


'''simple docstring'''







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
a__  : str				=	"src/transformers"

# Matches is_xxx_available()
a__  : List[Any]				=	re.compile(R'is\_([a-z_]*)_available()')
# Matches from xxx import bla
a__  : List[str]				=	re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
a__  : Dict				=	re.compile(R'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


a__  : List[str]				=	"\n{0} = None\n"

a__  : List[str]				=	"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


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







def _UpperCamelCase	(					__A   )							->		Optional[int]:


      '''simple docstring'''



      if _re_test_backend.search(__A   ) is None:
            return None
      UpperCamelCase__  =    [b[0] for b in _re_backend.findall(__A   )]
      backends.sort()
      return "_and_".join(__A   )







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


      '''simple docstring'''



      with open(os.path.join(__A				,      "__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
      while not lines[line_index].startswith("if TYPE_CHECKING"   ):
            line_index += 1

      UpperCamelCase__  =    {}
      # Go through the end of the file
      while line_index < len(__A   ):
            # If the line is an if is_backend_available, we grab all objects associated.
            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 len(lines[line_index]   ) <= 1 or lines[line_index].startswith(" " * 8   ):
                        UpperCamelCase__  =    lines[line_index]
                        UpperCamelCase__  =    _re_single_line_import.search(__A   )
                        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

                  UpperCamelCase__  =    objects
            else:
                  line_index += 1

      return backend_specific_objects







def _UpperCamelCase	(					__A				,      __A   )							->		int:


      '''simple docstring'''



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







def _UpperCamelCase	(					__A=None   )							->		str:


      '''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(__A				,      __A   ) for o in objects]   )
            UpperCamelCase__  =    dummy_file

      return dummy_files







def _UpperCamelCase	(					__A=False   )							->		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(__A				,      "utils"   )
      UpperCamelCase__  =    {
          backend: os.path.join(__A				,      F'''dummy_{short_names.get(__A				,      __A   )}_objects.py'''   )
          for backend in dummy_files.keys()
      }

      UpperCamelCase__  =    {}
      for backend, file_path in dummy_file_paths.items():
            if os.path.isfile(__A   ):
                  with open(__A				,      "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 transformers.utils.dummy_{short_names.get(__A				,      __A   )}_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(__A				,      __A   )}_objects.py. Run `make fix-copies` '''
                            "to fix this."   )


if __name__ == "__main__":
 a__  : Any				=	argparse.ArgumentParser()
 parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
 a__  : List[str]				=	parser.parse_args()

 check_dummies(args.fix_and_overwrite)









'''simple docstring'''







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class  lowercase_					(   a__       ):
    __UpperCAmelCase             =     (DPMSolverMultistepScheduler,)
    __UpperCAmelCase             =     (('num_inference_steps', 25),)


    def        __a							(					self  , **a   ):
          UpperCamelCase__  =    {
              "num_train_timesteps": 10_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "solver_order": 2,
              "prediction_type": "epsilon",
              "thresholding": False,
              "sample_max_value": 1.0,
              "algorithm_type": "dpmsolver++",
              "solver_type": "midpoint",
              "lower_order_final": False,
              "lambda_min_clipped": -float("inf"   ),
              "variance_type": None,
          }

          config.update(**a   )
          return config


    def        __a							(					self  , a=0  , **a   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )
          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )
          UpperCamelCase__  =    self.dummy_sample
          UpperCamelCase__  =    0.1 * sample
          UpperCamelCase__  =    [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                UpperCamelCase__ ,     UpperCamelCase__  =    sample, sample
                for t in range(a  , time_step + scheduler.config.solver_order + 1   ):
                      UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample
                      UpperCamelCase__  =    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   ):
          pass


    def        __a							(					self  , a=0  , **a   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )
          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )
          UpperCamelCase__  =    self.dummy_sample
          UpperCamelCase__  =    0.1 * sample
          UpperCamelCase__  =    [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config()
                UpperCamelCase__  =    scheduler_class(**a   )
                scheduler.set_timesteps(a   )

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a   )
                      UpperCamelCase__  =    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)
                      UpperCamelCase__  =    dummy_past_residuals[: new_scheduler.config.solver_order]

                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample
                UpperCamelCase__  =    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  , a=None  , **a   ):
          if scheduler is None:
                UpperCamelCase__  =    self.scheduler_classes[0]
                UpperCamelCase__  =    self.get_scheduler_config(**a   )
                UpperCamelCase__  =    scheduler_class(**a   )

          UpperCamelCase__  =    10
          UpperCamelCase__  =    self.dummy_model()
          UpperCamelCase__  =    self.dummy_sample_deter
          scheduler.set_timesteps(a   )

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

          return sample


    def        __a							(					self   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )

          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )

          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config()
                UpperCamelCase__  =    scheduler_class(**a   )

                UpperCamelCase__  =    self.dummy_sample
                UpperCamelCase__  =    0.1 * sample

                if num_inference_steps is not None and hasattr(a  , "set_timesteps"   ):
                      scheduler.set_timesteps(a   )
                elif num_inference_steps is not None and not hasattr(a  , "set_timesteps"   ):
                      UpperCamelCase__  =    num_inference_steps

                # copy over dummy past residuals (must be done after set_timesteps)
                UpperCamelCase__  =    [residual + 0.2, residual + 0.15, residual + 0.10]
                UpperCamelCase__  =    dummy_past_residuals[: scheduler.config.solver_order]

                UpperCamelCase__  =    scheduler.timesteps[5]
                UpperCamelCase__  =    scheduler.timesteps[6]

                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample
                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample

                self.assertEqual(output_a.shape  , sample.shape   )
                self.assertEqual(output_a.shape  , output_a.shape   )


    def        __a							(					self   ):
          for timesteps in [25, 50, 1_00, 9_99, 10_00]:
                self.check_over_configs(num_train_timesteps=a   )


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


    def        __a							(					self   ):
          for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                            for prediction_type in ["epsilon", "sample"]:
                                  if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                        if order == 3:
                                              continue
                                  else:
                                        self.check_over_configs(
                                            solver_order=a  , solver_type=a  , prediction_type=a  , algorithm_type=a  , )
                                  UpperCamelCase__  =    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   ):
          self.check_over_configs(lower_order_final=a   )
          self.check_over_configs(lower_order_final=a   )


    def        __a							(					self   ):
          self.check_over_configs(lambda_min_clipped=-float("inf"   )   )
          self.check_over_configs(lambda_min_clipped=-5.1   )


    def        __a							(					self   ):
          self.check_over_configs(variance_type=a   )
          self.check_over_configs(variance_type="learned_range"   )


    def        __a							(					self   ):
          for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
                self.check_over_forward(num_inference_steps=a  , time_step=0   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop()
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.3301   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(thresholding=a  , dynamic_thresholding_ratio=0.87  , sample_max_value=0.5   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 1.1364   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(prediction_type="v_prediction"   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.2251   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(prediction_type="v_prediction"  , use_karras_sigmas=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.2096   ) < 1e-3


    def        __a							(					self   ):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          UpperCamelCase__  =    DPMSolverMultistepScheduler(**self.get_scheduler_config()   )
          UpperCamelCase__  =    self.full_loop(scheduler=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.3301   ) < 1e-3

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

          UpperCamelCase__  =    self.full_loop(scheduler=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.3301   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.scheduler_classes[0]
          UpperCamelCase__  =    self.get_scheduler_config(thresholding=a  , dynamic_thresholding_ratio=0   )
          UpperCamelCase__  =    scheduler_class(**a   )

          UpperCamelCase__  =    10
          UpperCamelCase__  =    self.dummy_model()
          UpperCamelCase__  =    self.dummy_sample_deter.half()
          scheduler.set_timesteps(a   )

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

          assert sample.dtype == torch.floataa


    def        __a							(					self  , **a   ):
          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config(**a   )
                UpperCamelCase__  =    scheduler_class(**a   )

                scheduler.set_timesteps(scheduler.config.num_train_timesteps   )
                assert len(scheduler.timesteps.unique()   ) == scheduler.num_inference_steps





