

import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Optional[int]														=					(EulerDiscreteScheduler,)
    __lowerCamelCase						:	Optional[Any]														=					10





    def 				snake_case_    (				self						,       **a):
         lowercase__   :       Optional[Any]				=	{
             'num_train_timesteps': 1100,
             'beta_start': 0.0_001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**a)
         return config





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





    def 				snake_case_    (				self):
         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,       [0.0_002, 0.002, 0.02]):
              self.check_over_configs(beta_start=a						,       beta_end=a)





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





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





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.scheduler_classes[0]
         lowercase__   :       int				=	self.get_scheduler_config()
         lowercase__   :       int				=	scheduler_class(**a)

         scheduler.set_timesteps(self.num_inference_steps)

         lowercase__   :       Any				=	torch.manual_seed(0)

         lowercase__   :       int				=	self.dummy_model()
         lowercase__   :       int				=	self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase__   :       Optional[int]				=	sample.to(a)

         for i, t in enumerate(scheduler.timesteps):
              lowercase__   :       Any				=	scheduler.scale_model_input(a						,       a)

              lowercase__   :       str				=	model(a						,       a)

              lowercase__   :       Tuple				=	scheduler.step(a						,       a						,       a						,       generator=a)
              lowercase__   :       List[Any]				=	output.prev_sample

         lowercase__   :       Any				=	torch.sum(torch.abs(a))
         lowercase__   :       Optional[Any]				=	torch.mean(torch.abs(a))

         assert abs(result_sum.item() - 10.0_807) < 1e-2
         assert abs(result_mean.item() - 0.0_131) < 1e-3





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	self.scheduler_classes[0]
         lowercase__   :       Dict				=	self.get_scheduler_config(prediction_type='v_prediction')
         lowercase__   :       str				=	scheduler_class(**a)

         scheduler.set_timesteps(self.num_inference_steps)

         lowercase__   :       Optional[int]				=	torch.manual_seed(0)

         lowercase__   :       str				=	self.dummy_model()
         lowercase__   :       List[Any]				=	self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase__   :       Any				=	sample.to(a)

         for i, t in enumerate(scheduler.timesteps):
              lowercase__   :       Any				=	scheduler.scale_model_input(a						,       a)

              lowercase__   :       Union[str, Any]				=	model(a						,       a)

              lowercase__   :       str				=	scheduler.step(a						,       a						,       a						,       generator=a)
              lowercase__   :       List[str]				=	output.prev_sample

         lowercase__   :       str				=	torch.sum(torch.abs(a))
         lowercase__   :       str				=	torch.mean(torch.abs(a))

         assert abs(result_sum.item() - 0.0_002) < 1e-2
         assert abs(result_mean.item() - 2.2676e-06) < 1e-3





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	self.scheduler_classes[0]
         lowercase__   :       int				=	self.get_scheduler_config()
         lowercase__   :       List[Any]				=	scheduler_class(**a)

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

         lowercase__   :       Optional[int]				=	torch.manual_seed(0)

         lowercase__   :       Optional[int]				=	self.dummy_model()
         lowercase__   :       Optional[Any]				=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         lowercase__   :       Optional[int]				=	sample.to(a)

         for t in scheduler.timesteps:
              lowercase__   :       str				=	scheduler.scale_model_input(a						,       a)

              lowercase__   :       Dict				=	model(a						,       a)

              lowercase__   :       List[str]				=	scheduler.step(a						,       a						,       a						,       generator=a)
              lowercase__   :       Any				=	output.prev_sample

         lowercase__   :       Optional[int]				=	torch.sum(torch.abs(a))
         lowercase__   :       str				=	torch.mean(torch.abs(a))

         assert abs(result_sum.item() - 10.0_807) < 1e-2
         assert abs(result_mean.item() - 0.0_131) < 1e-3





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	self.scheduler_classes[0]
         lowercase__   :       Optional[Any]				=	self.get_scheduler_config()
         lowercase__   :       Any				=	scheduler_class(**a						,       use_karras_sigmas=a)

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

         lowercase__   :       Tuple				=	torch.manual_seed(0)

         lowercase__   :       Any				=	self.dummy_model()
         lowercase__   :       Union[str, Any]				=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         lowercase__   :       Optional[Any]				=	sample.to(a)

         for t in scheduler.timesteps:
              lowercase__   :       Any				=	scheduler.scale_model_input(a						,       a)

              lowercase__   :       int				=	model(a						,       a)

              lowercase__   :       Optional[int]				=	scheduler.step(a						,       a						,       a						,       generator=a)
              lowercase__   :       Union[str, Any]				=	output.prev_sample

         lowercase__   :       int				=	torch.sum(torch.abs(a))
         lowercase__   :       Optional[int]				=	torch.mean(torch.abs(a))

         assert abs(result_sum.item() - 124.52_299_499_511_719) < 1e-2
         assert abs(result_mean.item() - 0.16_213_932_633_399_963) < 1e-3




import os

try:
       from .build_directory_md import good_file_paths
except ImportError:
       from build_directory_md import good_file_paths  # type: ignore

snake_case_        =			list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

snake_case_        =			[file for file in filepaths if file != file.lower()]
if upper_files:
       print(F'''{len(upper_files)} files contain uppercase characters:''')
       print('''\n'''.join(upper_files) + '''\n''')

snake_case_        =			[file for file in filepaths if ''' ''' in file]
if space_files:
       print(F'''{len(space_files)} files contain space characters:''')
       print('''\n'''.join(space_files) + '''\n''')

snake_case_        =			[file for file in filepaths if '''-''' in file]
if hyphen_files:
       print(F'''{len(hyphen_files)} files contain hyphen characters:''')
       print('''\n'''.join(hyphen_files) + '''\n''')

snake_case_        =			[file for file in filepaths if os.sep not in file]
if nodir_files:
       print(F'''{len(nodir_files)} files are not in a directory:''')
       print('''\n'''.join(nodir_files) + '''\n''')

snake_case_        =			len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
       import sys

       sys.exit(bad_files)

