





'''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>]`"							)










'''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



