(torch.allclose(tokens[2]					, EXPECTED_OUTPUT[2]   )   )






 @require_torch
 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      import torch

      UpperCamelCase						    =					JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics'   )
      UpperCamelCase						    =					tokenizer(**self.metas   )['input_ids']
      # fmt: off
      UpperCamelCase						    =					[
          torch.tensor([[
              0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
              31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
              31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
              40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
              79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
              77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
              27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
              37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
              32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
              77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
              77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
              46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
              77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
              77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
              77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
              77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
              64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
              40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
              40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
              38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
              31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
              41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
              46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
              41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
              31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
              31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
              23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
              44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
              31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
              38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
              40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
              27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
              31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
              34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
              31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
              1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
              31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
              45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
              31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
              15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
              11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
              45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
              41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
              44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
              46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
              27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
              35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
              77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
              31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
              41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
              77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
              40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
              77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
              27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
              77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
              77, 77, 77, 77, 77, 77]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]   ),
      ]
      # fmt: on
      self.assertTrue(torch.allclose(tokens[0]					, EXPECTED_OUTPUT[0]   )   )
      self.assertTrue(torch.allclose(tokens[1]					, EXPECTED_OUTPUT[1]   )   )
      self.assertTrue(torch.allclose(tokens[2]					, EXPECTED_OUTPUT[2]   )   )


