

'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 def __init__(       self  , snake_case_  , snake_case_   ):
   super().__init__()

   # make sure scheduler can always be converted to DDIM
   lowercase									=DDIMScheduler.from_config(scheduler.config   )

   self.register_modules(unet=snake_case_  , scheduler=snake_case_   )







 @torch.no_grad()
 def __call__(       self  , snake_case_ = 1  , snake_case_ = None  , snake_case_ = 0.0  , snake_case_ = 50  , snake_case_ = None  , snake_case_ = "pil"  , snake_case_ = True  , ):

   # Sample gaussian noise to begin loop
   if isinstance(self.unet.config.sample_size  , snake_case_   ):
     lowercase									=(
         batch_size,
         self.unet.config.in_channels,
         self.unet.config.sample_size,
         self.unet.config.sample_size,
     )
   else:
     lowercase									=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

   if isinstance(snake_case_  , snake_case_   ) and len(snake_case_   ) != batch_size:
     raise ValueError(
         f'You have passed a list of generators of length {len(snake_case_   )}, but requested an effective batch'
         f' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

   lowercase									=randn_tensor(snake_case_  , generator=snake_case_  , device=self.device  , dtype=self.unet.dtype   )

   # set step values
   self.scheduler.set_timesteps(snake_case_   )

   for t in self.progress_bar(self.scheduler.timesteps   ):
     # 1. predict noise model_output
     lowercase									=self.unet(snake_case_  , snake_case_   ).sample

     # 2. predict previous mean of image x_t-1 and add variance depending on eta
     # eta corresponds to η in paper and should be between [0, 1]
     # do x_t -> x_t-1
     lowercase									=self.scheduler.step(
         snake_case_  , snake_case_  , snake_case_  , eta=snake_case_  , use_clipped_model_output=snake_case_  , generator=snake_case_   ).prev_sample

   lowercase									=(image / 2 + 0.5).clamp(0  , 1   )
   lowercase									=image.cpu().permute(0  , 2  , 3  , 1   ).numpy()
   if output_type == "pil":
     lowercase									=self.numpy_to_pil(snake_case_   )

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=snake_case_   )





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


a_      :      Optional[int]    =     False
@skip_mps
class    __UpperCamelCase			(       _lowercase	,	_lowercase	,	_lowercase	,	unittest.TestCase				):

 """simple docstring"""



 _lowercase   :    List[Any]      						=					StableDiffusionAttendAndExcitePipeline
 _lowercase   :    str      						=					False
 _lowercase   :    List[str]      						=					TEXT_TO_IMAGE_PARAMS
 _lowercase   :    Optional[int]      						=					TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}				)
 _lowercase   :    Union[str, Any]      						=					TEXT_TO_IMAGE_IMAGE_PARAMS
 _lowercase   :    List[Any]      						=					TEXT_TO_IMAGE_IMAGE_PARAMS

 @classmethod
 def 						_UpperCAmelCase       (	cls      ) ->			Dict:
  super().setUpClass()
  torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE      )

 @classmethod
 def 						_UpperCAmelCase       (	cls      ) ->			Tuple:
  super().tearDownClass()
  torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  torch.manual_seed(0      )
  a__												= UNetaDConditionModel(
      block_out_channels=(3_2, 6_4)		,				layers_per_block=1		,				sample_size=3_2		,				in_channels=4		,				out_channels=4		,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,				cross_attention_dim=3_2		,				attention_head_dim=(2, 4)		,				use_linear_projection=SCREAMING_SNAKE_CASE		,				)
  a__												= DDIMScheduler(
      beta_start=0.0_00_85		,				beta_end=0.0_12		,				beta_schedule='''scaled_linear'''		,				clip_sample=SCREAMING_SNAKE_CASE		,				set_alpha_to_one=SCREAMING_SNAKE_CASE		,				)
  torch.manual_seed(0      )
  a__												= AutoencoderKL(
      block_out_channels=[3_2, 6_4]		,				in_channels=3		,				out_channels=3		,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,				latent_channels=4		,				sample_size=1_2_8		,				)
  torch.manual_seed(0      )
  a__												= CLIPTextConfig(
      bos_token_id=0		,				eos_token_id=2		,				hidden_size=3_2		,				intermediate_size=3_7		,				layer_norm_eps=1e-0_5		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=1_0_0_0		,				hidden_act='''gelu'''		,				projection_dim=5_1_2		,				)
  a__												= CLIPTextModel(SCREAMING_SNAKE_CASE      )
  a__												= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

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

  return components

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=0      ) ->			Dict:
  if str(SCREAMING_SNAKE_CASE      ).startswith('''mps'''      ):
   a__												= torch.manual_seed(SCREAMING_SNAKE_CASE      )
  else:
   a__												= torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(SCREAMING_SNAKE_CASE      )
  a__												= a__												= {
      '''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      ) ->			str:
  a__												= '''cpu'''

  a__												= self.get_dummy_components()
  a__												= self.pipeline_class(**SCREAMING_SNAKE_CASE      )
  pipe.to(SCREAMING_SNAKE_CASE      )
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

  self.assertEqual(image.shape		,				(1, 6_4, 6_4, 3)      )
  a__												= np.array(
      [0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96]      )
  a__												= np.abs(image_slice.flatten() - expected_slice      ).max()
  self.assertLessEqual(SCREAMING_SNAKE_CASE		,				1e-3      )

 def 						_UpperCAmelCase       (	self      ) ->			str:
  super().test_cpu_offload_forward_pass(expected_max_diff=5e-4      )

 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
  self._test_inference_batch_consistent(batch_sizes=[1, 2]      )

 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  self._test_inference_batch_single_identical(batch_size=2		,				expected_max_diff=7e-4      )

 def 						_UpperCAmelCase       (	self      ) ->			int:
  super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4      )

 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  super().test_save_load_local(expected_max_difference=5e-4      )



 def 						_UpperCAmelCase       (	self      ) ->			str:
  super().test_save_load_optional_components(expected_max_difference=4e-4      )





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

 """simple docstring"""



 @classmethod
 def 						_UpperCAmelCase       (	cls      ) ->			Optional[int]:
  super().setUpClass()
  torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE      )

 @classmethod
 def 						_UpperCAmelCase       (	cls      ) ->			str:
  super().tearDownClass()
  torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()



 def 						_UpperCAmelCase       (	self      ) ->			str:
  a__												= torch.manual_seed(5_1      )

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

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

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

  a__												= 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





