
"""simple docstring"""

from __future__ import annotations






def 							A						(    snake_case				:list[int]    , snake_case				:list[int]    , snake_case				:int		)				->     tuple[float, list[float]]:
 __UpperCamelCase      =	list(range(len(snake_case		)		)		)
 __UpperCamelCase      =	[v / w for v, w in zip(snake_case    , snake_case		)]
 index.sort(key=lambda snake_case		: ratio[i]    , reverse=snake_case		)

 __UpperCamelCase      =	0
 __UpperCamelCase      =	[0] * len(snake_case		)
 for i in index:
  if weight[i] <= capacity:
   __UpperCamelCase      =	1
   max_value += value[i]
   capacity -= weight[i]
  else:
   __UpperCamelCase      =	capacity / weight[i]
   max_value += value[i] * capacity / weight[i]
   break

 return max_value, fractions


if __name__ == "__main__":
      import doctest

      doctest.testmod()





"""simple docstring"""

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


UpperCamelCase     :    List[Any]       =			False

@skip_mps
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						StableDiffusionAttendAndExcitePipeline
     lowercase						       =						False
     lowercase						       =						TEXT_TO_IMAGE_PARAMS
     lowercase						       =						TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"}      )
     lowercase						       =						TEXT_TO_IMAGE_IMAGE_PARAMS
     lowercase						       =						TEXT_TO_IMAGE_IMAGE_PARAMS





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().setUpClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().tearDownClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      torch.manual_seed(0				)
      __UpperCamelCase      =	UNetaDConditionModel(
          block_out_channels=(32, 64)     ,					layers_per_block=1     ,					sample_size=32     ,					in_channels=4     ,					out_channels=4     ,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,					cross_attention_dim=32     ,					attention_head_dim=(2, 4)     ,					use_linear_projection=__UpperCAmelCase     ,					)
      __UpperCamelCase      =	DDIMScheduler(
          beta_start=0.0_0_0_8_5     ,					beta_end=0.0_1_2     ,					beta_schedule='scaled_linear'     ,					clip_sample=__UpperCAmelCase     ,					set_alpha_to_one=__UpperCAmelCase     ,					)
      torch.manual_seed(0				)
      __UpperCamelCase      =	AutoencoderKL(
          block_out_channels=[32, 64]     ,					in_channels=3     ,					out_channels=3     ,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,					latent_channels=4     ,					sample_size=128     ,					)
      torch.manual_seed(0				)
      __UpperCamelCase      =	CLIPTextConfig(
          bos_token_id=0     ,					eos_token_id=2     ,					hidden_size=32     ,					intermediate_size=37     ,					layer_norm_eps=1E-05     ,					num_attention_heads=4     ,					num_hidden_layers=5     ,					pad_token_id=1     ,					vocab_size=1000     ,					hidden_act='gelu'     ,					projection_dim=512     ,					)
      __UpperCamelCase      =	CLIPTextModel(__UpperCAmelCase				)
      __UpperCamelCase      =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

      __UpperCamelCase      =	{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }

      return components





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=0				):

      '''simple docstring'''




      if str(__UpperCAmelCase				).startswith('mps'				):
       __UpperCamelCase      =	torch.manual_seed(__UpperCAmelCase				)
      else:
       __UpperCamelCase      =	torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
      __UpperCamelCase      =	__UpperCamelCase      =	{
          'prompt': 'a cat and a frog',
          'token_indices': [2, 5],
          'generator': generator,
          'num_inference_steps': 1,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
          'max_iter_to_alter': 2,
          'thresholds': {0: 0.7},
      }
      return inputs





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	'cpu'

      __UpperCamelCase      =	self.get_dummy_components()
      __UpperCamelCase      =	self.pipeline_class(**__UpperCAmelCase				)
      pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

      __UpperCamelCase      =	self.get_dummy_inputs(__UpperCAmelCase				)
      __UpperCamelCase      =	pipe(**__UpperCAmelCase				).images
      __UpperCamelCase      =	image[0, -3:, -3:, -1]

      self.assertEqual(image.shape     ,					(1, 64, 64, 3)				)
      __UpperCamelCase      =	np.array(
          [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]				)
      __UpperCamelCase      =	np.abs(image_slice.flatten() - expected_slice				).max()
      self.assertLessEqual(__UpperCAmelCase     ,					1E-3				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_cpu_offload_forward_pass(expected_max_diff=5E-4				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self._test_inference_batch_consistent(batch_sizes=[1, 2]				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self._test_inference_batch_single_identical(batch_size=2     ,					expected_max_diff=7E-4				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_save_load_local(expected_max_difference=5E-4				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_save_load_optional_components(expected_max_difference=4E-4				)


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





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().setUpClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().tearDownClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	torch.manual_seed(51				)

      __UpperCamelCase      =	StableDiffusionAttendAndExcitePipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'     ,					safety_checker=__UpperCAmelCase     ,					torch_dtype=torch.floataa				)
      pipe.to('cuda'				)

      __UpperCamelCase      =	'a painting of an elephant with glasses'
      __UpperCamelCase      =	[5, 7]

      __UpperCamelCase      =	pipe(
          prompt=__UpperCAmelCase     ,					token_indices=__UpperCAmelCase     ,					guidance_scale=7.5     ,					generator=__UpperCAmelCase     ,					num_inference_steps=5     ,					max_iter_to_alter=5     ,					output_type='numpy'     ,					).images[0]

      __UpperCamelCase      =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'				)
      assert np.abs((expected_image - image).max()				) < 5E-1



