






'''simple docstring'''
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							(UnCLIPScheduler,)




 def      __magic_name__	(      self	:							Optional[int]					,      **__lowercase	:							List[str]      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Tuple									={
           "num_train_timesteps": 10_00,
           "variance_type": "fixed_small_log",
           "clip_sample": True,
           "clip_sample_range": 1.0,
           "prediction_type": "epsilon",
       }

       config.update(**UpperCamelCase_      )
       return config




 def      __magic_name__	(      self	:							str      )	->						Union[str, Any]:
       for timesteps in [1, 5, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=UpperCamelCase_      )




 def      __magic_name__	(      self	:							str      )	->						Union[str, Any]:
       for variance in ["fixed_small_log", "learned_range"]:
             self.check_over_configs(variance_type=UpperCamelCase_      )




 def      __magic_name__	(      self	:							str      )	->						Optional[int]:
       for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=UpperCamelCase_      )




 def      __magic_name__	(      self	:							Tuple      )	->						Union[str, Any]:
       for clip_sample_range in [1, 5, 10, 20]:
             self.check_over_configs(clip_sample_range=UpperCamelCase_      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						Any:
       for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(prediction_type=UpperCamelCase_      )




 def      __magic_name__	(      self	:							Any      )	->						Tuple:
       for time_step in [0, 5_00, 9_99]:
             for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
                   if prev_timestep is not None and prev_timestep >= time_step:
                         continue

                   self.check_over_forward(time_step=UpperCamelCase_					,      prev_timestep=UpperCamelCase_      )




 def      __magic_name__	(      self	:							List[Any]      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Dict									=self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.get_scheduler_config(variance_type='''fixed_small_log'''      )
       SCREAMING_SNAKE_CASE__     :				int									=scheduler_class(**UpperCamelCase_      )

       assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 1.0000e-10      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_87      ) - 0.0549625      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_99      ) - 0.9994987      )      ) < 1e-5




 def      __magic_name__	(      self	:							Any      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__     :				str									=self.get_scheduler_config(variance_type='''learned_range'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=scheduler_class(**UpperCamelCase_      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=0.5

       assert scheduler._get_variance(1					,      predicted_variance=UpperCamelCase_      ) - -10.1_71_27_90 < 1e-5
       assert scheduler._get_variance(4_87					,      predicted_variance=UpperCamelCase_      ) - -5.7998052 < 1e-5
       assert scheduler._get_variance(9_99					,      predicted_variance=UpperCamelCase_      ) - -0.0010011 < 1e-5




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__     :				Dict									=self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__     :				Tuple									=scheduler_class(**UpperCamelCase_      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=scheduler.timesteps

       SCREAMING_SNAKE_CASE__     :				Any									=self.dummy_model()
       SCREAMING_SNAKE_CASE__     :				List[Any]									=self.dummy_sample_deter
       SCREAMING_SNAKE_CASE__     :				Any									=torch.manual_seed(0      )

       for i, t in enumerate(UpperCamelCase_      ):
             # 1. predict noise residual
             SCREAMING_SNAKE_CASE__     :				Dict									=model(UpperCamelCase_					,      UpperCamelCase_      )

             # 2. predict previous mean of sample x_t-1
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=scheduler.step(UpperCamelCase_					,      UpperCamelCase_					,      UpperCamelCase_					,      generator=UpperCamelCase_      ).prev_sample

             SCREAMING_SNAKE_CASE__     :				List[Any]									=pred_prev_sample

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.sum(torch.abs(UpperCamelCase_      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.mean(torch.abs(UpperCamelCase_      )      )

       assert abs(result_sum.item() - 2_52.2_68_24_95      ) < 1e-2
       assert abs(result_mean.item() - 0.3284743      ) < 1e-3




 def      __magic_name__	(      self	:							Any      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__     :				str									=scheduler_class(**UpperCamelCase_      )

       scheduler.set_timesteps(25      )

       SCREAMING_SNAKE_CASE__     :				str									=scheduler.timesteps

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.dummy_model()
       SCREAMING_SNAKE_CASE__     :				Dict									=self.dummy_sample_deter
       SCREAMING_SNAKE_CASE__     :				List[str]									=torch.manual_seed(0      )

       for i, t in enumerate(UpperCamelCase_      ):
             # 1. predict noise residual
             SCREAMING_SNAKE_CASE__     :				Tuple									=model(UpperCamelCase_					,      UpperCamelCase_      )

             if i + 1 == timesteps.shape[0]:
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									=None
             else:
                   SCREAMING_SNAKE_CASE__     :				Dict									=timesteps[i + 1]

             # 2. predict previous mean of sample x_t-1
             SCREAMING_SNAKE_CASE__     :				Tuple									=scheduler.step(
                 UpperCamelCase_					,      UpperCamelCase_					,      UpperCamelCase_					,      prev_timestep=UpperCamelCase_					,      generator=UpperCamelCase_      ).prev_sample

             SCREAMING_SNAKE_CASE__     :				Optional[int]									=pred_prev_sample

       SCREAMING_SNAKE_CASE__     :				str									=torch.sum(torch.abs(UpperCamelCase_      )      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.mean(torch.abs(UpperCamelCase_      )      )

       assert abs(result_sum.item() - 2_58.2_04_49_83      ) < 1e-2
       assert abs(result_mean.item() - 0.3362038      ) < 1e-3




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




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







'''simple docstring'''
import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch






class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):
 snake_case_  							=							JukeboxTokenizer
 snake_case_  							=							{
     """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      __magic_name__	(      self	:							Optional[int]      )	->						str:
       import torch

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

       SCREAMING_SNAKE_CASE__     :				int									=JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics'''      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=tokenizer(**self.metas      )['''input_ids''']
       # fmt: off
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=[
           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]      )      )