

import bisect
import copy
from collections import defaultdict

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

from utils import logger





def    _snake_case		(snake_case_				:    Optional[int]						,					snake_case_				:    Union[str, Any]     )       ->		List[str]:
  __UpperCAmelCase  :     Any		=    copy.deepcopy(snake_case_     )
  __UpperCAmelCase  :     List[Any]		=    sorted(snake_case_     )
  __UpperCAmelCase  :     int		=    [bisect.bisect_right(snake_case_						,					snake_case_     ) for y in x]
  return quantized





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







class     lowerCamelCase_   ( _lowerCAmelCase):




 def __init__( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   if not isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ):
     raise ValueError(
         'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(_UpperCAmelCase       )       )
   __UpperCAmelCase  :     Any		=    sampler
   __UpperCAmelCase  :     Any		=    group_ids
   __UpperCAmelCase  :     str		=    batch_size




 def __iter__( self       ):
   __UpperCAmelCase  :     List[str]		=    defaultdict(_UpperCAmelCase       )
   __UpperCAmelCase  :     List[Any]		=    defaultdict(_UpperCAmelCase       )

   __UpperCAmelCase  :     List[Any]		=    0
   for idx in self.sampler:
     __UpperCAmelCase  :     Union[str, Any]		=    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
   __UpperCAmelCase  :     Optional[Any]		=    len(self       )
   __UpperCAmelCase  :     Optional[Any]		=    expected_num_batches - num_batches
   if num_remaining > 0:
     # for the remaining batches, group the batches by similar lengths
     __UpperCAmelCase  :     Any		=    []
     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]
         __UpperCAmelCase  :     Tuple		=    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       ):
   return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_SCREAMING_SNAKE_CASE          = False


@nightly
@require_torch_gpu
class     lowerCamelCase_   ( unittest.TestCase):




 def 						a					( self       ):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()




 def 						a					( self       ):
   __UpperCAmelCase  :     Dict		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   # remove text_unet
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Any		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )

   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     Union[str, Any]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=2     ,     output_type='numpy'     ,     ).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(_UpperCAmelCase       )
     __UpperCAmelCase  :     List[str]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_UpperCAmelCase       )

   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     List[str]		=    generator.manual_seed(0       )
   __UpperCAmelCase  :     Optional[int]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=2     ,     output_type='numpy'     ,     ).images

   assert np.abs(image - new_image       ).sum() < 1E-5, "Models don't have the same forward pass"




 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[Any]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Tuple		=    'cyberpunk 2077'
   __UpperCAmelCase  :     Dict		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     List[Any]		=    pipe(
       prompt=_UpperCAmelCase     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=50     ,     output_type='numpy'     ,     ).images

   __UpperCAmelCase  :     Tuple		=    image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   __UpperCAmelCase  :     Tuple		=    np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]       )

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





