






import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger






class 	lowercase_      (			snake_case__     ):






      '''simple docstring'''




      __snake_case  						=						'config.json'







      @register_to_config
      def __init__(   self   :		Dict				,      __UpperCAmelCase   :		Optional[Any]=2				,      __UpperCAmelCase   :		Tuple=5				,      __UpperCAmelCase   :		Union[str, Any]=(2, 5)				,      __UpperCAmelCase   :		int="for diffusion"				,      __UpperCAmelCase   :		str=[1, 3]				,      )					->Tuple:




       """simple docstring"""


       pass






class 	lowercase_      (			snake_case__     ):






      '''simple docstring'''




      __snake_case  						=						'config.json'







      @register_to_config
      def __init__(   self   :		Any				,      __UpperCAmelCase   :		Dict=2				,      __UpperCAmelCase   :		Optional[int]=5				,      __UpperCAmelCase   :		Optional[int]=(2, 5)				,      __UpperCAmelCase   :		Dict="for diffusion"				,      __UpperCAmelCase   :		str=[1, 3]				,      )					->Tuple:




       """simple docstring"""


       pass






class 	lowercase_      (			snake_case__     ):






      '''simple docstring'''




      __snake_case  						=						'config.json'







      @register_to_config
      def __init__(   self   :		int				,      __UpperCAmelCase   :		Optional[Any]=2				,      __UpperCAmelCase   :		Union[str, Any]=5				,      __UpperCAmelCase   :		Optional[Any]=(2, 5)				,      __UpperCAmelCase   :		Union[str, Any]="for diffusion"				,      __UpperCAmelCase   :		List[Any]=[1, 3]				,      __UpperCAmelCase   :		List[Any]=[1, 3]				,      )					->Any:




       """simple docstring"""


       pass






class 	lowercase_      (			snake_case__     ):






      '''simple docstring'''




      __snake_case  						=						'config.json'







      @register_to_config
      def __init__(   self   :		Optional[int]				,      __UpperCAmelCase   :		Optional[int]=2				,      __UpperCAmelCase   :		Optional[int]=5				,      __UpperCAmelCase   :		str=(2, 5)				,      __UpperCAmelCase   :		str="for diffusion"				,      __UpperCAmelCase   :		Union[str, Any]=[1, 5]				,      __UpperCAmelCase   :		Dict=[5, 4]				,      )					->Optional[int]:




       """simple docstring"""


       pass






class 	lowercase_      (			unittest.TestCase     ):






      '''simple docstring'''




      def    __lowerCAmelCase      (   self   :		Tuple						)					->Optional[int]:




       """simple docstring"""


       with self.assertRaises(__UpperCAmelCase						):
        ConfigMixin.load_config('''dummy_path'''						)







      def    __lowerCAmelCase      (   self   :		Any						)					->Dict:




       """simple docstring"""


       a							=   SampleObject()
       a							=   obj.config
       assert config["a"] == 2
       assert config["b"] == 5
       assert config["c"] == (2, 5)
       assert config["d"] == "for diffusion"
       assert config["e"] == [1, 3]

       # init ignore private arguments
       a							=   SampleObject(_name_or_path='''lalala'''						)
       a							=   obj.config
       assert config["a"] == 2
       assert config["b"] == 5
       assert config["c"] == (2, 5)
       assert config["d"] == "for diffusion"
       assert config["e"] == [1, 3]

       # can override default
       a							=   SampleObject(c=6						)
       a							=   obj.config
       assert config["a"] == 2
       assert config["b"] == 5
       assert config["c"] == 6
       assert config["d"] == "for diffusion"
       assert config["e"] == [1, 3]

       # can use positional arguments.
       a							=   SampleObject(1				,      c=6						)
       a							=   obj.config
       assert config["a"] == 1
       assert config["b"] == 5
       assert config["c"] == 6
       assert config["d"] == "for diffusion"
       assert config["e"] == [1, 3]







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




       """simple docstring"""


       a							=   SampleObject()
       a							=   obj.config

       assert config["a"] == 2
       assert config["b"] == 5
       assert config["c"] == (2, 5)
       assert config["d"] == "for diffusion"
       assert config["e"] == [1, 3]

       with tempfile.TemporaryDirectory() as tmpdirname:
        obj.save_config(__UpperCAmelCase						)
        a							=   SampleObject.from_config(SampleObject.load_config(__UpperCAmelCase						)						)
        a							=   new_obj.config

       # unfreeze configs
       a							=   dict(__UpperCAmelCase						)
       a							=   dict(__UpperCAmelCase						)

       assert config.pop('''c'''						) == (2, 5)  # instantiated as tuple
       assert new_config.pop('''c'''						) == [2, 5]  # saved & loaded as list because of json
       config.pop('''_use_default_values'''						)
       assert config == new_config







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




       """simple docstring"""


       a							=   logging.get_logger('''diffusers.configuration_utils'''						)
       # 30 for warning
       logger.setLevel(30						)

       with CaptureLogger(__UpperCAmelCase						) as cap_logger:
        a							=   DDIMScheduler.from_pretrained(
            '''hf-internal-testing/tiny-stable-diffusion-torch'''				,      subfolder='''scheduler'''						)

       assert ddim.__class__ == DDIMScheduler
       # no warning should be thrown
       assert cap_logger.out == ""







      def    __lowerCAmelCase      (   self   :		List[str]						)					->Any:




       """simple docstring"""


       a							=   logging.get_logger('''diffusers.configuration_utils'''						)
       # 30 for warning
       logger.setLevel(30						)

       with CaptureLogger(__UpperCAmelCase						) as cap_logger:
        a							=   EulerDiscreteScheduler.from_pretrained(
            '''hf-internal-testing/tiny-stable-diffusion-torch'''				,      subfolder='''scheduler'''						)

       assert euler.__class__ == EulerDiscreteScheduler
       # no warning should be thrown
       assert cap_logger.out == ""







      def    __lowerCAmelCase      (   self   :		int						)					->Tuple:




       """simple docstring"""


       a							=   logging.get_logger('''diffusers.configuration_utils'''						)
       # 30 for warning
       logger.setLevel(30						)

       with CaptureLogger(__UpperCAmelCase						) as cap_logger:
        a							=   EulerAncestralDiscreteScheduler.from_pretrained(
            '''hf-internal-testing/tiny-stable-diffusion-torch'''				,      subfolder='''scheduler'''						)

       assert euler.__class__ == EulerAncestralDiscreteScheduler
       # no warning should be thrown
       assert cap_logger.out == ""







      def    __lowerCAmelCase      (   self   :		List[str]						)					->int:




       """simple docstring"""


       a							=   logging.get_logger('''diffusers.configuration_utils'''						)
       # 30 for warning
       logger.setLevel(30						)

       with CaptureLogger(__UpperCAmelCase						) as cap_logger:
        a							=   PNDMScheduler.from_pretrained(
            '''hf-internal-testing/tiny-stable-diffusion-torch'''				,      subfolder='''scheduler'''						)

       assert pndm.__class__ == PNDMScheduler
       # no warning should be thrown
       assert cap_logger.out == ""







      def    __lowerCAmelCase      (   self   :		List[str]						)					->int:




       """simple docstring"""


       a							=   logging.get_logger('''diffusers.configuration_utils'''						)
       # 30 for warning
       logger.setLevel(30						)

       with CaptureLogger(__UpperCAmelCase						) as cap_logger:
        a							=   DDPMScheduler.from_pretrained(
            '''hf-internal-testing/tiny-stable-diffusion-torch'''				,      subfolder='''scheduler'''				,      prediction_type='''sample'''				,      beta_end=8				,      )

       with CaptureLogger(__UpperCAmelCase						) as cap_logger_a:
        a							=   DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''				,      beta_start=88						)

       assert ddpm.__class__ == DDPMScheduler
       assert ddpm.config.prediction_type == "sample"
       assert ddpm.config.beta_end == 8
       assert ddpm_a.config.beta_start == 88

       # no warning should be thrown
       assert cap_logger.out == ""
       assert cap_logger_a.out == ""







      def    __lowerCAmelCase      (   self   :		int						)					->Optional[int]:




       """simple docstring"""


       a							=   logging.get_logger('''diffusers.configuration_utils'''						)
       # 30 for warning
       logger.setLevel(30						)

       with CaptureLogger(__UpperCAmelCase						) as cap_logger:
        a							=   DPMSolverMultistepScheduler.from_pretrained(
            '''hf-internal-testing/tiny-stable-diffusion-torch'''				,      subfolder='''scheduler'''						)

       assert dpm.__class__ == DPMSolverMultistepScheduler
       # no warning should be thrown
       assert cap_logger.out == ""







      def    __lowerCAmelCase      (   self   :		Dict						)					->int:




       """simple docstring"""


       a							=   SampleObject()

       a							=   {k: v for k, v in config.config.items() if not k.startswith('''_'''						)}

       # make sure that default config has all keys in `_use_default_values`
       assert set(config_dict.keys()						) == set(config.config._use_default_values						)

       with tempfile.TemporaryDirectory() as tmpdirname:
        config.save_config(__UpperCAmelCase						)

        # now loading it with SampleObject2 should put f into `_use_default_values`
        a							=   SampleObjecta.from_config(__UpperCAmelCase						)

        assert "f" in config._use_default_values
        assert config.f == [1, 3]

       # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
       # **BECAUSE** it is part of `config._use_default_values`
       a							=   SampleObjecta.from_config(config.config						)
       assert new_config.f == [5, 4]

       config.config._use_default_values.pop()
       a							=   SampleObjecta.from_config(config.config						)
       assert new_config_a.f == [1, 3]

       # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
       assert new_config_a.e == [1, 3]










import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union






class 	lowercase_      (			enum.Enum     ):






      '''simple docstring'''




      __snake_case  						=						'''timm'''
      __snake_case  						=						'''transformers'''
def 		_a					(   a				:Optional[Iterable[str]]			,							a				:Optional[Iterable[int]]			,							a				:Optional[Iterable[str]]				)							->    Optional[Any]:
 if stage_names is None:
  raise ValueError('''Stage_names must be set for transformers backbones'''				)

 if out_features is not None:
  if not isinstance(a			,							(list,)				):
   raise ValueError(F"""out_features must be a list {type(a				)}"""				)
  if any(feat not in stage_names for feat in out_features				):
   raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}"""				)

 if out_indices is not None:
  if not isinstance(a			,							(list, tuple)				):
   raise ValueError(F"""out_indices must be a list or tuple, got {type(a				)}"""				)
  if any(idx >= len(a				) for idx in out_indices				):
   raise ValueError('''out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'''				)

 if out_features is not None and out_indices is not None:
  if len(a				) != len(a				):
   raise ValueError('''out_features and out_indices should have the same length if both are set'''				)
  if out_features != [stage_names[idx] for idx in out_indices]:
   raise ValueError('''out_features and out_indices should correspond to the same stages if both are set'''				)
def 		_a					(   a				:Optional[List[str]]			,							a				:Optional[Union[List[int], Tuple[int]]]			,							a				:List[str]			,							)							->    Union[str, Any]:
 if out_indices is None and out_features is None:
  a							=   [len(a				) - 1]
  a							=   [stage_names[-1]]
 elif out_indices is None and out_features is not None:
  a							=   [stage_names.index(a				) for layer in out_features]
 elif out_features is None and out_indices is not None:
  a							=   [stage_names[idx] for idx in out_indices]
 return out_features, out_indices
def 		_a					(   a				:Optional[List[str]]			,							a				:Optional[Union[List[int], Tuple[int]]]			,							a				:List[str]			,							)							->    Tuple[List[str], List[int]]:
 verify_out_features_out_indices(out_features=a			,							out_indices=a			,							stage_names=a				)
 a						,  a							=   _align_output_features_output_indices(
     out_features=a			,							out_indices=a			,							stage_names=a				)
 # Verify that the aligned out_features and out_indices are valid
 verify_out_features_out_indices(out_features=a			,							out_indices=a			,							stage_names=a				)
 return output_features, output_indices






class 	lowercase_      :






      '''simple docstring'''




      __snake_case  						=						None







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		Tuple						)					->None:




       """simple docstring"""


       if getattr(self				,      '''_backbone'''				,      __UpperCAmelCase						) is None:
        raise ValueError('''self._backbone must be set before calling _init_timm_backbone'''						)

       # These will diagree with the defaults for the transformers models e.g. for resnet50
       # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
       # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
       a							=   [stage['''module'''] for stage in self._backbone.feature_info.info]
       a							=   [stage['''num_chs'''] for stage in self._backbone.feature_info.info]
       a							=   self._backbone.feature_info.out_indices
       a							=   self._backbone.feature_info.module_name()

       # We verify the out indices and out features are valid
       verify_out_features_out_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)
       a						,  a							=   out_features, out_indices







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		Tuple						)					->None:




       """simple docstring"""


       a							=   getattr(__UpperCAmelCase				,      '''stage_names'''						)
       a							=   getattr(__UpperCAmelCase				,      '''out_features'''				,      __UpperCAmelCase						)
       a							=   getattr(__UpperCAmelCase				,      '''out_indices'''				,      __UpperCAmelCase						)

       a							=   stage_names
       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=__UpperCAmelCase						)
       # Number of channels for each stage. This is set in the transformer backbone model init
       a							=   None







      def    __lowerCAmelCase      (   self   :		str				,      __UpperCAmelCase   :		Union[str, Any]						)					->None:




       """simple docstring"""


       a							=   config

       a							=   getattr(__UpperCAmelCase				,      '''use_timm_backbone'''				,      __UpperCAmelCase						)
       a							=   BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

       if self.backbone_type == BackboneType.TIMM:
        self._init_timm_backbone(__UpperCAmelCase						)
       elif self.backbone_type == BackboneType.TRANSFORMERS:
        self._init_transformers_backbone(__UpperCAmelCase						)
       else:
        raise ValueError(F"""backbone_type {self.backbone_type} not supported."""						)







      @property
      def    __lowerCAmelCase      (   self   :		Dict						)					->Optional[Any]:




       """simple docstring"""


       return self._out_features







      @out_features.setter
      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		List[str]						)					->List[Any]:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		Dict						)					->int:




       """simple docstring"""


       return self._out_indices







      @out_indices.setter
      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		Union[Tuple[int], List[int]]						)					->List[str]:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		Tuple						)					->str:




       """simple docstring"""


       return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names						)}







      @property
      def    __lowerCAmelCase      (   self   :		int						)					->Optional[int]:




       """simple docstring"""


       return [self.out_feature_channels[name] for name in self.out_features]







      def    __lowerCAmelCase      (   self   :		Optional[Any]				,      *__UpperCAmelCase   :		List[str]				,      **__UpperCAmelCase   :		Optional[Any]						)					->Optional[int]:




       """simple docstring"""


       a							=   dict(inspect.signature(self.forward						).parameters						)
       a							=   {k: v for k, v in kwargs.items() if k in signature}
       return self(*__UpperCAmelCase				,      **__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		Dict				,      __UpperCAmelCase   :		Optional[bool] = None				,      __UpperCAmelCase   :		Optional[bool] = None				,      __UpperCAmelCase   :		Optional[bool] = None				,      )					->int:




       """simple docstring"""


       raise NotImplementedError('''This method should be implemented by the derived class.'''						)







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




       """simple docstring"""


       a							=   super().to_dict()
       a							=   output.pop('''_out_features'''						)
       a							=   output.pop('''_out_indices'''						)
       return output






class 	lowercase_      :






      '''simple docstring'''




      @property
      def    __lowerCAmelCase      (   self   :		List[str]						)					->Optional[int]:




       """simple docstring"""


       return self._out_features







      @out_features.setter
      def    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		List[str]						)					->int:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->str:




       """simple docstring"""


       return self._out_indices







      @out_indices.setter
      def    __lowerCAmelCase      (   self   :		str				,      __UpperCAmelCase   :		Union[Tuple[int], List[int]]						)					->int:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







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




       """simple docstring"""


       a							=   super().to_dict()
       a							=   output.pop('''_out_features'''						)
       a							=   output.pop('''_out_indices'''						)
       return output


