



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       _snake_case				(     lowerCamelCase_ ):






   '''simple docstring'''






   A__      : Optional[int]       =						(EulerAncestralDiscreteScheduler,)
   A__      : Optional[int]       =						10


   def       A__    (    self:     Union[str, Any]  ,**lowerCamelCase_:     Union[str, Any]							)	->      Optional[Any]:
       UpperCAmelCase_     :       Dict  		=					{
           """num_train_timesteps""": 1100,
           """beta_start""": 0.0_0_0_1,
           """beta_end""": 0.0_2,
           """beta_schedule""": """linear""",
       }

       config.update(**lowerCAmelCase_							)
       return config


   def       A__    (    self:     Optional[int]							)	->      Dict:
       for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=lowerCAmelCase_							)


   def       A__    (    self:     Union[str, Any]							)	->      List[str]:
       for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,[0.0_0_0_2, 0.0_0_2, 0.0_2]							):
           self.check_over_configs(beta_start=lowerCAmelCase_  ,beta_end=lowerCAmelCase_							)


   def       A__    (    self:     Any							)	->      Optional[int]:
       for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=lowerCAmelCase_							)


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


   def       A__    (    self:     int							)	->      List[Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					self.scheduler_classes[0]
       UpperCAmelCase_     :       List[str]  		=					self.get_scheduler_config()
       UpperCAmelCase_     :       List[str]  		=					scheduler_class(**lowerCAmelCase_							)

       scheduler.set_timesteps(self.num_inference_steps							)

       UpperCAmelCase_     :       int  		=					torch.manual_seed(0							)

       UpperCAmelCase_     :       Dict  		=					self.dummy_model()
       UpperCAmelCase_     :       int  		=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCAmelCase_     :       str  		=					sample.to(lowerCAmelCase_							)

       for i, t in enumerate(scheduler.timesteps							):
           UpperCAmelCase_     :       Optional[Any]  		=					scheduler.scale_model_input(lowerCAmelCase_  ,lowerCAmelCase_							)

           UpperCAmelCase_     :       List[str]  		=					model(lowerCAmelCase_  ,lowerCAmelCase_							)

           UpperCAmelCase_     :       Any  		=					scheduler.step(lowerCAmelCase_  ,lowerCAmelCase_  ,lowerCAmelCase_  ,generator=lowerCAmelCase_							)
           UpperCAmelCase_     :       List[Any]  		=					output.prev_sample

       UpperCAmelCase_     :       Optional[int]  		=					torch.sum(torch.abs(lowerCAmelCase_							)							)
       UpperCAmelCase_     :       Tuple  		=					torch.mean(torch.abs(lowerCAmelCase_							)							)

       assert abs(result_sum.item() - 152.3192							) < 1e-2
       assert abs(result_mean.item() - 0.1_9_8_3							) < 1e-3


   def       A__    (    self:     str							)	->      Optional[Any]:
       UpperCAmelCase_     :       List[str]  		=					self.scheduler_classes[0]
       UpperCAmelCase_     :       int  		=					self.get_scheduler_config(prediction_type="""v_prediction"""							)
       UpperCAmelCase_     :       Union[str, Any]  		=					scheduler_class(**lowerCAmelCase_							)

       scheduler.set_timesteps(self.num_inference_steps							)

       UpperCAmelCase_     :       Union[str, Any]  		=					torch.manual_seed(0							)

       UpperCAmelCase_     :       Dict  		=					self.dummy_model()
       UpperCAmelCase_     :       List[str]  		=					self.dummy_sample_deter * scheduler.init_noise_sigma
       UpperCAmelCase_     :       Tuple  		=					sample.to(lowerCAmelCase_							)

       for i, t in enumerate(scheduler.timesteps							):
           UpperCAmelCase_     :       Tuple  		=					scheduler.scale_model_input(lowerCAmelCase_  ,lowerCAmelCase_							)

           UpperCAmelCase_     :       List[str]  		=					model(lowerCAmelCase_  ,lowerCAmelCase_							)

           UpperCAmelCase_     :       Optional[int]  		=					scheduler.step(lowerCAmelCase_  ,lowerCAmelCase_  ,lowerCAmelCase_  ,generator=lowerCAmelCase_							)
           UpperCAmelCase_     :       Optional[Any]  		=					output.prev_sample

       UpperCAmelCase_     :       List[Any]  		=					torch.sum(torch.abs(lowerCAmelCase_							)							)
       UpperCAmelCase_     :       Tuple  		=					torch.mean(torch.abs(lowerCAmelCase_							)							)

       assert abs(result_sum.item() - 108.4439							) < 1e-2
       assert abs(result_mean.item() - 0.1_4_1_2							) < 1e-3






   def       A__    (    self:     Optional[Any]							)	->      Tuple:
       UpperCAmelCase_     :       Tuple  		=					self.scheduler_classes[0]
       UpperCAmelCase_     :       Union[str, Any]  		=					self.get_scheduler_config()
       UpperCAmelCase_     :       Dict  		=					scheduler_class(**lowerCAmelCase_							)

       scheduler.set_timesteps(self.num_inference_steps  ,device=lowerCAmelCase_							)
       UpperCAmelCase_     :       Any  		=					torch.manual_seed(0							)

       UpperCAmelCase_     :       Dict  		=					self.dummy_model()
       UpperCAmelCase_     :       Dict  		=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCAmelCase_     :       List[Any]  		=					sample.to(lowerCAmelCase_							)

       for t in scheduler.timesteps:
           UpperCAmelCase_     :       Dict  		=					scheduler.scale_model_input(lowerCAmelCase_  ,lowerCAmelCase_							)

           UpperCAmelCase_     :       Dict  		=					model(lowerCAmelCase_  ,lowerCAmelCase_							)

           UpperCAmelCase_     :       Dict  		=					scheduler.step(lowerCAmelCase_  ,lowerCAmelCase_  ,lowerCAmelCase_  ,generator=lowerCAmelCase_							)
           UpperCAmelCase_     :       int  		=					output.prev_sample

       UpperCAmelCase_     :       List[str]  		=					torch.sum(torch.abs(lowerCAmelCase_							)							)
       UpperCAmelCase_     :       Optional[Any]  		=					torch.mean(torch.abs(lowerCAmelCase_							)							)

       assert abs(result_sum.item() - 152.3192							) < 1e-2
       assert abs(result_mean.item() - 0.1_9_8_3							) < 1e-3

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__  	=      {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      ["ImageGPTFeatureExtractor"]
						lowerCAmelCase__  	=      ["ImageGPTImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      [
						    "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ImageGPTForCausalImageModeling",
						    "ImageGPTForImageClassification",
						    "ImageGPTModel",
						    "ImageGPTPreTrainedModel",
						    "load_tf_weights_in_imagegpt",
						]


if TYPE_CHECKING:
						from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
												from .image_processing_imagegpt import ImageGPTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_imagegpt import (
												    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ImageGPTForCausalImageModeling,
												    ImageGPTForImageClassification,
												    ImageGPTModel,
												    ImageGPTPreTrainedModel,
												    load_tf_weights_in_imagegpt,
												)

else:
						import sys

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




