




'''simple docstring'''







import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_MAPPING,
       SegformerForImageClassification,
       SegformerForSemanticSegmentation,
       SegformerModel,
   )
   from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import SegformerImageProcessor


class 			_a  (	__a ):
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.config_class(**self.inputs_dict				)
           self.parent.assertTrue(hasattr(lowercase   ,				'''hidden_sizes'''				)				)
           self.parent.assertTrue(hasattr(lowercase   ,				'''num_attention_heads'''				)				)
           self.parent.assertTrue(hasattr(lowercase   ,				'''num_encoder_blocks'''				)				)






class 			_a  :
       def __init__(	self						:	Optional[Any]   ,				lowercase						:	Any   ,				lowercase						:	Optional[Any]=13   ,				lowercase						:	Any=64   ,				lowercase						:	Any=3   ,				lowercase						:	int=4   ,				lowercase						:	Any=[2, 2, 2, 2]   ,				lowercase						:	Optional[int]=[8, 4, 2, 1]   ,				lowercase						:	int=[16, 32, 64, 128]   ,				lowercase						:	Dict=[1, 4, 8, 16]   ,				lowercase						:	List[Any]=[1, 2, 4, 8]   ,				lowercase						:	Optional[Any]=True   ,				lowercase						:	Tuple=True   ,				lowercase						:	Tuple="gelu"   ,				lowercase						:	Any=0.1   ,				lowercase						:	Optional[int]=0.1   ,				lowercase						:	Optional[Any]=0.02   ,				lowercase						:	Optional[int]=3   ,				lowercase						:	Optional[int]=None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		parent
           UpperCAmelCase			       =		batch_size
           UpperCAmelCase			       =		image_size
           UpperCAmelCase			       =		num_channels
           UpperCAmelCase			       =		num_encoder_blocks
           UpperCAmelCase			       =		sr_ratios
           UpperCAmelCase			       =		depths
           UpperCAmelCase			       =		hidden_sizes
           UpperCAmelCase			       =		downsampling_rates
           UpperCAmelCase			       =		num_attention_heads
           UpperCAmelCase			       =		is_training
           UpperCAmelCase			       =		use_labels
           UpperCAmelCase			       =		hidden_act
           UpperCAmelCase			       =		hidden_dropout_prob
           UpperCAmelCase			       =		attention_probs_dropout_prob
           UpperCAmelCase			       =		initializer_range
           UpperCAmelCase			       =		num_labels
           UpperCAmelCase			       =		scope
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           UpperCAmelCase			       =		None
           if self.use_labels:
               UpperCAmelCase			       =		ids_tensor([self.batch_size, self.image_size, self.image_size]   ,				self.num_labels				)

           UpperCAmelCase			       =		self.get_config()
           return config, pixel_values, labels
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           return SegformerConfig(
               image_size=self.image_size   ,				num_channels=self.num_channels   ,				num_encoder_blocks=self.num_encoder_blocks   ,				depths=self.depths   ,				hidden_sizes=self.hidden_sizes   ,				num_attention_heads=self.num_attention_heads   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				initializer_range=self.initializer_range   ,				)
       def     A      (	self						:	Any   ,				lowercase						:	Optional[int]   ,				lowercase						:	Optional[Any]   ,				lowercase						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		SegformerModel(config=lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase				)
           UpperCAmelCase			       =		UpperCAmelCase			       =		self.image_size // (self.downsampling_rates[-1] * 2)
           self.parent.assertEqual(
               result.last_hidden_state.shape   ,				(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	Dict   ,				lowercase						:	Dict   ,				lowercase						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.num_labels
           UpperCAmelCase			       =		SegformerForSemanticSegmentation(lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase				)
           self.parent.assertEqual(
               result.logits.shape   ,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)				)
           UpperCAmelCase			       =		model(lowercase   ,				labels=lowercase				)
           self.parent.assertEqual(
               result.logits.shape   ,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)				)
           self.parent.assertGreater(result.loss   ,				0.0				)
       def     A      (	self						:	Dict   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	Tuple   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		1
           UpperCAmelCase			       =		SegformerForSemanticSegmentation(config=lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		torch.randint(0   ,				1   ,				(self.batch_size, self.image_size, self.image_size)				).to(lowercase				)
           UpperCAmelCase			       =		model(lowercase   ,				labels=lowercase				)
           self.parent.assertGreater(result.loss   ,				0.0				)







       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.prepare_config_and_inputs()
           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		config_and_inputs
           UpperCAmelCase			       =		{'''pixel_values''': pixel_values}
           return config, inputs_dict






@require_torch
class 			_a  (	__a							,							__a							,							unittest.TestCase ):
       __a				:     Optional[Any]   				=					(
           (
               SegformerModel,
               SegformerForSemanticSegmentation,
               SegformerForImageClassification,
           )
           if is_torch_available()
           else ()
       )
       __a				:     Dict   				=					(
           {
               """feature-extraction""": SegformerModel,
               """image-classification""": SegformerForImageClassification,
               """image-segmentation""": SegformerForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       __a				:     Union[str, Any]   				=					True
       __a				:     str   				=					False
       __a				:     Dict   				=					False
       __a				:     int   				=					False
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		SegformerModelTester(self				)
           UpperCAmelCase			       =		SegformerConfigTester(self   ,				config_class=lowercase				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_binary_image_segmentation(*lowercase				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_segmentation(*lowercase				)
       @unittest.skip('''SegFormer does not use inputs_embeds'''				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           pass
       @unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods'''				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)
               UpperCAmelCase			       =		inspect.signature(model.forward				)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               UpperCAmelCase			       =		[*signature.parameters.keys()]

               UpperCAmelCase			       =		['''pixel_values''']
               self.assertListEqual(arg_names[:1]   ,				lowercase				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase			       =		True

           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		True
               UpperCAmelCase			       =		False
               UpperCAmelCase			       =		True
               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.eval()
               with torch.no_grad():
                   UpperCAmelCase			       =		model(**self._prepare_for_class(lowercase   ,				lowercase				)				)
               UpperCAmelCase			       =		outputs.attentions

               UpperCAmelCase			       =		sum(self.model_tester.depths				)
               self.assertEqual(len(lowercase				)   ,				lowercase				)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               UpperCAmelCase			       =		True
               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.eval()
               with torch.no_grad():
                   UpperCAmelCase			       =		model(**self._prepare_for_class(lowercase   ,				lowercase				)				)
               UpperCAmelCase			       =		outputs.attentions

               self.assertEqual(len(lowercase				)   ,				lowercase				)

               # verify the first attentions (first block, first layer)
               UpperCAmelCase			       =		(self.model_tester.image_size // 4) ** 2
               UpperCAmelCase			       =		(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
               self.assertListEqual(
                   list(attentions[0].shape[-3:]				)   ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,				)

               # verify the last attentions (last block, last layer)
               UpperCAmelCase			       =		(self.model_tester.image_size // 32) ** 2
               UpperCAmelCase			       =		(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
               self.assertListEqual(
                   list(attentions[-1].shape[-3:]				)   ,				[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]   ,				)
               UpperCAmelCase			       =		len(lowercase				)

               # Check attention is always last and order is fine
               UpperCAmelCase			       =		True
               UpperCAmelCase			       =		True
               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.eval()
               with torch.no_grad():
                   UpperCAmelCase			       =		model(**self._prepare_for_class(lowercase   ,				lowercase				)				)

               self.assertEqual(out_len + 1   ,				len(lowercase				)				)

               UpperCAmelCase			       =		outputs.attentions

               self.assertEqual(len(lowercase				)   ,				lowercase				)
               # verify the first attentions (first block, first layer)
               UpperCAmelCase			       =		(self.model_tester.image_size // 4) ** 2
               UpperCAmelCase			       =		(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]				)   ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           def check_hidden_states_output(lowercase						:	Dict   ,				lowercase						:	List[str]   ,				lowercase						:	Optional[Any]				):
               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.eval()

               with torch.no_grad():
                   UpperCAmelCase			       =		model(**self._prepare_for_class(lowercase   ,				lowercase				)				)

               UpperCAmelCase			       =		outputs.hidden_states

               UpperCAmelCase			       =		self.model_tester.num_encoder_blocks
               self.assertEqual(len(lowercase				)   ,				lowercase				)

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]				)   ,				[
                       self.model_tester.hidden_sizes[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ]   ,				)

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		True
               check_hidden_states_output(lowercase   ,				lowercase   ,				lowercase				)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCAmelCase			       =		True

               check_hidden_states_output(lowercase   ,				lowercase   ,				lowercase				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           if not self.model_tester.is_training:
               return

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase			       =		True

           for model_class in self.all_model_classes:
               if model_class in get_values(lowercase				):
                   continue

               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.train()
               UpperCAmelCase			       =		self._prepare_for_class(lowercase   ,				lowercase   ,				return_labels=lowercase				)
               UpperCAmelCase			       =		model(**lowercase				).loss
               loss.backward()
       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           pass







       @slow
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase			       =		SegformerModel.from_pretrained(lowercase				)
               self.assertIsNotNone(lowercase				)






def 						snake_case_							():
    UpperCAmelCase			       =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
    return image







@require_torch
class 			_a  (	unittest.TestCase ):
       @slow
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		SegformerImageProcessor(
               image_scale=(512, 512)   ,				keep_ratio=lowercase   ,				align=lowercase   ,				do_random_crop=lowercase				)
           UpperCAmelCase			       =		SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''				).to(
               lowercase				)

           UpperCAmelCase			       =		prepare_img()
           UpperCAmelCase			       =		image_processor(images=lowercase   ,				return_tensors='''pt'''				)
           UpperCAmelCase			       =		encoded_inputs.pixel_values.to(lowercase				)

           with torch.no_grad():
               UpperCAmelCase			       =		model(lowercase				)

           UpperCAmelCase			       =		torch.Size((1, model.config.num_labels, 128, 128)				)
           self.assertEqual(outputs.logits.shape   ,				lowercase				)

           UpperCAmelCase			       =		torch.tensor(
               [
                   [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                   [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                   [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
               ]				).to(lowercase				)
           self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]   ,				lowercase   ,				atol=1E-4				)				)
       @slow
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		SegformerImageProcessor(
               image_scale=(512, 512)   ,				keep_ratio=lowercase   ,				align=lowercase   ,				do_random_crop=lowercase				)
           UpperCAmelCase			       =		SegformerForSemanticSegmentation.from_pretrained(
               '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024'''				).to(lowercase				)

           UpperCAmelCase			       =		prepare_img()
           UpperCAmelCase			       =		image_processor(images=lowercase   ,				return_tensors='''pt'''				)
           UpperCAmelCase			       =		encoded_inputs.pixel_values.to(lowercase				)

           with torch.no_grad():
               UpperCAmelCase			       =		model(lowercase				)

           UpperCAmelCase			       =		torch.Size((1, model.config.num_labels, 128, 128)				)
           self.assertEqual(outputs.logits.shape   ,				lowercase				)

           UpperCAmelCase			       =		torch.tensor(
               [
                   [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                   [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                   [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
               ]				).to(lowercase				)
           self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]   ,				lowercase   ,				atol=1E-1				)				)







       @slow
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		SegformerImageProcessor(
               image_scale=(512, 512)   ,				keep_ratio=lowercase   ,				align=lowercase   ,				do_random_crop=lowercase				)
           UpperCAmelCase			       =		SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''				).to(
               lowercase				)

           UpperCAmelCase			       =		prepare_img()
           UpperCAmelCase			       =		image_processor(images=lowercase   ,				return_tensors='''pt'''				)
           UpperCAmelCase			       =		encoded_inputs.pixel_values.to(lowercase				)

           with torch.no_grad():
               UpperCAmelCase			       =		model(lowercase				)

           UpperCAmelCase			       =		outputs.logits.detach().cpu()

           UpperCAmelCase			       =		image_processor.post_process_semantic_segmentation(outputs=lowercase   ,				target_sizes=[(500, 300)]				)
           UpperCAmelCase			       =		torch.Size((500, 300)				)
           self.assertEqual(segmentation[0].shape   ,				lowercase				)

           UpperCAmelCase			       =		image_processor.post_process_semantic_segmentation(outputs=lowercase				)
           UpperCAmelCase			       =		torch.Size((128, 128)				)
           self.assertEqual(segmentation[0].shape   ,				lowercase				)





import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch



class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:Tuple    			=		JukeboxTokenizer
 lowerCamelCase	:str    			=		{
     '''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
        ''',
 }



 @require_torch
 def      UpperCAmelCase							(					self							)  ->					Tuple:
        import torch

        _A        =  JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""							)
        _A        =  tokenizer(**self.metas							)["""input_ids"""]
        # fmt: off
        _A        =  [
            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      UpperCAmelCase							(					self							)  ->					List[str]:
        import torch

        _A        =  JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""							)
        _A        =  tokenizer(**self.metas							)["""input_ids"""]
        # fmt: off
        _A        =  [
            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]							)							)




