




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Optional[int]   =(DDIMScheduler,)
     _snake_case:		List[Any]   =(("eta", 0.0), ("num_inference_steps", 50))




     def        __snake_case      (			self     :      List[str]		,     **A__     :      Optional[int]	) ->				Dict:
           lowerCamelCase__					     = {
               'num_train_timesteps': 1_0_0_0,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
               'clip_sample': True,
           }

           config.update(**A__	)
           return config




     def        __snake_case      (			self     :      Dict		,     **A__     :      Any	) ->				List[Any]:
           lowerCamelCase__					     = self.scheduler_classes[0]
           lowerCamelCase__					     = self.get_scheduler_config(**A__	)
           lowerCamelCase__					     = scheduler_class(**A__	)

           lowerCamelCase__     ,						lowerCamelCase__					     = 1_0, 0.0

           lowerCamelCase__					     = self.dummy_model()
           lowerCamelCase__					     = self.dummy_sample_deter

           scheduler.set_timesteps(A__	)

           for t in scheduler.timesteps:
                 lowerCamelCase__					     = model(A__		,     A__	)
                 lowerCamelCase__					     = scheduler.step(A__		,     A__		,     A__		,     A__	).prev_sample

           return sample




     def        __snake_case      (			self     :      str	) ->				List[str]:
           for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=A__	)




     def        __snake_case      (			self     :      Dict	) ->				Optional[Any]:
           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=A__	)

           lowerCamelCase__					     = self.scheduler_classes[0]
           lowerCamelCase__					     = self.get_scheduler_config(steps_offset=1	)
           lowerCamelCase__					     = scheduler_class(**A__	)
           scheduler.set_timesteps(5	)
           assert torch.equal(scheduler.timesteps		,     torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]	)	)




     def        __snake_case      (			self     :      Optional[Any]	) ->				Any:
           for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,     [0.002, 0.02, 0.2, 2]	):
                 self.check_over_configs(beta_start=A__		,     beta_end=A__	)




     def        __snake_case      (			self     :      int	) ->				Dict:
           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=A__	)




     def        __snake_case      (			self     :      List[str]	) ->				Optional[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=A__	)




     def        __snake_case      (			self     :      List[str]	) ->				str:
           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=A__	)




     def        __snake_case      (			self     :      Optional[Any]	) ->				List[str]:
           for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=A__	)




     def        __snake_case      (			self     :      Any	) ->				Optional[int]:
           for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=A__	)




     def        __snake_case      (			self     :      str	) ->				Optional[int]:
           self.check_over_configs(thresholding=A__	)
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=A__		,     prediction_type=A__		,     sample_max_value=A__		,     )




     def        __snake_case      (			self     :      Optional[Any]	) ->				Any:
           for t in [1, 1_0, 4_9]:
                 self.check_over_forward(time_step=A__	)




     def        __snake_case      (			self     :      Union[str, Any]	) ->				Tuple:
           for t, num_inference_steps in zip([1, 1_0, 5_0]		,     [1_0, 5_0, 5_0_0]	):
                 self.check_over_forward(time_step=A__		,     num_inference_steps=A__	)




     def        __snake_case      (			self     :      List[Any]	) ->				int:
           for t, eta in zip([1, 1_0, 4_9]		,     [0.0, 0.5, 1.0]	):
                 self.check_over_forward(time_step=A__		,     eta=A__	)




     def        __snake_case      (			self     :      Dict	) ->				Union[str, Any]:
           lowerCamelCase__					     = self.scheduler_classes[0]
           lowerCamelCase__					     = self.get_scheduler_config()
           lowerCamelCase__					     = scheduler_class(**A__	)

           assert torch.sum(torch.abs(scheduler._get_variance(0		,     0	) - 0.0	)	) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_2_0		,     4_0_0	) - 0.14771	)	) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_8_0		,     9_6_0	) - 0.32460	)	) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(0		,     0	) - 0.0	)	) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_8_7		,     4_8_6	) - 0.00979	)	) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_9_9		,     9_9_8	) - 0.02	)	) < 1E-5




     def        __snake_case      (			self     :      int	) ->				List[Any]:
           lowerCamelCase__					     = self.full_loop()

           lowerCamelCase__					     = torch.sum(torch.abs(A__	)	)
           lowerCamelCase__					     = torch.mean(torch.abs(A__	)	)

           assert abs(result_sum.item() - 172.0067	) < 1E-2
           assert abs(result_mean.item() - 0.223967	) < 1E-3




     def        __snake_case      (			self     :      str	) ->				Optional[int]:
           lowerCamelCase__					     = self.full_loop(prediction_type='v_prediction'	)

           lowerCamelCase__					     = torch.sum(torch.abs(A__	)	)
           lowerCamelCase__					     = torch.mean(torch.abs(A__	)	)

           assert abs(result_sum.item() - 52.5302	) < 1E-2
           assert abs(result_mean.item() - 0.0684	) < 1E-3




     def        __snake_case      (			self     :      Tuple	) ->				List[Any]:
           # We specify different beta, so that the first alpha is 0.99
           lowerCamelCase__					     = self.full_loop(set_alpha_to_one=A__		,     beta_start=0.01	)
           lowerCamelCase__					     = torch.sum(torch.abs(A__	)	)
           lowerCamelCase__					     = torch.mean(torch.abs(A__	)	)

           assert abs(result_sum.item() - 149.8295	) < 1E-2
           assert abs(result_mean.item() - 0.1951	) < 1E-3




     def        __snake_case      (			self     :      int	) ->				Tuple:
           # We specify different beta, so that the first alpha is 0.99
           lowerCamelCase__					     = self.full_loop(set_alpha_to_one=A__		,     beta_start=0.01	)
           lowerCamelCase__					     = torch.sum(torch.abs(A__	)	)
           lowerCamelCase__					     = torch.mean(torch.abs(A__	)	)

           assert abs(result_sum.item() - 149.0784	) < 1E-2
           assert abs(result_mean.item() - 0.1941	) < 1E-3





import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @cached_property
     def        __snake_case      (			self     :      Union[str, Any]	) ->				str:
           return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'	)




     @cached_property
     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'	)




     def        __snake_case      (			self     :      List[str]	) ->				int:
           lowerCamelCase__					     = self.tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      List[str]	) ->				str:
           lowerCamelCase__					     = self.rust_tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      Any	) ->				str:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]




     def        __snake_case      (			self     :      str	) ->				Union[str, Any]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]