



'''simple docstring'''



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Dict							= (EulerDiscreteScheduler,)
				__magic_name__       :   str							= 10




				def   a__(	self    :				List[str]   ,					**lowerCAmelCase    :				Any   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			{
										    '''num_train_timesteps''': 1100,
										    '''beta_start''': 0.0001,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										}

										config.update(**lowerCAmelCase   )
										return config




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

										"""simple docstring"""




										for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=lowerCAmelCase   )




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

										"""simple docstring"""




										for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]   ,					[0.0002, 0.002, 0.02]   ):
																self.check_over_configs(beta_start=lowerCAmelCase   ,					beta_end=lowerCAmelCase   )




				def   a__(	self    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=lowerCAmelCase   )




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

										"""simple docstring"""




										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=lowerCAmelCase   )




				def   a__(	self    :				Dict   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config()
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   )

										scheduler.set_timesteps(self.num_inference_steps   )

										UpperCAmelCase         =			torch.manual_seed(0   )

										UpperCAmelCase         =			self.dummy_model()
										UpperCAmelCase         =			self.dummy_sample_deter * scheduler.init_noise_sigma
										UpperCAmelCase         =			sample.to(lowerCAmelCase   )

										for i, t in enumerate(scheduler.timesteps   ):
																UpperCAmelCase         =			scheduler.scale_model_input(lowerCAmelCase   ,					lowerCAmelCase   )

																UpperCAmelCase         =			model(lowerCAmelCase   ,					lowerCAmelCase   )

																UpperCAmelCase         =			scheduler.step(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					generator=lowerCAmelCase   )
																UpperCAmelCase         =			output.prev_sample

										UpperCAmelCase         =			torch.sum(torch.abs(lowerCAmelCase   )   )
										UpperCAmelCase         =			torch.mean(torch.abs(lowerCAmelCase   )   )

										assert abs(result_sum.item() - 10.0807   ) < 1E-2
										assert abs(result_mean.item() - 0.0131   ) < 1E-3




				def   a__(	self    :				Optional[int]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config(prediction_type='''v_prediction'''   )
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   )

										scheduler.set_timesteps(self.num_inference_steps   )

										UpperCAmelCase         =			torch.manual_seed(0   )

										UpperCAmelCase         =			self.dummy_model()
										UpperCAmelCase         =			self.dummy_sample_deter * scheduler.init_noise_sigma
										UpperCAmelCase         =			sample.to(lowerCAmelCase   )

										for i, t in enumerate(scheduler.timesteps   ):
																UpperCAmelCase         =			scheduler.scale_model_input(lowerCAmelCase   ,					lowerCAmelCase   )

																UpperCAmelCase         =			model(lowerCAmelCase   ,					lowerCAmelCase   )

																UpperCAmelCase         =			scheduler.step(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					generator=lowerCAmelCase   )
																UpperCAmelCase         =			output.prev_sample

										UpperCAmelCase         =			torch.sum(torch.abs(lowerCAmelCase   )   )
										UpperCAmelCase         =			torch.mean(torch.abs(lowerCAmelCase   )   )

										assert abs(result_sum.item() - 0.0002   ) < 1E-2
										assert abs(result_mean.item() - 2.2676E-06   ) < 1E-3




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

										"""simple docstring"""




										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config()
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   )

										scheduler.set_timesteps(self.num_inference_steps   ,					device=lowerCAmelCase   )

										UpperCAmelCase         =			torch.manual_seed(0   )

										UpperCAmelCase         =			self.dummy_model()
										UpperCAmelCase         =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										UpperCAmelCase         =			sample.to(lowerCAmelCase   )

										for t in scheduler.timesteps:
																UpperCAmelCase         =			scheduler.scale_model_input(lowerCAmelCase   ,					lowerCAmelCase   )

																UpperCAmelCase         =			model(lowerCAmelCase   ,					lowerCAmelCase   )

																UpperCAmelCase         =			scheduler.step(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					generator=lowerCAmelCase   )
																UpperCAmelCase         =			output.prev_sample

										UpperCAmelCase         =			torch.sum(torch.abs(lowerCAmelCase   )   )
										UpperCAmelCase         =			torch.mean(torch.abs(lowerCAmelCase   )   )

										assert abs(result_sum.item() - 10.0807   ) < 1E-2
										assert abs(result_mean.item() - 0.0131   ) < 1E-3





				def   a__(	self    :				Optional[Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config()
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   ,					use_karras_sigmas=lowerCAmelCase   )

										scheduler.set_timesteps(self.num_inference_steps   ,					device=lowerCAmelCase   )

										UpperCAmelCase         =			torch.manual_seed(0   )

										UpperCAmelCase         =			self.dummy_model()
										UpperCAmelCase         =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										UpperCAmelCase         =			sample.to(lowerCAmelCase   )

										for t in scheduler.timesteps:
																UpperCAmelCase         =			scheduler.scale_model_input(lowerCAmelCase   ,					lowerCAmelCase   )

																UpperCAmelCase         =			model(lowerCAmelCase   ,					lowerCAmelCase   )

																UpperCAmelCase         =			scheduler.step(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					generator=lowerCAmelCase   )
																UpperCAmelCase         =			output.prev_sample

										UpperCAmelCase         =			torch.sum(torch.abs(lowerCAmelCase   )   )
										UpperCAmelCase         =			torch.mean(torch.abs(lowerCAmelCase   )   )

										assert abs(result_sum.item() - 124.52299499511719   ) < 1E-2
										assert abs(result_mean.item() - 0.16213932633399963   ) < 1E-3









'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase    :    Any			     =  {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Optional[int]			     =  [
	    """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

	_lowercase    :    Tuple			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





