




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 (     UpperCamelCase_       ,						UpperCamelCase_       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Any       	=		StableDiffusionXLImgaImgPipeline
						UpperCamelCase_	:       str       	=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
						UpperCamelCase_	:       Optional[int]       	=		PipelineTesterMixin.required_optional_params - {'latents'}
						UpperCamelCase_	:       List[str]       	=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						UpperCamelCase_	:       Tuple       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase_	:       List[Any]       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS






						def     UpperCAmelCase_      ( self :     str				)    ->						List[str]:
													"""simple docstring"""


													torch.manual_seed(0				)
													__lowercase      =    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=__A				,							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				,							)
													__lowercase      =    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				)
													__lowercase      =    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				)
													__lowercase      =    CLIPTextConfig(
													    bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-0_5				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_000				,							hidden_act='''gelu'''				,							projection_dim=32				,							)
													__lowercase      =    CLIPTextModel(__A				)
													__lowercase      =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,							local_files_only=__A				)

													__lowercase      =    CLIPTextModelWithProjection(__A				)
													__lowercase      =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,							local_files_only=__A				)

													__lowercase      =    {
													    "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     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[int]=0				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    floats_tensor((1, 3, 32, 32)				,							rng=random.Random(__A				)				).to(__A				)
													__lowercase      =    image / 2 + 0.5
													if str(__A				).startswith('''mps'''				):
																				__lowercase      =    torch.manual_seed(__A				)
													else:
																				__lowercase      =    torch.Generator(device=__A				).manual_seed(__A				)
													__lowercase      =    {
													    "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     UpperCAmelCase_      ( self :     Tuple				)    ->						Dict:
													"""simple docstring"""


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

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

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

													__lowercase      =    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     UpperCAmelCase_      ( self :     Tuple				)    ->						Union[str, Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						str:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						Optional[int]:
													"""simple docstring"""


													pass






						def     UpperCAmelCase_      ( self :     int				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_dummy_components()
													__lowercase      =    StableDiffusionXLImgaImgPipeline(**__A				)
													__lowercase      =    sd_pipe.to(__A				)
													__lowercase      =    sd_pipe.to(__A				)
													sd_pipe.set_progress_bar_config(disable=__A				)

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

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

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

													(
													    __lowercase		
													)      =    sd_pipe.encode_prompt(__A				,							negative_prompt=__A				)

													__lowercase      =    sd_pipe(
													    **__A				,							prompt_embeds=__A				,							negative_prompt_embeds=__A				,							pooled_prompt_embeds=__A				,							negative_pooled_prompt_embeds=__A				,							)
													__lowercase      =    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     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						str:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     int="cpu"				,							lowerCamelCase__ :     str=torch.floataa				,							lowerCamelCase__ :     Tuple=0				)    ->						Tuple:
													"""simple docstring"""


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


													__lowercase      =    DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''				)
													pipe.to(__A				)
													pipe.set_progress_bar_config(disable=__A				)

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

													assert image.shape == (1, 512, 512, 3)
													__lowercase      =    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 __future__ import annotations







def 		_A(       UpperCamelCase__							:			list[int]				,       UpperCamelCase__							:			list[int]				,       UpperCamelCase__							:			int				)    -> tuple[float, list[float]]:
							'''simple docstring'''


							__lowercase      =    list(range(len(UpperCamelCase__				)				)				)
							__lowercase      =    [v / w for v, w in zip(UpperCamelCase__				,       UpperCamelCase__				)]
							index.sort(key=lambda UpperCamelCase__				: ratio[i]				,       reverse=UpperCamelCase__				)

							__lowercase      =    0
							__lowercase      =    [0] * len(UpperCamelCase__				)
							for i in index:
														if weight[i] <= capacity:
																					__lowercase      =    1
																					max_value += value[i]
																					capacity -= weight[i]
														else:
																					__lowercase      =    capacity / weight[i]
																					max_value += value[i] * capacity / weight[i]
																					break

							return max_value, fractions


if __name__ == "__main__":
			import doctest

			doctest.testmod()






