





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Union[str, Any]         =  (UnCLIPScheduler,)

   def 							__lowercase		(					self					,  **lowercase)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Optional[int] =				{
              'num_train_timesteps': 1000,
              'variance_type': 'fixed_small_log',
              'clip_sample': True,
              'clip_sample_range': 1.0,
              'prediction_type': 'epsilon',
          }

          config.update(**lowercase)
          return config

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          for timesteps in [1, 5, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          for variance in ["fixed_small_log", "learned_range"]:
                 self.check_over_configs(variance_type=lowercase)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=lowercase)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          for clip_sample_range in [1, 5, 10, 20]:
                 self.check_over_configs(clip_sample_range=lowercase)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(prediction_type=lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          for time_step in [0, 500, 999]:
                 for prev_timestep in [None, 5, 100, 250, 500, 750]:
                        if prev_timestep is not None and prev_timestep >= time_step:
                               continue

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

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.scheduler_classes[0]
          a__						:	Tuple =				self.get_scheduler_config(variance_type='fixed_small_log')
          a__						:	Dict =				scheduler_class(**lowercase)

          assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0000e-10)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_54_96_25)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9_99_49_87)) < 1e-5

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.scheduler_classes[0]
          a__						:	List[str] =				self.get_scheduler_config(variance_type='learned_range')
          a__						:	Optional[Any] =				scheduler_class(**lowercase)

          a__						:	Optional[Any] =				0.5

          assert scheduler._get_variance(1					,  predicted_variance=lowercase) - -10.1_71_27_90 < 1e-5
          assert scheduler._get_variance(487					,  predicted_variance=lowercase) - -5.7_99_80_52 < 1e-5
          assert scheduler._get_variance(999					,  predicted_variance=lowercase) - -0.0_01_00_11 < 1e-5

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Dict =				self.scheduler_classes[0]
          a__						:	List[str] =				self.get_scheduler_config()
          a__						:	Tuple =				scheduler_class(**lowercase)

          a__						:	List[str] =				scheduler.timesteps

          a__						:	Optional[Any] =				self.dummy_model()
          a__						:	int =				self.dummy_sample_deter
          a__						:	Tuple =				torch.manual_seed(0)

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

                 # 2. predict previous mean of sample x_t-1
                 a__						:	Union[str, Any] =				scheduler.step(lowercase					,  lowercase					,  lowercase					,  generator=lowercase).prev_sample

                 a__						:	Tuple =				pred_prev_sample

          a__						:	Tuple =				torch.sum(torch.abs(lowercase))
          a__						:	List[str] =				torch.mean(torch.abs(lowercase))

          assert abs(result_sum.item() - 2_52.2_68_24_95) < 1e-2
          assert abs(result_mean.item() - 0.3_28_47_43) < 1e-3

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[int] =				self.scheduler_classes[0]
          a__						:	Optional[int] =				self.get_scheduler_config()
          a__						:	Any =				scheduler_class(**lowercase)

          scheduler.set_timesteps(25)

          a__						:	List[Any] =				scheduler.timesteps

          a__						:	List[str] =				self.dummy_model()
          a__						:	Optional[Any] =				self.dummy_sample_deter
          a__						:	Optional[Any] =				torch.manual_seed(0)

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

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

                 # 2. predict previous mean of sample x_t-1
                 a__						:	int =				scheduler.step(
                     lowercase					,  lowercase					,  lowercase					,  prev_timestep=lowercase					,  generator=lowercase).prev_sample

                 a__						:	str =				pred_prev_sample

          a__						:	Dict =				torch.sum(torch.abs(lowercase))
          a__						:	Optional[int] =				torch.mean(torch.abs(lowercase))

          assert abs(result_sum.item() - 2_58.2_04_49_83) < 1e-2
          assert abs(result_mean.item() - 0.3_36_20_38) < 1e-3

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          pass




import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
			import torch




@require_torch
@require_optimum
@slow
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoTokenizer.from_pretrained(lowercase	)
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			tokenizer('This is me'			,  return_tensors='pt'	)

				_snake_case		=			model.to_bettertransformer()

				self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

				_snake_case		=			model.generate(**lowercase	)

				_snake_case		=			model.reverse_bettertransformer()

				self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowercase	)

							_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

							self.assertFalse(
							    any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

							_snake_case		=			model_reloaded.generate(**lowercase	)
							self.assertTrue(torch.allclose(lowercase			,  lowercase	)	)





	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
							with self.assertRaises(lowercase	):
										model.save_pretrained(lowercase	)

							_snake_case		=			model.reverse_bettertransformer()
							model.save_pretrained(lowercase	)