

"""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 =				False







@skip_mps
class      __lowercase			(    __lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       StableDiffusionAttendAndExcitePipeline
  snake_case_				       =       False
  snake_case_				       =       TEXT_TO_IMAGE_PARAMS
  snake_case_				       =       TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}					)
  snake_case_				       =       TEXT_TO_IMAGE_IMAGE_PARAMS
  snake_case_				       =       TEXT_TO_IMAGE_IMAGE_PARAMS

  @classmethod
  def    __lowercase       (  cls		:			Optional[int]    ):






   '''simple docstring'''
   super().setUpClass()
   torch.use_deterministic_algorithms(A    )

  @classmethod
  def    __lowercase       (  cls		:			Union[str, Any]    ):






   '''simple docstring'''
   super().tearDownClass()
   torch.use_deterministic_algorithms(A    )

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   torch.manual_seed(0    )
   UpperCAmelCase__					:  str					   =					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=A					,)
   UpperCAmelCase__					:  Tuple					   =					DDIMScheduler(
       beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule="""scaled_linear"""					,clip_sample=A					,set_alpha_to_one=A					,)
   torch.manual_seed(0    )
   UpperCAmelCase__					:  List[Any]					   =					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__					:  Any					   =					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=1_000					,hidden_act="""gelu"""					,projection_dim=512					,)
   UpperCAmelCase__					:  str					   =					CLIPTextModel(A    )
   UpperCAmelCase__					:  int					   =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

   UpperCAmelCase__					:  int					   =					{
       """unet""": unet,
       """scheduler""": scheduler,
       """vae""": vae,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """safety_checker""": None,
       """feature_extractor""": None,
   }

   return components

  def    __lowercase       (  self		:			Dict					,A		:			List[Any]					,A		:			int=0    ):






   '''simple docstring'''
   if str(A    ).startswith("""mps"""    ):
    UpperCAmelCase__					:  List[Any]					   =					torch.manual_seed(A    )
   else:
    UpperCAmelCase__					:  str					   =					torch.Generator(device=A    ).manual_seed(A    )
   UpperCAmelCase__					:  Union[str, Any]					   =					{
       """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    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					"""cpu"""

   UpperCAmelCase__					:  List[Any]					   =					self.get_dummy_components()
   UpperCAmelCase__					:  int					   =					self.pipeline_class(**A    )
   pipe.to(A    )
   pipe.set_progress_bar_config(disable=A    )

   UpperCAmelCase__					:  Union[str, Any]					   =					self.get_dummy_inputs(A    )
   UpperCAmelCase__					:  Any					   =					pipe(**A    ).images
   UpperCAmelCase__					:  str					   =					image[0, -3:, -3:, -1]

   self.assertEqual(image.shape					,(1, 64, 64, 3)    )
   UpperCAmelCase__					:  List[str]					   =					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__					:  Optional[int]					   =					np.abs(image_slice.flatten() - expected_slice    ).max()
   self.assertLessEqual(A					,1e-3    )

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   super().test_cpu_offload_forward_pass(expected_max_diff=5e-4    )

  def    __lowercase       (  self		:			Union[str, Any]    ):






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

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   self._test_inference_batch_single_identical(batch_size=2					,expected_max_diff=7e-4    )

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4    )

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   super().test_save_load_local(expected_max_difference=5e-4    )




  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   super().test_save_load_optional_components(expected_max_difference=4e-4    )







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

  @classmethod
  def    __lowercase       (  cls		:			Any    ):






   '''simple docstring'''
   super().setUpClass()
   torch.use_deterministic_algorithms(A    )

  @classmethod
  def    __lowercase       (  cls		:			List[str]    ):






   '''simple docstring'''
   super().tearDownClass()
   torch.use_deterministic_algorithms(A    )

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()




  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					torch.manual_seed(51    )

   UpperCAmelCase__					:  List[str]					   =					StableDiffusionAttendAndExcitePipeline.from_pretrained(
       """CompVis/stable-diffusion-v1-4"""					,safety_checker=A					,torch_dtype=torch.floataa    )
   pipe.to("""cuda"""    )

   UpperCAmelCase__					:  Optional[int]					   =					"""a painting of an elephant with glasses"""
   UpperCAmelCase__					:  List[Any]					   =					[5, 7]

   UpperCAmelCase__					:  Any					   =					pipe(
       prompt=A					,token_indices=A					,guidance_scale=7.5					,generator=A					,num_inference_steps=5					,max_iter_to_alter=5					,output_type="""numpy"""					,).images[0]

   UpperCAmelCase__					:  List[Any]					   =					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








"""simple docstring"""




__UpperCAmelCase =				'Tobias Carryer'

from time import time







class      __lowercase			:

  def __init__(  self		:			Union[str, Any]					,A		:			Dict					,A		:			Optional[int]					,A		:			Union[str, Any]					,A		:			int=int(time()    )    ):  # noqa: B008






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					multiplier
   UpperCAmelCase__					:  Union[str, Any]					   =					increment
   UpperCAmelCase__					:  Dict					   =					modulo
   UpperCAmelCase__					:  Tuple					   =					seed




  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					(self.multiplier * self.seed + self.increment) % self.modulo
   return self.seed


if __name__ == "__main__":
      # Show the LCG in action.
      __UpperCAmelCase =				LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
      while True:
            print(lcg.next_number())





