import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    str  			=   (DDPMScheduler,)



   def   lowerCAmelCase						(	self					:  Any		,  **_lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= {
        '''num_train_timesteps''': 1_000,
        '''beta_start''': 0.0001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
        '''variance_type''': '''fixed_small''',
        '''clip_sample''': True,
    }

    config.update(**_lowercase							)
    return config



   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    for timesteps in [1, 5, 100, 1_000]:
     self.check_over_configs(num_train_timesteps=_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    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=_lowercase		,  beta_end=_lowercase							)



   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    for variance in ["fixed_small", "fixed_large", "other"]:
     self.check_over_configs(variance_type=_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    self.check_over_configs(thresholding=_lowercase							)
    for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
      self.check_over_configs(
          thresholding=_lowercase		,  prediction_type=_lowercase		,  sample_max_value=_lowercase		,  )



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    for t in [0, 500, 999]:
     self.check_over_forward(time_step=_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= self.scheduler_classes[0]
    _UpperCamelCase:     str							= self.get_scheduler_config()
    _UpperCamelCase:     Dict							= scheduler_class(**_lowercase							)

    assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.00979							)							) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.02							)							) < 1E-5



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= self.scheduler_classes[0]
    _UpperCamelCase:     int							= self.get_scheduler_config()
    _UpperCamelCase:     Union[str, Any]							= scheduler_class(**_lowercase							)

    _UpperCamelCase:     Dict							= len(_lowercase							)

    _UpperCamelCase:     Tuple							= self.dummy_model()
    _UpperCamelCase:     List[str]							= self.dummy_sample_deter
    _UpperCamelCase:     List[Any]							= torch.manual_seed(0							)

    for t in reversed(range(_lowercase							)							):
     # 1. predict noise residual
     _UpperCamelCase:     Union[str, Any]							= model(_lowercase		,  _lowercase							)

     # 2. predict previous mean of sample x_t-1
     _UpperCamelCase:     List[Any]							= scheduler.step(_lowercase		,  _lowercase		,  _lowercase		,  generator=_lowercase							).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     _UpperCamelCase:     List[Any]							= pred_prev_sample

    _UpperCamelCase:     Dict							= torch.sum(torch.abs(_lowercase							)							)
    _UpperCamelCase:     List[Any]							= torch.mean(torch.abs(_lowercase							)							)

    assert abs(result_sum.item() - 258.9606							) < 1E-2
    assert abs(result_mean.item() - 0.3372							) < 1E-3



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= self.scheduler_classes[0]
    _UpperCamelCase:     Dict							= self.get_scheduler_config(prediction_type='''v_prediction'''							)
    _UpperCamelCase:     Optional[Any]							= scheduler_class(**_lowercase							)

    _UpperCamelCase:     Union[str, Any]							= len(_lowercase							)

    _UpperCamelCase:     Union[str, Any]							= self.dummy_model()
    _UpperCamelCase:     Union[str, Any]							= self.dummy_sample_deter
    _UpperCamelCase:     Any							= torch.manual_seed(0							)

    for t in reversed(range(_lowercase							)							):
     # 1. predict noise residual
     _UpperCamelCase:     int							= model(_lowercase		,  _lowercase							)

     # 2. predict previous mean of sample x_t-1
     _UpperCamelCase:     Union[str, Any]							= scheduler.step(_lowercase		,  _lowercase		,  _lowercase		,  generator=_lowercase							).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     _UpperCamelCase:     int							= pred_prev_sample

    _UpperCamelCase:     Any							= torch.sum(torch.abs(_lowercase							)							)
    _UpperCamelCase:     List[str]							= torch.mean(torch.abs(_lowercase							)							)

    assert abs(result_sum.item() - 202.0296							) < 1E-2
    assert abs(result_mean.item() - 0.2631							) < 1E-3



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     int							= self.scheduler_classes[0]
    _UpperCamelCase:     List[Any]							= self.get_scheduler_config()
    _UpperCamelCase:     List[str]							= scheduler_class(**_lowercase							)

    _UpperCamelCase:     Any							= [100, 87, 50, 1, 0]

    scheduler.set_timesteps(timesteps=_lowercase							)

    _UpperCamelCase:     int							= scheduler.timesteps

    for i, timestep in enumerate(_lowercase							):
     if i == len(_lowercase							) - 1:
      _UpperCamelCase:     str							= -1
     else:
      _UpperCamelCase:     Optional[Any]							= timesteps[i + 1]

     _UpperCamelCase:     Any							= scheduler.previous_timestep(_lowercase							)
     _UpperCamelCase:     Optional[int]							= prev_t.item()

     self.assertEqual(_lowercase		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= self.scheduler_classes[0]
    _UpperCamelCase:     int							= self.get_scheduler_config()
    _UpperCamelCase:     Optional[int]							= scheduler_class(**_lowercase							)

    _UpperCamelCase:     Optional[Any]							= [100, 87, 50, 51, 0]

    with self.assertRaises(_lowercase		,  msg='''`custom_timesteps` must be in descending order.'''							):
     scheduler.set_timesteps(timesteps=_lowercase							)



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.scheduler_classes[0]
    _UpperCamelCase:     int							= self.get_scheduler_config()
    _UpperCamelCase:     Tuple							= scheduler_class(**_lowercase							)

    _UpperCamelCase:     Tuple							= [100, 87, 50, 1, 0]
    _UpperCamelCase:     Union[str, Any]							= len(_lowercase							)

    with self.assertRaises(_lowercase		,  msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''							):
     scheduler.set_timesteps(num_inference_steps=_lowercase		,  timesteps=_lowercase							)



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= self.scheduler_classes[0]
    _UpperCamelCase:     List[str]							= self.get_scheduler_config()
    _UpperCamelCase:     Optional[int]							= scheduler_class(**_lowercase							)

    _UpperCamelCase:     List[str]							= [scheduler.config.num_train_timesteps]

    with self.assertRaises(
        _lowercase		,  msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''		,  ):
     scheduler.set_timesteps(timesteps=_lowercase							)
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    '''split_dict'''  ,					[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42  ,					dataset_name='''my_dataset'''		)}		),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42		)}		),
        SplitDict({'''train''': SplitInfo()}		),
    ]  ,					)
def       lowerCAmelCase_     (		lowercase:							SplitDict		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= split_dict._to_yaml_list()
 assert len(lowercase		) == len(lowercase		)
 _UpperCamelCase:     Tuple							= SplitDict._from_yaml_list(lowercase		)
 for split_name, split_info in split_dict.items():
  # dataset_name field is deprecated, and is therefore not part of the YAML dump
  _UpperCamelCase:     Optional[Any]							= None
  # the split name of split_dict takes over the name of the split info object
  _UpperCamelCase:     Any							= split_name
 assert split_dict == reloaded





@pytest.mark.parametrize(
    '''split_info'''  ,					[SplitInfo(), SplitInfo(dataset_name=lowercase		), SplitInfo(dataset_name='''my_dataset'''		)]		)
def       lowerCAmelCase_     (		lowercase:							str		)    ->				Tuple:







 '''simple docstring'''
 # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
 # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
 _UpperCamelCase:     Optional[Any]							= asdict(SplitDict({'''train''': split_info}		)		)
 assert "dataset_name" in split_dict_asdict["train"]
 assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name