code: 
  
  
  
  
  
  '''simple docstring'''
  
  import inspect
  
  from diffusers.utils import is_flax_available
  from diffusers.utils.testing_utils import require_flax
  
  
  if is_flax_available():
    import jax
  
  
  
  
  
  
  @require_flax
  class      UpperCAmelCase			:
   def   UpperCAmelCase_							(       self   :str							)->							str:
       A__,			A__       =    self.prepare_init_args_and_inputs_for_common()
  
       A__       =    self.model_class(**lowercase_							)
       A__       =    model.init(inputs_dict["prng_key"]		, inputs_dict["sample"]							)
       jax.lax.stop_gradient(lowercase_							)
  
       A__       =    model.apply(lowercase_		, inputs_dict["sample"]							)
  
       if isinstance(lowercase_		, lowercase_							):
           A__       =    output.sample
  
       self.assertIsNotNone(lowercase_							)
       A__       =    inputs_dict["sample"].shape
       self.assertEqual(output.shape		, lowercase_		, "Input and output shapes do not match"							)
   def   UpperCAmelCase_							(       self   :Any							)->							Optional[int]:
       A__,			A__       =    self.prepare_init_args_and_inputs_for_common()
  
       A__       =    16
       A__       =    (16, 32)
  
       A__       =    self.model_class(**lowercase_							)
       A__       =    model.init(inputs_dict["prng_key"]		, inputs_dict["sample"]							)
       jax.lax.stop_gradient(lowercase_							)
  
       A__       =    model.apply(lowercase_		, inputs_dict["sample"]							)
  
       if isinstance(lowercase_		, lowercase_							):
           A__       =    output.sample
  
       self.assertIsNotNone(lowercase_							)
       A__       =    inputs_dict["sample"].shape
       self.assertEqual(output.shape		, lowercase_		, "Input and output shapes do not match"							)
  
  
  
   def   UpperCAmelCase_							(       self   :int							)->							Tuple:
       A__       =    "kwargs" in inspect.signature(self.model_class.__init__							).parameters
       A__       =    len(self.model_class._deprecated_kwargs							) > 0
  
       if has_kwarg_in_model_class and not has_deprecated_kwarg:
           raise ValueError(
               F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
               " under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
               " no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
               " [<deprecated_argument>]`"							)
  
       if not has_kwarg_in_model_class and has_deprecated_kwarg:
           raise ValueError(
               F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
               " under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
               F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
               " from `_deprecated_kwargs = [<deprecated_argument>]`"							)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      Optional[Any] ):
      A__       =    copy.deepcopy(_lowerCamelCase )
      A__       =    sorted(_lowerCamelCase )
      A__       =    [bisect.bisect_right(_lowerCamelCase		, _lowerCamelCase ) for y in x]
      return quantized
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      Tuple=0 ):
      A__       =    np.arange(start=3		, stop=_lowerCamelCase		, step=4 ).tolist() if k > 0 else [10]
      A__       =    _quantize(_lowerCamelCase		, _lowerCamelCase )
      # count number of elements per group
      A__       =    np.unique(_lowerCamelCase		, return_counts=_lowerCamelCase )[1]
      A__       =    [0] + bins + [np.inf]
      logger.info("Using {} as bins for aspect lengths quantization".format(_lowerCamelCase ) )
      logger.info("Count of instances per bin: {}".format(_lowerCamelCase ) )
      return groups
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Optional[Any]		, lowercase_   :List[Any]		, lowercase_   :Optional[int]		, lowercase_   :str							)->							Dict:
       if not isinstance(lowercase_		, lowercase_							):
           raise ValueError(
               "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(lowercase_							)							)
       A__       =    sampler
       A__       =    group_ids
       A__       =    batch_size
   def __iter__(       self   :int							)->							Tuple:
       A__       =    defaultdict(lowercase_							)
       A__       =    defaultdict(lowercase_							)
  
       A__       =    0
       for idx in self.sampler:
           A__       =    self.group_ids[idx]
           buffer_per_group[group_id].append(lowercase_							)
           samples_per_group[group_id].append(lowercase_							)
           if len(buffer_per_group[group_id]							) == self.batch_size:
               yield buffer_per_group[group_id]  # TODO
               num_batches += 1
               del buffer_per_group[group_id]
           assert len(buffer_per_group[group_id]							) < self.batch_size
  
       # now we have run out of elements that satisfy
       # the group criteria, let's return the remaining
       # elements so that the size of the sampler is
       # deterministic
       A__       =    len(self							)
       A__       =    expected_num_batches - num_batches
       if num_remaining > 0:
           # for the remaining batches, group the batches by similar lengths
           A__       =    []
           for group_id, idxs in sorted(buffer_per_group.items()		, key=lambda lowercase_							: x[0]							):
               batch_idx.extend(lowercase_							)
               if len(lowercase_							) >= self.batch_size:
                   yield batch_idx[: self.batch_size]
                   A__       =    batch_idx[self.batch_size :]
                   num_remaining -= 1
           if len(lowercase_							) > 0:
               yield batch_idx
               num_remaining -= 1
       assert num_remaining == 0
  
  
  
   def __len__(       self   :int							)->							Tuple:
       return (len(self.sampler							) + self.batch_size - 1) // self.batch_size
  
  
  
  