




"""simple docstring"""

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Union[str, Any]				  =     (DDIMParallelScheduler,)
 UpperCAmelCase   :					List[Any]				  =     (('''eta''', 0.0), ('''num_inference_steps''', 50))

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							**_UpperCAmelCase	:	Dict		):
       _A        =       {
           'num_train_timesteps': 1_000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'clip_sample': True,
       }

       config.update(**_UpperCAmelCase		)
       return config

 def       lowerCAmelCase_							(			self	:	int   ,							**_UpperCAmelCase	:	List[str]		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
       _A        =       scheduler_class(**_UpperCAmelCase		)

       _A       ,   _A        =       10, 0.0

       _A        =       self.dummy_model()
       _A        =       self.dummy_sample_deter

       scheduler.set_timesteps(_UpperCAmelCase		)

       for t in scheduler.timesteps:
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

       return sample

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       for timesteps in [100, 500, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	int		):
       for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=_UpperCAmelCase		)

       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config(steps_offset=1		)
       _A        =       scheduler_class(**_UpperCAmelCase		)
       scheduler.set_timesteps(5		)
       assert torch.equal(scheduler.timesteps   ,							torch.LongTensor([801, 601, 401, 201, 1]		)		)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,							[0.002, 0.02, 0.2, 2]		):
             self.check_over_configs(beta_start=_UpperCAmelCase   ,							beta_end=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Dict		):
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_UpperCAmelCase		)

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

 def       lowerCAmelCase_							(			self	:	int		):
       for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	int		):
       self.check_over_configs(thresholding=_UpperCAmelCase		)
       for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=_UpperCAmelCase   ,							prediction_type=_UpperCAmelCase   ,							sample_max_value=_UpperCAmelCase   ,							)

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       for t in [1, 10, 49]:
             self.check_over_forward(time_step=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	int		):
       for t, num_inference_steps in zip([1, 10, 50]   ,							[10, 50, 500]		):
             self.check_over_forward(time_step=_UpperCAmelCase   ,							num_inference_steps=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str		):
       for t, eta in zip([1, 10, 49]   ,							[0.0, 0.5, 1.0]		):
             self.check_over_forward(time_step=_UpperCAmelCase   ,							eta=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config()
       _A        =       scheduler_class(**_UpperCAmelCase		)

       assert torch.sum(torch.abs(scheduler._get_variance(0   ,							0		) - 0.0		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(420   ,							400		) - 0.1_4771		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(980   ,							960		) - 0.3_2460		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(0   ,							0		) - 0.0		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487   ,							486		) - 0.0_0979		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999   ,							998		) - 0.02		)		) < 1E-5

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config()
       _A        =       scheduler_class(**_UpperCAmelCase		)

       _A       ,   _A        =       10, 0.0
       scheduler.set_timesteps(_UpperCAmelCase		)

       _A        =       self.dummy_model()
       _A        =       self.dummy_sample_deter
       _A        =       self.dummy_sample_deter + 0.1
       _A        =       self.dummy_sample_deter - 0.1

       _A        =       samplea.shape[0]
       _A        =       torch.stack([samplea, samplea, samplea]   ,							dim=0		)
       _A        =       torch.arange(_UpperCAmelCase		)[0:3, None].repeat(1   ,							_UpperCAmelCase		)

       _A        =       model(samples.flatten(0   ,							1		)   ,							timesteps.flatten(0   ,							1		)		)
       _A        =       scheduler.batch_step_no_noise(_UpperCAmelCase   ,							timesteps.flatten(0   ,							1		)   ,							samples.flatten(0   ,							1		)   ,							_UpperCAmelCase		)

       _A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_sum.item() - 1147.7904		) < 1E-2
       assert abs(result_mean.item() - 0.4982		) < 1E-3

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       self.full_loop()

       _A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_sum.item() - 172.0067		) < 1E-2
       assert abs(result_mean.item() - 0.22_3967		) < 1E-3

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       self.full_loop(prediction_type='v_prediction'		)

       _A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_sum.item() - 52.5302		) < 1E-2
       assert abs(result_mean.item() - 0.0684		) < 1E-3

 def       lowerCAmelCase_							(			self	:	List[str]		):
       # We specify different beta, so that the first alpha is 0.99
       _A        =       self.full_loop(set_alpha_to_one=_UpperCAmelCase   ,							beta_start=0.01		)
       _A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_sum.item() - 149.8295		) < 1E-2
       assert abs(result_mean.item() - 0.1951		) < 1E-3



 def       lowerCAmelCase_							(			self	:	List[str]		):
       # We specify different beta, so that the first alpha is 0.99
       _A        =       self.full_loop(set_alpha_to_one=_UpperCAmelCase   ,							beta_start=0.01		)
       _A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_sum.item() - 149.0784		) < 1E-2
       assert abs(result_mean.item() - 0.1941		) < 1E-3







"""simple docstring"""

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


a         =      HUGGINGFACE_HUB_CACHE


a         =      '''config.json'''
a         =      '''diffusion_pytorch_model.bin'''
a         =      '''diffusion_flax_model.msgpack'''
a         =      '''model.onnx'''
a         =      '''diffusion_pytorch_model.safetensors'''
a         =      '''weights.pb'''
a         =      '''https://huggingface.co'''
a         =      default_cache_path
a         =      '''diffusers_modules'''
a         =      os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
a         =      ['''fp16''', '''non-ema''']
a         =      '''.self_attn'''

