






"""simple docstring"""


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Dict							=					(DDIMScheduler,)
   A_		:      Tuple							=					(("eta", 0.0), ("num_inference_steps", 50))


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			**_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					{
         '''num_train_timesteps''': 10_00,
         '''beta_start''': 0.00_01,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''clip_sample''': True,
     }

     config.update(**_lowerCamelCase    )
     return config


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			**_lowerCamelCase					:	str    ):

     '''simple docstring'''
     __A		     =					self.scheduler_classes[0]
     __A		     =					self.get_scheduler_config(**_lowerCamelCase    )
     __A		     =					scheduler_class(**_lowerCamelCase    )

     __A     ,       __A		     =					10, 0.0

     __A		     =					self.dummy_model()
     __A		     =					self.dummy_sample_deter

     scheduler.set_timesteps(_lowerCamelCase    )

     for t in scheduler.timesteps:
       __A		     =					model(_lowerCamelCase,			_lowerCamelCase    )
       __A		     =					scheduler.step(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    ).prev_sample

     return sample


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     for timesteps in [1_00, 5_00, 10_00]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=_lowerCamelCase    )

     __A		     =					self.scheduler_classes[0]
     __A		     =					self.get_scheduler_config(steps_offset=1    )
     __A		     =					scheduler_class(**_lowerCamelCase    )
     scheduler.set_timesteps(5    )
     assert torch.equal(scheduler.timesteps,			torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1],			[0.0_02, 0.02, 0.2, 2]    ):
       self.check_over_configs(beta_start=_lowerCamelCase,			beta_end=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     for timestep_spacing in ["trailing", "leading"]:
       self.check_over_configs(timestep_spacing=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     for rescale_betas_zero_snr in [True, False]:
       self.check_over_configs(rescale_betas_zero_snr=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     self.check_over_configs(thresholding=_lowerCamelCase    )
     for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(
             thresholding=_lowerCamelCase,			prediction_type=_lowerCamelCase,			sample_max_value=_lowerCamelCase,			)


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     for t in [1, 10, 49]:
       self.check_over_forward(time_step=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     for t, num_inference_steps in zip([1, 10, 50],			[10, 50, 5_00]    ):
       self.check_over_forward(time_step=_lowerCamelCase,			num_inference_steps=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     for t, eta in zip([1, 10, 49],			[0.0, 0.5, 1.0]    ):
       self.check_over_forward(time_step=_lowerCamelCase,			eta=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     __A		     =					self.scheduler_classes[0]
     __A		     =					self.get_scheduler_config()
     __A		     =					scheduler_class(**_lowerCamelCase    )

     assert torch.sum(torch.abs(scheduler._get_variance(0,			0    ) - 0.0    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_20,			4_00    ) - 0.1_47_71    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_80,			9_60    ) - 0.3_24_60    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(0,			0    ) - 0.0    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_87,			4_86    ) - 0.0_09_79    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_99,			9_98    ) - 0.02    )    ) < 1e-5


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					self.full_loop()

     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
     assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					self.full_loop(prediction_type='''v_prediction'''    )

     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 52.53_02    ) < 1e-2
     assert abs(result_mean.item() - 0.06_84    ) < 1e-3


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     # We specify different beta, so that the first alpha is 0.99
     __A		     =					self.full_loop(set_alpha_to_one=_lowerCamelCase,			beta_start=0.01    )
     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
     assert abs(result_mean.item() - 0.19_51    ) < 1e-3






   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     # We specify different beta, so that the first alpha is 0.99
     __A		     =					self.full_loop(set_alpha_to_one=_lowerCamelCase,			beta_start=0.01    )
     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
     assert abs(result_mean.item() - 0.19_41    ) < 1e-3









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
UpperCamelCase__			=    "src/transformers"

# Matches is_xxx_available()
UpperCamelCase__			=    re.compile(R"is\_([a-z_]*)_available()")
# Matches from xxx import bla
UpperCamelCase__			=    re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
UpperCamelCase__			=    re.compile(R"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")


UpperCamelCase__			=    "\n{0} = None\n"

UpperCamelCase__			=    "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


UpperCamelCase__			=    "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"






def 			UpperCAmelCase__     (     lowercase__ )			->   List[Any]:
						if _re_test_backend.search(lowercase__ ) is None:
												return None
						__lowercase											=					[b[0] for b in _re_backend.findall(lowercase__ )]
						backends.sort()
						return "_and_".join(lowercase__ )






def 			UpperCAmelCase__     (     )			->   Dict:
						with open(os.path.join(lowercase__    ,							"""__init__.py""" )    ,							"""r"""    ,							encoding="""utf-8"""    ,							newline="""\n""" ) as f:
												__lowercase											=					f.readlines()

						# Get to the point we do the actual imports for type checking
						__lowercase											=					0
						while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
												line_index += 1

						__lowercase											=					{}
						# Go through the end of the file
						while line_index < len(lowercase__ ):
												# If the line is an if is_backend_available, we grab all objects associated.
												__lowercase											=					find_backend(lines[line_index] )
												if backend is not None:
																		while not lines[line_index].startswith("""    else:""" ):
																								line_index += 1
																		line_index += 1

																		__lowercase											=					[]
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
																								__lowercase											=					lines[line_index]
																								__lowercase											=					_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(""" """ * 12 ):
																														objects.append(line[12:-2] )
																								line_index += 1

																		__lowercase											=					objects
												else:
																		line_index += 1

						return backend_specific_objects






def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__ )			->   int:
						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 			UpperCAmelCase__     (     lowercase__=None )			->   List[str]:
						if backend_specific_objects is None:
												__lowercase											=					read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						__lowercase											=					{}

						for backend, objects in backend_specific_objects.items():
												__lowercase											=					"""[""" + """, """.join(F"\"{b}\"" for b in backend.split("""_and_""" ) ) + """]"""
												__lowercase											=					"""# 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] )
												__lowercase											=					dummy_file

						return dummy_files






def 			UpperCAmelCase__     (     lowercase__=False )			->   Optional[Any]:
						__lowercase											=					create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						__lowercase											=					{"""torch""": """pt"""}

						# Locate actual dummy modules and read their content.
						__lowercase											=					os.path.join(lowercase__    ,							"""utils""" )
						__lowercase											=					{
						    backend: os.path.join(lowercase__    ,							F"dummy_{short_names.get(lowercase__    ,							lowercase__ )}_objects.py" )
						    for backend in dummy_files.keys()
						}

						__lowercase											=					{}
						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:
																								__lowercase											=					f.read()
												else:
																		__lowercase											=					""""""

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    F"Updating transformers.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"transformers.utils.dummy_{short_names.get(lowercase__    ,							lowercase__ )}_objects.py. Run `make fix-copies` "
																								    """to fix this.""" )


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

			check_dummies(args.fix_and_overwrite)





