





import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   A			(__a   ,     __a   ,     unittest.TestCase    ):
			'''simple docstring'''




			__lowerCamelCase	:       Union[str, Any]							=    StableDiffusionXLImgaImgPipeline
			__lowerCamelCase	:       List[str]							=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
			__lowerCamelCase	:       Union[str, Any]							=    PipelineTesterMixin.required_optional_params - {'''latents'''}
			__lowerCamelCase	:       Dict							=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__lowerCamelCase	:       Union[str, Any]							=    IMAGE_TO_IMAGE_IMAGE_PARAMS
			__lowerCamelCase	:       str							=    IMAGE_TO_IMAGE_IMAGE_PARAMS






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







					"""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=lowerCAmelCase__							,	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=1_28							,	)
					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=10_00							,	hidden_act="""gelu"""							,	projection_dim=32							,	)
					A__        =   CLIPTextModel(lowerCAmelCase__   )
					A__        =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							,	local_files_only=lowerCAmelCase__   )

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

					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    a_       (			self   :    int							,	__lowerCAmelCase   :    Union[str, Any]							,	__lowerCAmelCase   :    Any=0   )	->	Optional[int]:







					"""simple docstring"""






					A__        =   floats_tensor((1, 3, 32, 32)							,	rng=random.Random(lowerCAmelCase__   )   ).to(lowerCAmelCase__   )
					A__        =   image / 2 + 0.5
					if str(lowerCAmelCase__   ).startswith("""mps"""   ):
							A__        =   torch.manual_seed(lowerCAmelCase__   )
					else:
							A__        =   torch.Generator(device=lowerCAmelCase__   ).manual_seed(lowerCAmelCase__   )
					A__        =   {
					    """prompt""": """A painting of a squirrel eating a burger""",
					    """image""": image,
					    """generator""": generator,
					    """num_inference_steps""": 2,
					    """guidance_scale""": 5.0,
					    """output_type""": """numpy""",
					    """strength""": 0.7_5,
					}
					return inputs






			def    a_       (			self   :    Optional[Any]   )	->	int:







					"""simple docstring"""






					A__        =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
					A__        =   self.get_dummy_components()
					A__        =   StableDiffusionXLImgaImgPipeline(**lowerCAmelCase__   )
					A__        =   sd_pipe.to(lowerCAmelCase__   )
					sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

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

					assert image.shape == (1, 32, 32, 3)

					A__        =   np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]   )

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






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







					"""simple docstring"""






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






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







					"""simple docstring"""






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






			def    a_       (			self   :    List[str]   )	->	List[Any]:







					"""simple docstring"""






					pass







			def    a_       (			self   :    Tuple   )	->	Dict:







					"""simple docstring"""






					A__        =   self.get_dummy_components()
					A__        =   StableDiffusionXLImgaImgPipeline(**lowerCAmelCase__   )
					A__        =   sd_pipe.to(lowerCAmelCase__   )
					A__        =   sd_pipe.to(lowerCAmelCase__   )
					sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

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

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

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

					(
					    (
					    A__
					)      ,			(
					    A__
					)      ,			(
					    A__
					)      ,			(
					    A__
					)      ,			
					)        =   sd_pipe.encode_prompt(lowerCAmelCase__							,	negative_prompt=lowerCAmelCase__   )

					A__        =   sd_pipe(
					    **lowerCAmelCase__							,	prompt_embeds=lowerCAmelCase__							,	negative_prompt_embeds=lowerCAmelCase__							,	pooled_prompt_embeds=lowerCAmelCase__							,	negative_pooled_prompt_embeds=lowerCAmelCase__							,	)
					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


@slow
@require_torch_gpu
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			def    a_       (			self   :    str   )	->	Any:







					"""simple docstring"""






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






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Any="cpu"							,	__lowerCAmelCase   :    str=torch.floataa							,	__lowerCAmelCase   :    Optional[Any]=0   )	->	List[str]:







					"""simple docstring"""






					A__        =   torch.Generator(device=lowerCAmelCase__   ).manual_seed(lowerCAmelCase__   )
					A__        =   np.random.RandomState(lowerCAmelCase__   ).standard_normal((1, 4, 64, 64)   )
					A__        =   torch.from_numpy(lowerCAmelCase__   ).to(device=lowerCAmelCase__							,	dtype=lowerCAmelCase__   )
					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    a_       (			self   :    List[str]   )	->	List[str]:







					"""simple docstring"""






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

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

					assert image.shape == (1, 5_12, 5_12, 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





"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_ = 10**9				):
		__SCREAMING_SNAKE_CASE            = 1
		__SCREAMING_SNAKE_CASE            = 2

		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = 0
		while perimeter <= max_perimeter:
				perimeters_sum += perimeter

				prev_value += 2 * value
				value += prev_value

				__SCREAMING_SNAKE_CASE            = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
				i += 1

		return perimeters_sum


if __name__ == "__main__":
							print(F"""{solution() = }""")




