



'''simple docstring'''





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


class 							_lowerCAmelCase	(  enum.Enum							):


 """simple docstring"""

 lowerCamelCase									=     '''timm'''
 lowerCamelCase									=     '''transformers'''



def     UpperCAmelCase     (    a_      ,		a_      ,		a_		)   ->       Optional[Any]:



   """simple docstring"""






   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     UpperCAmelCase     (    a_      ,		a_      ,		a_      ,		)   ->       Dict:



   """simple docstring"""






   if out_indices is None and out_features is None:
      A_  :		Optional[Any]						=					[len(a_		) - 1]
      A_  :		Optional[int]						=					[stage_names[-1]]
   elif out_indices is None and out_features is not None:
      A_  :		Optional[Any]						=					[stage_names.index(a_		) for layer in out_features]
   elif out_features is None and out_indices is not None:
      A_  :		int						=					[stage_names[idx] for idx in out_indices]
   return out_features, out_indices



def     UpperCAmelCase     (    a_      ,		a_      ,		a_      ,		)   ->       Tuple[List[str], List[int]]:



   """simple docstring"""






   verify_out_features_out_indices(out_features=a_      ,		out_indices=a_      ,		stage_names=a_		)
   A_						,  A_  :		List[Any]						=					_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 							_lowerCAmelCase	:


 """simple docstring"""

 lowerCamelCase									=     None







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    None:
    if getattr(self					,    """_backbone"""					,    _lowerCamelCase  ) 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_  :		str						=					[stage["""module"""] for stage in self._backbone.feature_info.info]
    A_  :		Any						=					[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
    A_  :		Tuple						=					self._backbone.feature_info.out_indices
    A_  :		str						=					self._backbone.feature_info.module_name()

    # We verify the out indices and out features are valid
    verify_out_features_out_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=self.stage_names  )
    A_						,  A_  :		List[Any]						=					out_features, out_indices







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    None:
    A_  :		List[Any]						=					getattr(_lowerCamelCase					,    """stage_names"""  )
    A_  :		int						=					getattr(_lowerCamelCase					,    """out_features"""					,    _lowerCamelCase  )
    A_  :		Optional[int]						=					getattr(_lowerCamelCase					,    """out_indices"""					,    _lowerCamelCase  )

    A_  :		Union[str, Any]						=					stage_names
    A_						,  A_  :		List[str]						=					get_aligned_output_features_output_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=_lowerCamelCase  )
    # Number of channels for each stage. This is set in the transformer backbone model init
    A_  :		int						=					None







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    None:
    A_  :		Optional[Any]						=					config

    A_  :		Optional[Any]						=					getattr(_lowerCamelCase					,    """use_timm_backbone"""					,    _lowerCamelCase  )
    A_  :		Optional[int]						=					BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

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







 @property
 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    return self._out_features







 @out_features.setter
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Any:
    A_						,  A_  :		Union[str, Any]						=					get_aligned_output_features_output_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=self.stage_names  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Any:
    return self._out_indices







 @out_indices.setter
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Union[str, Any]:
    A_						,  A_  :		List[str]						=					get_aligned_output_features_output_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=self.stage_names  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    # the current backbones will output the number of channels for each stage
    # even if that stage is not in the out_features list.
    return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names  )}







 @property
 def       UpperCAmelCase_       (      self  )    ->    Any:
    return [self.out_feature_channels[name] for name in self.out_features]







 def       UpperCAmelCase_       (      self					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    List[Any]:
    A_  :		Optional[int]						=					dict(inspect.signature(self.forward  ).parameters  )
    A_  :		str						=					{k: v for k, v in kwargs.items() if k in signature}
    return self(*_lowerCamelCase					,    **_lowerCamelCase  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase = None					,    _lowerCamelCase = None					,    _lowerCamelCase = None					,    )    ->    List[Any]:
    raise NotImplementedError("""This method should be implemented by the derived class."""  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Optional[int]						=					super().to_dict()
    A_  :		Union[str, Any]						=					output.pop("""_out_features"""  )
    A_  :		int						=					output.pop("""_out_indices"""  )
    return output


class 							_lowerCAmelCase	:


 """simple docstring"""

 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    return self._out_features







 @out_features.setter
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    A_						,  A_  :		List[str]						=					get_aligned_output_features_output_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=self.stage_names  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    return self._out_indices







 @out_indices.setter
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    str:
    A_						,  A_  :		Union[str, Any]						=					get_aligned_output_features_output_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=self.stage_names  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Optional[int]						=					super().to_dict()
    A_  :		Tuple						=					output.pop("""_out_features"""  )
    A_  :		Optional[Any]						=					output.pop("""_out_indices"""  )
    return output





'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
