

'''simple docstring'''





import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")






def 							_lowerCAmelCase (      __magic_name__	:    Dict	)							->     Optional[int]:
      lowercase   :       int								=test_file.split(os.path.sep	)
      if components[0:2] != ["tests", "models"]:
            raise ValueError(
                '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
                f'''{test_file} instead.'''	)
      lowercase   :       Union[str, Any]								=components[-1]
      if not test_fn.endswith('''py'''	):
            raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''	)
      if not test_fn.startswith('''test_modeling_'''	):
            raise ValueError(
                f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''	)

      lowercase   :       Optional[int]								=components[:-1] + [test_fn.replace('''.py'''      ,   ''''''	)]
      lowercase   :       Dict								='''.'''.join(__magic_name__	)

      return test_module_path






def 							_lowerCAmelCase (      __magic_name__	:    Dict	)							->     Dict:
      lowercase   :       Optional[int]								=get_module_path(__magic_name__	)
      lowercase   :       Tuple								=importlib.import_module(__magic_name__	)

      return test_module






def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]	)							->     Tuple:
      lowercase   :       Optional[int]								=[]
      lowercase   :       Union[str, Any]								=get_test_module(__magic_name__	)
      for attr in dir(__magic_name__	):
            if attr.endswith('''ModelTester'''	):
                  tester_classes.append(getattr(__magic_name__      ,   __magic_name__	)	)

    # sort with class names
      return sorted(__magic_name__      ,   key=lambda __magic_name__	: x.__name__	)






def 							_lowerCAmelCase (      __magic_name__	:    List[str]	)							->     Optional[int]:
      lowercase   :       Optional[Any]								=[]
      lowercase   :       str								=get_test_module(__magic_name__	)
      for attr in dir(__magic_name__	):
            lowercase   :       Any								=getattr(__magic_name__      ,   __magic_name__	)
            # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
            # `all_model_classes` is not empty (which also excludes other special classes).
            lowercase   :       Optional[int]								=getattr(__magic_name__      ,   '''all_model_classes'''      ,   []	)
            if len(__magic_name__	) > 0:
                  test_classes.append(__magic_name__	)

    # sort with class names
      return sorted(__magic_name__      ,   key=lambda __magic_name__	: x.__name__	)






def 							_lowerCAmelCase (      __magic_name__	:    Dict	)							->     Union[str, Any]:
      lowercase   :       List[str]								=get_test_classes(__magic_name__	)
      lowercase   :       List[str]								=set()
      for test_class in test_classes:
            model_classes.update(test_class.all_model_classes	)

      # sort with class names
      return sorted(__magic_name__      ,   key=lambda __magic_name__	: x.__name__	)






def 							_lowerCAmelCase (      __magic_name__	:    Any	)							->     Union[str, Any]:
      lowercase   :       List[Any]								=test_class()
      if hasattr(__magic_name__      ,   '''setUp'''	):
            test.setUp()

      lowercase   :       List[Any]								=None
      if hasattr(__magic_name__      ,   '''model_tester'''	):
            # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
            if test.model_tester is not None:
                  lowercase   :       Dict								=test.model_tester.__class__

      return model_tester






def 							_lowerCAmelCase (      __magic_name__	:    Any      ,   __magic_name__	:    int	)							->     int:
      lowercase   :       Tuple								=get_test_classes(__magic_name__	)

      lowercase   :       Optional[int]								=[]
      for test_class in test_classes:
            if model_class in test_class.all_model_classes:
                  target_test_classes.append(__magic_name__	)

    # sort with class names
      return sorted(__magic_name__      ,   key=lambda __magic_name__	: x.__name__	)






def 							_lowerCAmelCase (      __magic_name__	:    Optional[Any]      ,   __magic_name__	:    Tuple	)							->     str:
      lowercase   :       Any								=get_test_classes_for_model(__magic_name__      ,   __magic_name__	)

      lowercase   :       List[Any]								=[]
      for test_class in test_classes:
            lowercase   :       int								=get_model_tester_from_test_class(__magic_name__	)
            if tester_class is not None:
                  tester_classes.append(__magic_name__	)

    # sort with class names
      return sorted(__magic_name__      ,   key=lambda __magic_name__	: x.__name__	)






def 							_lowerCAmelCase (      __magic_name__	:    Optional[Any]	)							->     Tuple:
      lowercase   :       Tuple								=get_test_classes(__magic_name__	)
      lowercase   :       Optional[int]								={test_class: get_model_tester_from_test_class(__magic_name__	) for test_class in test_classes}
      return test_tester_mapping






def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]	)							->     int:
      lowercase   :       Optional[int]								=get_model_classes(__magic_name__	)
      lowercase   :       Any								={
          model_class: get_test_classes_for_model(__magic_name__      ,   __magic_name__	) for model_class in model_classes
      }
      return model_test_mapping






def 							_lowerCAmelCase (      __magic_name__	:    int	)							->     Optional[int]:
      lowercase   :       Dict								=get_model_classes(__magic_name__	)
      lowercase   :       Any								={
          model_class: get_tester_classes_for_model(__magic_name__      ,   __magic_name__	) for model_class in model_classes
      }
      return model_to_tester_mapping







def 							_lowerCAmelCase (      __magic_name__	:    Optional[Any]	)							->     int:
      if isinstance(__magic_name__      ,   __magic_name__	):
            return o
      elif isinstance(__magic_name__      ,   __magic_name__	):
            return o.__name__
      elif isinstance(__magic_name__      ,   (list, tuple)	):
            return [to_json(__magic_name__	) for x in o]
      elif isinstance(__magic_name__      ,   __magic_name__	):
            return {to_json(__magic_name__	): to_json(__magic_name__	) for k, v in o.items()}
      else:
            return o








'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase   :       Any								=DDIMScheduler.from_config(scheduler.config      )

										self.register_modules(unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__      )







				@torch.no_grad()
				def __call__(					self    :		List[Any]		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				):
										'''simple docstring'''

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size		,				UpperCAmelCase__      ):
																lowercase   :       Optional[int]								=(
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																lowercase   :       Optional[int]								=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ) and len(UpperCAmelCase__      ) != batch_size:
																raise ValueError(
																    F'''You have passed a list of generators of length {len(UpperCAmelCase__      )}, but requested an effective batch'''
																    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

										lowercase   :       str								=randn_tensor(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device=self.device		,				dtype=self.unet.dtype      )

										# set step values
										self.scheduler.set_timesteps(UpperCAmelCase__      )

										for t in self.progress_bar(self.scheduler.timesteps      ):
																# 1. predict noise model_output
																lowercase   :       Dict								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__      ).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																lowercase   :       Dict								=self.scheduler.step(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				eta=UpperCAmelCase__		,				use_clipped_model_output=UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

										lowercase   :       Optional[Any]								=(image / 2 + 0.5).clamp(0		,				1      )
										lowercase   :       Optional[Any]								=image.cpu().permute(0		,				2		,				3		,				1      ).numpy()
										if output_type == "pil":
																lowercase   :       List[str]								=self.numpy_to_pil(UpperCAmelCase__      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__      )





