



"""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       lowercase__   (    __snake_case				,					__snake_case				,					unittest.TestCase      ):







       '''simple docstring'''


       UpperCamelCase						=     StableDiffusionXLPipeline
       UpperCamelCase						=     TEXT_TO_IMAGE_PARAMS
       UpperCamelCase						=     TEXT_TO_IMAGE_BATCH_PARAMS
       UpperCamelCase						=     TEXT_TO_IMAGE_IMAGE_PARAMS
       UpperCamelCase						=     TEXT_TO_IMAGE_IMAGE_PARAMS



       def      lowercase__				(							self			:  List[Any]    ) ->						int:


             '''simple docstring'''




             torch.manual_seed(0    )
             UpperCAmelCase_			 =							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=lowercase_		,				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		,				)
             UpperCAmelCase_			 =							EulerDiscreteScheduler(
                 beta_start=0.0_0085		,				beta_end=0.012		,				steps_offset=1		,				beta_schedule="scaled_linear"		,				timestep_spacing="leading"		,				)
             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=32		,				)
             UpperCAmelCase_			 =							CLIPTextModel(lowercase_    )
             UpperCAmelCase_			 =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		,				local_files_only=lowercase_    )

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

             UpperCAmelCase_			 =							{
                 "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      lowercase__				(							self			:  List[Any]		,				_UpperCAmelCase			:  Any		,				_UpperCAmelCase			:  Dict=0    ) ->						Any:


             '''simple docstring'''




             if str(lowercase_    ).startswith("mps"    ):
                   UpperCAmelCase_			 =							torch.manual_seed(lowercase_    )
             else:
                   UpperCAmelCase_			 =							torch.Generator(device=lowercase_    ).manual_seed(lowercase_    )
             UpperCAmelCase_			 =							{
                 "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      lowercase__				(							self			:  Optional[int]    ) ->						Any:


             '''simple docstring'''




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

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

             assert image.shape == (1, 64, 64, 3)
             UpperCAmelCase_			 =							np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]    )

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



       def      lowercase__				(							self			:  List[str]    ) ->						int:


             '''simple docstring'''




             UpperCAmelCase_			 =							self.get_dummy_components()
             UpperCAmelCase_			 =							StableDiffusionXLPipeline(**lowercase_    )
             UpperCAmelCase_			 =							sd_pipe.to(lowercase_    )
             UpperCAmelCase_			 =							sd_pipe.to(lowercase_    )
             sd_pipe.set_progress_bar_config(disable=lowercase_    )

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

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

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

             (
                 UpperCAmelCase_
             )			 =							sd_pipe.encode_prompt(lowercase_		,				negative_prompt=lowercase_    )

             UpperCAmelCase_			 =							sd_pipe(
                 **lowercase_		,				prompt_embeds=lowercase_		,				negative_prompt_embeds=lowercase_		,				pooled_prompt_embeds=lowercase_		,				negative_pooled_prompt_embeds=lowercase_		,				)
             UpperCAmelCase_			 =							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      lowercase__				(							self			:  Optional[Any]    ) ->						Union[str, Any]:


             '''simple docstring'''




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







       def      lowercase__				(							self			:  Optional[int]    ) ->						Dict:


             '''simple docstring'''




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



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







       '''simple docstring'''


       def      lowercase__				(							self			:  Optional[Any]    ) ->						Dict:


             '''simple docstring'''




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



       def      lowercase__				(							self			:  int		,				_UpperCAmelCase			:  Union[str, Any]		,				_UpperCAmelCase			:  Union[str, Any]="cpu"		,				_UpperCAmelCase			:  Optional[int]=torch.floataa		,				_UpperCAmelCase			:  List[str]=0    ) ->						Any:


             '''simple docstring'''




             UpperCAmelCase_			 =							torch.Generator(device=lowercase_    ).manual_seed(lowercase_    )
             UpperCAmelCase_			 =							np.random.RandomState(lowercase_    ).standard_normal((1, 4, 64, 64)    )
             UpperCAmelCase_			 =							torch.from_numpy(lowercase_    ).to(device=lowercase_		,				dtype=lowercase_    )
             UpperCAmelCase_			 =							{
                 "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      lowercase__				(							self			:  Optional[int]    ) ->						int:


             '''simple docstring'''




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

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

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_			 =							np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]    )
             assert np.abs(image_slice - expected_slice    ).max() < 7e-3





def 					snake_case						(__lowercase						)       ->       int:

						'''simple docstring'''



						if not isinstance(__lowercase					,			__lowercase						) or number < 0:
												raise ValueError("Input must be a non-negative integer"						)

						_snake_case						:     List[Any]				=    number & ~(number - 1)
						_snake_case						:     Union[str, Any]				=    0
						while intermediate:
												intermediate >>= 1
												index += 1
						return index - 1


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)