

'''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       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   str       ,   SCREAMING_SNAKE_CASE      :   Dict						):
  UpperCAmelCase													=	copy.deepcopy(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	sorted(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	[bisect.bisect_right(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) for y in x]
  return quantized


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   List[Any]       ,   SCREAMING_SNAKE_CASE      :   Optional[int]=0						):
  UpperCAmelCase													=	np.arange(start=3       ,   stop=SCREAMING_SNAKE_CASE       ,   step=4						).tolist() if k > 0 else [10]
  UpperCAmelCase													=	_quantize(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  # count number of elements per group
  UpperCAmelCase													=	np.unique(SCREAMING_SNAKE_CASE       ,   return_counts=SCREAMING_SNAKE_CASE						)[1]
  UpperCAmelCase													=	[0] + bins + [np.inf]
  logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE						)						)
  logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE						)						)
  return groups



class        lowercase_	(		a ):

  '''simple docstring'''





  def __init__(     self   , a_   , a_   , a_     )							->				Dict:


    """simple docstring"""
    if not isinstance(a_   , a_     ):
      raise ValueError(
          'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(a_     )     )
    UpperCAmelCase													=	sampler
    UpperCAmelCase													=	group_ids
    UpperCAmelCase													=	batch_size
  def __iter__(     self     )							->				Optional[Any]:


    """simple docstring"""
    UpperCAmelCase													=	defaultdict(a_     )
    UpperCAmelCase													=	defaultdict(a_     )

    UpperCAmelCase													=	0
    for idx in self.sampler:
      UpperCAmelCase													=	self.group_ids[idx]
      buffer_per_group[group_id].append(a_     )
      samples_per_group[group_id].append(a_     )
      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
    UpperCAmelCase													=	len(self     )
    UpperCAmelCase													=	expected_num_batches - num_batches
    if num_remaining > 0:
      # for the remaining batches, group the batches by similar lengths
      UpperCAmelCase													=	[]
      for group_id, idxs in sorted(buffer_per_group.items()   , key=lambda a_     : x[0]     ):
        batch_idx.extend(a_     )
        if len(a_     ) >= self.batch_size:
          yield batch_idx[: self.batch_size]
          UpperCAmelCase													=	batch_idx[self.batch_size :]
          num_remaining -= 1
      if len(a_     ) > 0:
        yield batch_idx
        num_remaining -= 1
    assert num_remaining == 0





  def __len__(     self     )							->				int:


    """simple docstring"""
    return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size





'''simple docstring'''




_a				: Tuple 							=						'\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n'

_a				: Tuple 							=						[{'type': 'code', 'content': INSTALL_CONTENT}]


