




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = (DDPMScheduler,)


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					**UpperCAmelCase__ :      Union[str, Any])		->Any:





           '''simple docstring'''

           A__ 					=       {
               '''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(**UpperCAmelCase__)
           return config


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->List[Any]:





           '''simple docstring'''

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


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





           '''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=UpperCAmelCase__   ,					beta_end=UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->Dict:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Dict:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->List[Any]:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any])		->Tuple:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->str:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Union[str, Any]:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Union[str, Any]:





           '''simple docstring'''

           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)) < 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 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->List[Any]:





           '''simple docstring'''

           A__ 					=       self.scheduler_classes[0]
           A__ 					=       self.get_scheduler_config()
           A__ 					=       scheduler_class(**UpperCAmelCase__)

           A__ 					=       len(UpperCAmelCase__)

           A__ 					=       self.dummy_model()
           A__ 					=       self.dummy_sample_deter
           A__ 					=       torch.manual_seed(0)

           for t in reversed(range(UpperCAmelCase__)):
                  # 1. predict noise residual
                  A__ 					=       model(UpperCAmelCase__   ,					UpperCAmelCase__)

                  # 2. predict previous mean of sample x_t-1
                  A__ 					=       scheduler.step(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__   ,					generator=UpperCAmelCase__).prev_sample

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

           A__ 					=       torch.sum(torch.abs(UpperCAmelCase__))
           A__ 					=       torch.mean(torch.abs(UpperCAmelCase__))

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


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->int:





           '''simple docstring'''

           A__ 					=       self.scheduler_classes[0]
           A__ 					=       self.get_scheduler_config(prediction_type='''v_prediction''')
           A__ 					=       scheduler_class(**UpperCAmelCase__)

           A__ 					=       len(UpperCAmelCase__)

           A__ 					=       self.dummy_model()
           A__ 					=       self.dummy_sample_deter
           A__ 					=       torch.manual_seed(0)

           for t in reversed(range(UpperCAmelCase__)):
                  # 1. predict noise residual
                  A__ 					=       model(UpperCAmelCase__   ,					UpperCAmelCase__)

                  # 2. predict previous mean of sample x_t-1
                  A__ 					=       scheduler.step(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__   ,					generator=UpperCAmelCase__).prev_sample

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

           A__ 					=       torch.sum(torch.abs(UpperCAmelCase__))
           A__ 					=       torch.mean(torch.abs(UpperCAmelCase__))

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


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->Tuple:





           '''simple docstring'''

           A__ 					=       self.scheduler_classes[0]
           A__ 					=       self.get_scheduler_config()
           A__ 					=       scheduler_class(**UpperCAmelCase__)

           A__ 					=       [100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=UpperCAmelCase__)

           A__ 					=       scheduler.timesteps

           for i, timestep in enumerate(UpperCAmelCase__):
                  if i == len(UpperCAmelCase__) - 1:
                         A__ 					=       -1
                  else:
                         A__ 					=       timesteps[i + 1]

                  A__ 					=       scheduler.previous_timestep(UpperCAmelCase__)
                  A__ 					=       prev_t.item()

                  self.assertEqual(UpperCAmelCase__   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->List[str]:





           '''simple docstring'''

           A__ 					=       self.scheduler_classes[0]
           A__ 					=       self.get_scheduler_config()
           A__ 					=       scheduler_class(**UpperCAmelCase__)

           A__ 					=       [100, 87, 50, 51, 0]

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


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





           '''simple docstring'''

           A__ 					=       self.scheduler_classes[0]
           A__ 					=       self.get_scheduler_config()
           A__ 					=       scheduler_class(**UpperCAmelCase__)

           A__ 					=       [100, 87, 50, 1, 0]
           A__ 					=       len(UpperCAmelCase__)

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





    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->List[str]:





           '''simple docstring'''

           A__ 					=       self.scheduler_classes[0]
           A__ 					=       self.get_scheduler_config()
           A__ 					=       scheduler_class(**UpperCAmelCase__)

           A__ 					=       [scheduler.config.num_train_timesteps]

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






import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__snake_case   							=						'''src/diffusers'''

# Matches is_xxx_available()
__snake_case   							=						re.compile(r'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
__snake_case   							=						re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')


__snake_case   							=						'''
{0} = None
'''

__snake_case   							=						'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
'''


__snake_case   							=						'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			_re_backend.findall(_lowercase )
     if len(_lowercase ) == 0:
          return None

     return "_and_".join(_lowercase )






def 		_A		(		)					-> Tuple:


     """simple docstring"""


     with open(os.path.join(_lowercase					,  '__init__.py' )					,  'r'					,  encoding='utf-8'					,  newline='\n' ) as f:
          __UpperCamelCase =			f.readlines()

     # Get to the point we do the actual imports for type checking
     __UpperCamelCase =			0
     __UpperCamelCase =			{}
     # Go through the end of the file
     while line_index < len(_lowercase ):
          # If the line contains is_backend_available, we grab all objects associated with the `else` block
          __UpperCamelCase =			find_backend(lines[line_index] )
          if backend is not None:
               while not lines[line_index].startswith('else:' ):
                    line_index += 1
               line_index += 1
               __UpperCamelCase =			[]
               # Until we unindent, add backend objects to the list
               while line_index < len(_lowercase ) and len(lines[line_index] ) > 1:
                    __UpperCamelCase =			lines[line_index]
                    __UpperCamelCase =			_re_single_line_import.search(_lowercase )
                    if single_line_import_search is not None:
                         objects.extend(single_line_import_search.groups()[0].split(', ' ) )
                    elif line.startswith(' ' * 8 ):
                         objects.append(line[8:-2] )
                    line_index += 1

               if len(_lowercase ) > 0:
                    __UpperCamelCase =			objects
          else:
               line_index += 1

     return backend_specific_objects






def 		_A		(		_lowercase					,  _lowercase )					-> Union[str, Any]:


     """simple docstring"""


     if name.isupper():
          return DUMMY_CONSTANT.format(_lowercase )
     elif name.islower():
          return DUMMY_FUNCTION.format(_lowercase					,  _lowercase )
     else:
          return DUMMY_CLASS.format(_lowercase					,  _lowercase )






def 		_A		(		_lowercase=None )					-> Optional[Any]:


     """simple docstring"""


     if backend_specific_objects is None:
          __UpperCamelCase =			read_init()
     # For special correspondence backend to module name as used in the function requires_modulename
     __UpperCamelCase =			{}

     for backend, objects in backend_specific_objects.items():
          __UpperCamelCase =			'[' + ', '.join(f'''"{b}"''' for b in backend.split('_and_' ) ) + ']'
          __UpperCamelCase =			'# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
          dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
          dummy_file += "\n".join([create_dummy_object(_lowercase					,  _lowercase ) for o in objects] )
          __UpperCamelCase =			dummy_file

     return dummy_files






def 		_A		(		_lowercase=False )					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			create_dummy_files()
     # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
     __UpperCamelCase =			{'torch': 'pt'}

     # Locate actual dummy modules and read their content.
     __UpperCamelCase =			os.path.join(_lowercase					,  'utils' )
     __UpperCamelCase =			{
         backend: os.path.join(_lowercase					,  f'''dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py''' )
         for backend in dummy_files.keys()
     }

     __UpperCamelCase =			{}
     for backend, file_path in dummy_file_paths.items():
          if os.path.isfile(_lowercase ):
               with open(_lowercase					,  'r'					,  encoding='utf-8'					,  newline='\n' ) as f:
                    __UpperCamelCase =			f.read()
          else:
               __UpperCamelCase =			''

     for backend in dummy_files.keys():
          if dummy_files[backend] != actual_dummies[backend]:
               if overwrite:
                    print(
                        f'''Updating diffusers.utils.dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py as the main '''
                        '__init__ has new objects.' )
                    with open(dummy_file_paths[backend]					,  'w'					,  encoding='utf-8'					,  newline='\n' ) as f:
                         f.write(dummy_files[backend] )
               else:
                    raise ValueError(
                        'The main __init__ has objects that are not present in '
                        f'''diffusers.utils.dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py. Run `make fix-copies` '''
                        'to fix this.' )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    __snake_case   							=						parser.parse_args()

    check_dummies(args.fix_and_overwrite)


