





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 				_UpperCamelCase       (						lowerCAmelCase_	,    lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       Any       = StableDiffusionXLPipeline
       _UpperCamelCase					:       Tuple       = TEXT_TO_IMAGE_PARAMS
       _UpperCamelCase					:       List[Any]       = TEXT_TO_IMAGE_BATCH_PARAMS
       _UpperCamelCase					:       Dict       = TEXT_TO_IMAGE_IMAGE_PARAMS
       _UpperCamelCase					:       Optional[int]       = TEXT_TO_IMAGE_IMAGE_PARAMS

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




              """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=_SCREAMING_SNAKE_CASE       , 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_00_85       , beta_end=0.0_12       , 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(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       , local_files_only=_SCREAMING_SNAKE_CASE		)

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

              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:			Any       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			int=0		)							->			List[Any]:




              """simple docstring"""


              if str(_SCREAMING_SNAKE_CASE		).startswith("mps"		):
                     UpperCamelCase_ 							=	torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_ 							=	torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              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:			List[Any]		)							->			int:




              """simple docstring"""


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

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

              assert image.shape == (1, 64, 64, 3)
              UpperCamelCase_ 							=	np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]		)

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

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




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_dummy_components()
              UpperCamelCase_ 							=	StableDiffusionXLPipeline(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	sd_pipe.to(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	sd_pipe.to(_SCREAMING_SNAKE_CASE		)
              sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

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

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

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

              (
                  (
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			
              ) 							=	sd_pipe.encode_prompt(_SCREAMING_SNAKE_CASE       , negative_prompt=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	sd_pipe(
                  **_SCREAMING_SNAKE_CASE       , prompt_embeds=_SCREAMING_SNAKE_CASE       , negative_prompt_embeds=_SCREAMING_SNAKE_CASE       , pooled_prompt_embeds=_SCREAMING_SNAKE_CASE       , negative_pooled_prompt_embeds=_SCREAMING_SNAKE_CASE       , )
              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]		)							->			List[str]:




              """simple docstring"""


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



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




              """simple docstring"""


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



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

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




              """simple docstring"""


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

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




              """simple docstring"""


              UpperCamelCase_ 							=	torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	np.random.RandomState(_SCREAMING_SNAKE_CASE		).standard_normal((1, 4, 64, 64)		)
              UpperCamelCase_ 							=	torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE       , dtype=_SCREAMING_SNAKE_CASE		)
              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]		)							->			Optional[int]:




              """simple docstring"""


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

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

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase_ 							=	np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]		)
              assert np.abs(image_slice - expected_slice		).max() < 7e-3








from itertools import compress, repeat
from math import ceil, sqrt




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		list[int]:

       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       UpperCamelCase_ 							=	bytearray(b"\x01"	) * ((num >> 1) - 1)

       for i in range(3       ,		int(sqrt(UpperCamelCase_	)	) + 1       ,		2	):
              if sieve[(i >> 1) - 1]:
                     UpperCamelCase_ 							=	i**2
                     UpperCamelCase_ 							=	repeat(
                         0       ,		ceil((num - i_squared) / (i << 1)	)	)

       return [2] + list(compress(range(3       ,		UpperCamelCase_       ,		2	)       ,		UpperCamelCase_	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

