




'''simple docstring'''





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, 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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class     a__ (      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      unittest.TestCase   ):
     a	:    int	      =			StableDiffusionXLPipeline
     a	:    List[Any]	      =			TEXT_TO_IMAGE_PARAMS
     a	:    int	      =			TEXT_TO_IMAGE_BATCH_PARAMS
     a	:    Any	      =			TEXT_TO_IMAGE_IMAGE_PARAMS
     a	:    str	      =			TEXT_TO_IMAGE_IMAGE_PARAMS







     def     lowerCAmelCase_       (			self				)						->					Union[str, Any]:



          '''simple docstring'''



          torch.manual_seed(0				)
          a											=   UNetaDConditionModel(
              block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=4						,	out_channels=4						,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,	attention_head_dim=(2, 4)						,	use_linear_projection=UpperCamelCase__						,	addition_embed_type="text_time"						,	addition_time_embed_dim=8						,	transformer_layers_per_block=(1, 2)						,	projection_class_embeddings_input_dim=80						,	cross_attention_dim=64						,	)
          a											=   EulerDiscreteScheduler(
              beta_start=0.0_0_0_8_5						,	beta_end=0.0_1_2						,	steps_offset=1						,	beta_schedule="scaled_linear"						,	timestep_spacing="leading"						,	)
          torch.manual_seed(0				)
          a											=   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				)
          a											=   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=32						,	)
          a											=   CLIPTextModel(UpperCamelCase__				)
          a											=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						,	local_files_only=UpperCamelCase__				)

          a											=   CLIPTextModelWithProjection(UpperCamelCase__				)
          a											=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						,	local_files_only=UpperCamelCase__				)

          a											=   {
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "text_encoder_2": text_encoder_a,
              "tokenizer_2": tokenizer_a,
              # "safety_checker": None,
              # "feature_extractor": None,
          }
          return components







     def     lowerCAmelCase_       (			self						,	A						,	A=0				)						->					str:



          '''simple docstring'''



          if str(UpperCamelCase__				).startswith("mps"				):
               a											=   torch.manual_seed(UpperCamelCase__				)
          else:
               a											=   torch.Generator(device=UpperCamelCase__				).manual_seed(UpperCamelCase__				)
          a											=   {
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 5.0,
              "output_type": "numpy",
          }
          return inputs







     def     lowerCAmelCase_       (			self				)						->					Union[str, Any]:



          '''simple docstring'''



          a											=   "cpu"  # ensure determinism for the device-dependent torch.Generator
          a											=   self.get_dummy_components()
          a											=   StableDiffusionXLPipeline(**UpperCamelCase__				)
          a											=   sd_pipe.to(UpperCamelCase__				)
          sd_pipe.set_progress_bar_config(disable=UpperCamelCase__				)

          a											=   self.get_dummy_inputs(UpperCamelCase__				)
          a											=   sd_pipe(**UpperCamelCase__				).images
          a											=   image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          a											=   np.array([0.5_8_7_3, 0.6_1_2_8, 0.4_7_9_7, 0.5_1_2_2, 0.5_6_7_4, 0.4_6_3_9, 0.5_2_2_7, 0.5_1_4_9, 0.4_7_4_7]				)

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







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          a											=   self.get_dummy_components()
          a											=   StableDiffusionXLPipeline(**UpperCamelCase__				)
          a											=   sd_pipe.to(UpperCamelCase__				)
          a											=   sd_pipe.to(UpperCamelCase__				)
          sd_pipe.set_progress_bar_config(disable=UpperCamelCase__				)

          # forward without prompt embeds
          a											=   self.get_dummy_inputs(UpperCamelCase__				)
          a											=   3 * ["this is a negative prompt"]
          a											=   negative_prompt
          a											=   3 * [inputs["prompt"]]

          a											=   sd_pipe(**UpperCamelCase__				)
          a											=   output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          a											=   self.get_dummy_inputs(UpperCamelCase__				)
          a											=   3 * ["this is a negative prompt"]
          a											=   3 * [inputs.pop("prompt"				)]

          (
              (
              a 
          )						,   (
              a 
          )						,   (
              a 
          )						,   (
              a 
          )						,    
          )											=   sd_pipe.encode_prompt(UpperCamelCase__						,	negative_prompt=UpperCamelCase__				)

          a											=   sd_pipe(
              **UpperCamelCase__						,	prompt_embeds=UpperCamelCase__						,	negative_prompt_embeds=UpperCamelCase__						,	pooled_prompt_embeds=UpperCamelCase__						,	negative_pooled_prompt_embeds=UpperCamelCase__						,	)
          a											=   output.images[0, -3:, -3:, -1]

          # make sure that it's equal
          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()				).max() < 1e-4







     def     lowerCAmelCase_       (			self				)						->					Union[str, Any]:



          '''simple docstring'''



          super().test_attention_slicing_forward_pass(expected_max_diff=3e-3				)







     def     lowerCAmelCase_       (			self				)						->					Union[str, Any]:



          '''simple docstring'''



          super().test_inference_batch_single_identical(expected_max_diff=3e-3				)







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







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A						,	A="cpu"						,	A=torch.floataa						,	A=0				)						->					Dict:



          '''simple docstring'''



          a											=   torch.Generator(device=UpperCamelCase__				).manual_seed(UpperCamelCase__				)
          a											=   np.random.RandomState(UpperCamelCase__				).standard_normal((1, 4, 64, 64)				)
          a											=   torch.from_numpy(UpperCamelCase__				).to(device=UpperCamelCase__						,	dtype=UpperCamelCase__				)
          a											=   {
              "prompt": "a photograph of an astronaut riding a horse",
              "latents": latents,
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          a											=   DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"				)
          pipe.to(UpperCamelCase__				)
          pipe.set_progress_bar_config(disable=UpperCamelCase__				)

          a											=   self.get_inputs(UpperCamelCase__				)
          a											=   pipe(**UpperCamelCase__				).images
          a											=   image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          a											=   np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]				)
          assert np.abs(image_slice - expected_slice				).max() < 7e-3







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available




@dataclass
class     a__ (      UpperCamelCase__   ):
     a	:    Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
   from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
   from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline

