



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    int									=  (UnCLIPScheduler,)



					def    A__		(			self    ,	**lowerCAmelCase     ):
							UpperCAmelCase_						=				{
							    "num_train_timesteps": 1000,
							    "variance_type": "fixed_small_log",
							    "clip_sample": True,
							    "clip_sample_range": 1.0,
							    "prediction_type": "epsilon",
							}

							config.update(**_UpperCAmelCase     )
							return config



					def    A__		(			self     ):




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



					def    A__		(			self     ):




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



					def    A__		(			self     ):




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



					def    A__		(			self     ):




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



					def    A__		(			self     ):




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



					def    A__		(			self     ):




							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     ):
							UpperCAmelCase_						=				self.scheduler_classes[0]
							UpperCAmelCase_						=				self.get_scheduler_config(variance_type="fixed_small_log"     )
							UpperCAmelCase_						=				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     ):
							UpperCAmelCase_						=				self.scheduler_classes[0]
							UpperCAmelCase_						=				self.get_scheduler_config(variance_type="learned_range"     )
							UpperCAmelCase_						=				scheduler_class(**_UpperCAmelCase     )

							UpperCAmelCase_						=				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     ):
							UpperCAmelCase_						=				self.scheduler_classes[0]
							UpperCAmelCase_						=				self.get_scheduler_config()
							UpperCAmelCase_						=				scheduler_class(**_UpperCAmelCase     )

							UpperCAmelCase_						=				scheduler.timesteps

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

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

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

									UpperCAmelCase_						=				pred_prev_sample

							UpperCAmelCase_						=				torch.sum(torch.abs(_UpperCAmelCase     )     )
							UpperCAmelCase_						=				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     ):
							UpperCAmelCase_						=				self.scheduler_classes[0]
							UpperCAmelCase_						=				self.get_scheduler_config()
							UpperCAmelCase_						=				scheduler_class(**_UpperCAmelCase     )

							scheduler.set_timesteps(25     )

							UpperCAmelCase_						=				scheduler.timesteps

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

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

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

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

									UpperCAmelCase_						=				pred_prev_sample

							UpperCAmelCase_						=				torch.sum(torch.abs(_UpperCAmelCase     )     )
							UpperCAmelCase_						=				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     ):
							pass



					def    A__		(			self     ):
							pass






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

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

