





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_           =      '\n{0} = None\n'

snake_case_           =      '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


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






def lowerCamelCase__   (							snake_case_     :  str	)				->    Tuple:
   __snake_case								=       _re_backend.findall(snake_case_	)
   if len(snake_case_	) == 0:
      return None

   return "_and_".join(snake_case_	)






def lowerCamelCase__   (							)				->    Dict:
   with open(os.path.join(snake_case_	,				'''__init__.py'''	)	,				'''r'''	,				encoding='''utf-8'''	,				newline='''\n'''	) as f:
      __snake_case								=       f.readlines()

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

   return backend_specific_objects






def lowerCamelCase__   (							snake_case_     :  List[Any]	,				snake_case_     :  Optional[int]	)				->    List[Any]:
   if name.isupper():
      return DUMMY_CONSTANT.format(snake_case_	)
   elif name.islower():
      return DUMMY_FUNCTION.format(snake_case_	,				snake_case_	)
   else:
      return DUMMY_CLASS.format(snake_case_	,				snake_case_	)






def lowerCamelCase__   (							snake_case_     :  Dict=None	)				->    str:
   if backend_specific_objects is None:
      __snake_case								=       read_init()
   # For special correspondence backend to module name as used in the function requires_modulename
   __snake_case								=       {}

   for backend, objects in backend_specific_objects.items():
      __snake_case								=       '''[''' + ''', '''.join(f"""\"{b}\"""" for b in backend.split('''_and_'''	)	) + ''']'''
      __snake_case								=       '''# 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(snake_case_	,				snake_case_	) for o in objects]	)
      __snake_case								=       dummy_file

   return dummy_files






def lowerCamelCase__   (							snake_case_     :  Union[str, Any]=False	)				->    Union[str, Any]:
   __snake_case								=       create_dummy_files()
   # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
   __snake_case								=       {'''torch''': '''pt'''}

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

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

   for backend in dummy_files.keys():
      if dummy_files[backend] != actual_dummies[backend]:
         if overwrite:
            print(
                f"""Updating diffusers.utils.dummy_{short_names.get(snake_case_	,				snake_case_	)}_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(snake_case_	,				snake_case_	)}_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)








import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Union[str, Any]							=   (DPMSolverSinglestepScheduler,)
      A_						:    Union[str, Any]							=   (('num_inference_steps', 25),)


      def        a			(self	:				Dict    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       {
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''solver_order''': 2,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
             '''sample_max_value''': 1.0,
             '''algorithm_type''': '''dpmsolver++''',
             '''solver_type''': '''midpoint''',
             '''lambda_min_clipped''': -float('''inf'''				),
             '''variance_type''': None,
         }

         config.update(**a__				)
         return config


      def        a			(self	:				str    ,      a__	:				Any=0    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config(**a__				)
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)
            # copy over dummy past residuals
            __snake_case								=       dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__				)
               __snake_case								=       scheduler_class.from_pretrained(a__				)
               new_scheduler.set_timesteps(a__				)
               # copy over dummy past residuals
               __snake_case								=       dummy_past_residuals[: new_scheduler.config.solver_order]

            __snake_case	,     __snake_case								=       sample, sample
            for t in range(a__    ,      time_step + scheduler.config.solver_order + 1				):
               __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
               __snake_case								=       new_scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

               assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"


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





         """simple docstring"""



         pass


      def        a			(self	:				List[Any]    ,      a__	:				Dict=0    ,      **a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config()
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)

            # copy over dummy past residuals (must be after setting timesteps)
            __snake_case								=       dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__				)
               __snake_case								=       scheduler_class.from_pretrained(a__				)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(a__				)

               # copy over dummy past residual (must be after setting timesteps)
               __snake_case								=       dummy_past_residuals[: new_scheduler.config.solver_order]

            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
            __snake_case								=       new_scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

            assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"


      def        a			(self	:				int    ,      a__	:				Tuple=None    ,      **a__	:				List[str]				):





         """simple docstring"""



         if scheduler is None:
            __snake_case								=       self.scheduler_classes[0]
            __snake_case								=       self.get_scheduler_config(**a__				)
            __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(**a__				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

         for i, t in enumerate(scheduler.timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         return sample


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
         __snake_case								=       50
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:]				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_5_7_4				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         for timesteps in [25, 50, 100, 999, 1000]:
            self.check_over_configs(num_train_timesteps=a__				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3

         __snake_case								=       DEISMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       DPMSolverMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       UniPCMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       DPMSolverSinglestepScheduler.from_config(scheduler.config				)

         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3


      def        a			(self	:				List[str]				):





         """simple docstring"""



         self.check_over_configs(thresholding=a__				)
         for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
               for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=a__    ,      prediction_type=a__    ,      sample_max_value=a__    ,      algorithm_type='''dpmsolver++'''    ,      solver_order=a__    ,      solver_type=a__    ,      )


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





         """simple docstring"""



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


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





         """simple docstring"""



         for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
               for order in [1, 2, 3]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      algorithm_type=a__    ,      )
                     __snake_case								=       self.full_loop(
                         solver_order=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      algorithm_type=a__    ,      )
                     assert not torch.isnan(a__				).any(), "Samples have nan numbers"


      def        a			(self	:				List[str]				):





         """simple docstring"""



         self.check_over_configs(lower_order_final=a__				)
         self.check_over_configs(lower_order_final=a__				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         self.check_over_configs(lambda_min_clipped=-float('''inf'''				)				)
         self.check_over_configs(lambda_min_clipped=-5.1				)


      def        a			(self	:				Tuple				):





         """simple docstring"""



         self.check_over_configs(variance_type=a__				)
         self.check_over_configs(variance_type='''learned_range'''				)


      def        a			(self	:				int				):





         """simple docstring"""



         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
            self.check_over_forward(num_inference_steps=a__    ,      time_step=0				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop()
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.full_loop(use_karras_sigmas=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_2_4_8				) < 1E-3


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.1_4_5_3				) < 1E-3


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''    ,      use_karras_sigmas=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.0_6_4_9				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(thresholding=a__    ,      dynamic_thresholding_ratio=0				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter.half()
         scheduler.set_timesteps(a__				)

         for i, t in enumerate(scheduler.timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         assert sample.dtype == torch.floataa

