
import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







  @property
  def 							_lowercase							(			self      :			Dict   )      ->     List[Any]:







    """simple docstring"""






    torch.manual_seed(0   )
    __magic_name__    					=	UNetaDModel(
        block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")      ,							up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")      ,							)
    return model
  def 							_lowercase							(			self      :			Optional[int]   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	"""cpu"""
    __magic_name__    					=	self.dummy_uncond_unet
    __magic_name__    					=	DDPMScheduler()

    __magic_name__    					=	DDPMPipeline(unet=UpperCamelCase__      ,							scheduler=UpperCamelCase__   )
    ddpm.to(UpperCamelCase__   )
    ddpm.set_progress_bar_config(disable=UpperCamelCase__   )

    __magic_name__    					=	torch.Generator(device=UpperCamelCase__   ).manual_seed(0   )
    __magic_name__    					=	ddpm(generator=UpperCamelCase__      ,							num_inference_steps=2      ,							output_type="""numpy"""   ).images

    __magic_name__    					=	torch.Generator(device=UpperCamelCase__   ).manual_seed(0   )
    __magic_name__    					=	ddpm(generator=UpperCamelCase__      ,							num_inference_steps=2      ,							output_type="""numpy"""      ,							return_dict=UpperCamelCase__   )[0]

    __magic_name__    					=	image[0, -3:, -3:, -1]
    __magic_name__    					=	image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __magic_name__    					=	np.array(
        [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2





  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	self.dummy_uncond_unet
    __magic_name__    					=	DDPMScheduler(prediction_type="""sample"""   )

    __magic_name__    					=	DDPMPipeline(unet=UpperCamelCase__      ,							scheduler=UpperCamelCase__   )
    ddpm.to(UpperCamelCase__   )
    ddpm.set_progress_bar_config(disable=UpperCamelCase__   )

    __magic_name__    					=	torch.manual_seed(0   )
    __magic_name__    					=	ddpm(generator=UpperCamelCase__      ,							num_inference_steps=2      ,							output_type="""numpy"""   ).images

    __magic_name__    					=	torch.manual_seed(0   )
    __magic_name__    					=	ddpm(generator=UpperCamelCase__      ,							num_inference_steps=2      ,							output_type="""numpy"""   )[0]

    __magic_name__    					=	image[0, -3:, -3:, -1]
    __magic_name__    					=	image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __magic_name__    					=	1E-2 if torch_device != """mps""" else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()   ).max() < tolerance







@slow
@require_torch_gpu
class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







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







    """simple docstring"""






    __magic_name__    					=	"""google/ddpm-cifar10-32"""

    __magic_name__    					=	UNetaDModel.from_pretrained(UpperCamelCase__   )
    __magic_name__    					=	DDPMScheduler.from_pretrained(UpperCamelCase__   )

    __magic_name__    					=	DDPMPipeline(unet=UpperCamelCase__      ,							scheduler=UpperCamelCase__   )
    ddpm.to(UpperCamelCase__   )
    ddpm.set_progress_bar_config(disable=UpperCamelCase__   )

    __magic_name__    					=	torch.manual_seed(0   )
    __magic_name__    					=	ddpm(generator=UpperCamelCase__      ,							output_type="""numpy"""   ).images

    __magic_name__    					=	image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __magic_name__    					=	np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]   )
    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def a__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	copy.deepcopy(A_ )
  __magic_name__    					=	sorted(A_ )
  __magic_name__    					=	[bisect.bisect_right(A_,					A_ ) for y in x]
  return quantized

def a__   (	A_,					A_=0 ):






  '''simple docstring'''





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





class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  def __init__(			self      :			Dict      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    if not isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
      raise ValueError(
          """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(UpperCamelCase__   )   )
    __magic_name__    					=	sampler
    __magic_name__    					=	group_ids
    __magic_name__    					=	batch_size
  def __iter__(			self      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	defaultdict(UpperCamelCase__   )
    __magic_name__    					=	defaultdict(UpperCamelCase__   )

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





  def __len__(			self      :			List[Any]   )      ->     Any:







    """simple docstring"""






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

