
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	=(UnCLIPScheduler,)




				def   __a		(      self       ,       **_UpperCamelCase  )       ->   Tuple:
							lowerCAmelCase_      			=			{
							    "num_train_timesteps": 1_000,
							    "variance_type": "fixed_small_log",
							    "clip_sample": True,
							    "clip_sample_range": 1.0,
							    "prediction_type": "epsilon",
							}

							config.update(**_UpperCamelCase  )
							return config




				def   __a		(      self  )       ->   Tuple:
							for timesteps in [1, 5, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase  )




				def   __a		(      self  )       ->   Any:
							for variance in ["fixed_small_log", "learned_range"]:
										self.check_over_configs(variance_type=_UpperCamelCase  )




				def   __a		(      self  )       ->   str:
							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=_UpperCamelCase  )




				def   __a		(      self  )       ->   List[Any]:
							for clip_sample_range in [1, 5, 10, 20]:
										self.check_over_configs(clip_sample_range=_UpperCamelCase  )




				def   __a		(      self  )       ->   Dict:
							for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(prediction_type=_UpperCamelCase  )




				def   __a		(      self  )       ->   List[str]:
							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=_UpperCamelCase       ,       prev_timestep=_UpperCamelCase  )




				def   __a		(      self  )       ->   int:
							lowerCAmelCase_      			=			self.scheduler_classes[0]
							lowerCAmelCase_      			=			self.get_scheduler_config(variance_type="fixed_small_log"  )
							lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )

							assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 1.0_0_0_0e-1_0  )  ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0549625  )  ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.9994987  )  ) < 1e-5




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			self.scheduler_classes[0]
							lowerCAmelCase_      			=			self.get_scheduler_config(variance_type="learned_range"  )
							lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )

							lowerCAmelCase_      			=			0.5

							assert scheduler._get_variance(1       ,       predicted_variance=_UpperCamelCase  ) - -10.1712790 < 1e-5
							assert scheduler._get_variance(487       ,       predicted_variance=_UpperCamelCase  ) - -5.7998052 < 1e-5
							assert scheduler._get_variance(999       ,       predicted_variance=_UpperCamelCase  ) - -0.0010011 < 1e-5




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			self.scheduler_classes[0]
							lowerCAmelCase_      			=			self.get_scheduler_config()
							lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )

							lowerCAmelCase_      			=			scheduler.timesteps

							lowerCAmelCase_      			=			self.dummy_model()
							lowerCAmelCase_      			=			self.dummy_sample_deter
							lowerCAmelCase_      			=			torch.manual_seed(0  )

							for i, t in enumerate(_UpperCamelCase  ):
										# 1. predict noise residual
										lowerCAmelCase_      			=			model(_UpperCamelCase       ,       _UpperCamelCase  )

										# 2. predict previous mean of sample x_t-1
										lowerCAmelCase_      			=			scheduler.step(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       generator=_UpperCamelCase  ).prev_sample

										lowerCAmelCase_      			=			pred_prev_sample

							lowerCAmelCase_      			=			torch.sum(torch.abs(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_sum.item() - 252.2682495  ) < 1e-2
							assert abs(result_mean.item() - 0.3284743  ) < 1e-3




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.scheduler_classes[0]
							lowerCAmelCase_      			=			self.get_scheduler_config()
							lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )

							scheduler.set_timesteps(25  )

							lowerCAmelCase_      			=			scheduler.timesteps

							lowerCAmelCase_      			=			self.dummy_model()
							lowerCAmelCase_      			=			self.dummy_sample_deter
							lowerCAmelCase_      			=			torch.manual_seed(0  )

							for i, t in enumerate(_UpperCamelCase  ):
										# 1. predict noise residual
										lowerCAmelCase_      			=			model(_UpperCamelCase       ,       _UpperCamelCase  )

										if i + 1 == timesteps.shape[0]:
													lowerCAmelCase_      			=			None
										else:
													lowerCAmelCase_      			=			timesteps[i + 1]

										# 2. predict previous mean of sample x_t-1
										lowerCAmelCase_      			=			scheduler.step(
										    _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       prev_timestep=_UpperCamelCase       ,       generator=_UpperCamelCase  ).prev_sample

										lowerCAmelCase_      			=			pred_prev_sample

							lowerCAmelCase_      			=			torch.sum(torch.abs(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_sum.item() - 258.2044983  ) < 1e-2
							assert abs(result_mean.item() - 0.3362038  ) < 1e-3




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




				def   __a		(      self  )       ->   Any:
							pass





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A     =  {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  ["NllbTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  ["NllbTokenizerFast"]


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb import NllbTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb_fast import NllbTokenizerFast

else:
			import sys

			_A     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



