






"""simple docstring"""



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 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =StableDiffusionXLImgaImgPipeline
   SCREAMING_SNAKE_CASE_    =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   SCREAMING_SNAKE_CASE_    =PipelineTesterMixin.required_optional_params - {'''latents'''}
   SCREAMING_SNAKE_CASE_    =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   SCREAMING_SNAKE_CASE_    =IMAGE_TO_IMAGE_IMAGE_PARAMS
   SCREAMING_SNAKE_CASE_    =IMAGE_TO_IMAGE_IMAGE_PARAMS
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        torch.manual_seed(0							)
        UpperCAmelCase__						:    int							=							UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)							,			layers_per_block=2							,			sample_size=3_2							,			in_channels=4							,			out_channels=4							,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,			attention_head_dim=(2, 4)							,			use_linear_projection=snake_case__							,			addition_embed_type="text_time"							,			addition_time_embed_dim=8							,			transformer_layers_per_block=(1, 2)							,			projection_class_embeddings_input_dim=8_0							,			cross_attention_dim=6_4							,			)
        UpperCAmelCase__						:    Union[str, Any]							=							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__						:    Union[str, Any]							=							AutoencoderKL(
            block_out_channels=[3_2, 6_4]							,			in_channels=3							,			out_channels=3							,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,			latent_channels=4							,			sample_size=1_2_8							,			)
        torch.manual_seed(0							)
        UpperCAmelCase__						:    Optional[int]							=							CLIPTextConfig(
            bos_token_id=0							,			eos_token_id=2							,			hidden_size=3_2							,			intermediate_size=3_7							,			layer_norm_eps=1e-05							,			num_attention_heads=4							,			num_hidden_layers=5							,			pad_token_id=1							,			vocab_size=1_0_0_0							,			hidden_act="gelu"							,			projection_dim=3_2							,			)
        UpperCAmelCase__						:    List[Any]							=							CLIPTextModel(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							,			local_files_only=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							CLIPTextModelWithProjection(snake_case__							)
        UpperCAmelCase__						:    Dict							=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							,			local_files_only=snake_case__							)

        UpperCAmelCase__						:    Tuple							=							{
            "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      :	List[str]							,			snake_case__      :	str							,			snake_case__      :	str=0							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							floats_tensor((1, 3, 3_2, 3_2)							,			rng=random.Random(snake_case__							)							).to(snake_case__							)
        UpperCAmelCase__						:    Any							=							image / 2 + 0.5
        if str(snake_case__							).startswith("mps"							):
             UpperCAmelCase__						:    Any							=							torch.manual_seed(snake_case__							)
        else:
             UpperCAmelCase__						:    Optional[Any]							=							torch.Generator(device=snake_case__							).manual_seed(snake_case__							)
        UpperCAmelCase__						:    str							=							{
            "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": 0.75,
        }
        return inputs
   def       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							"cpu"  # ensure determinism for the device-dependent torch.Generator
        UpperCAmelCase__						:    List[Any]							=							self.get_dummy_components()
        UpperCAmelCase__						:    Union[str, Any]							=							StableDiffusionXLImgaImgPipeline(**snake_case__							)
        UpperCAmelCase__						:    Any							=							sd_pipe.to(snake_case__							)
        sd_pipe.set_progress_bar_config(disable=snake_case__							)

        UpperCAmelCase__						:    int							=							self.get_dummy_inputs(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							sd_pipe(**snake_case__							).images
        UpperCAmelCase__						:    List[Any]							=							image[0, -3:, -3:, -1]

        assert image.shape == (1, 3_2, 3_2, 3)

        UpperCAmelCase__						:    Optional[int]							=							np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        super().test_attention_slicing_forward_pass(expected_max_diff=3e-3							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        super().test_inference_batch_single_identical(expected_max_diff=3e-3							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        pass




   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.get_dummy_components()
        UpperCAmelCase__						:    List[str]							=							StableDiffusionXLImgaImgPipeline(**snake_case__							)
        UpperCAmelCase__						:    List[Any]							=							sd_pipe.to(snake_case__							)
        UpperCAmelCase__						:    int							=							sd_pipe.to(snake_case__							)
        sd_pipe.set_progress_bar_config(disable=snake_case__							)

        # forward without prompt embeds
        UpperCAmelCase__						:    Any							=							self.get_dummy_inputs(snake_case__							)
        UpperCAmelCase__						:    str							=							3 * ["this is a negative prompt"]
        UpperCAmelCase__						:    List[str]							=							negative_prompt
        UpperCAmelCase__						:    Tuple							=							3 * [inputs["prompt"]]

        UpperCAmelCase__						:    Optional[int]							=							sd_pipe(**snake_case__							)
        UpperCAmelCase__						:    List[str]							=							output.images[0, -3:, -3:, -1]

        # forward with prompt embeds
        UpperCAmelCase__						:    str							=							self.get_dummy_inputs(snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							3 * ["this is a negative prompt"]
        UpperCAmelCase__						:    Optional[Any]							=							3 * [inputs.pop("prompt"							)]

        (
            (
            UpperCAmelCase__
        )							,				(
            UpperCAmelCase__
        )							,				(
            UpperCAmelCase__
        )							,				(
            UpperCAmelCase__
        )							,				
        )						:    Tuple							=							sd_pipe.encode_prompt(snake_case__							,			negative_prompt=snake_case__							)

        UpperCAmelCase__						:    Tuple							=							sd_pipe(
            **snake_case__							,			prompt_embeds=snake_case__							,			negative_prompt_embeds=snake_case__							,			pooled_prompt_embeds=snake_case__							,			negative_pooled_prompt_embeds=snake_case__							,			)
        UpperCAmelCase__						:    int							=							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 			lowerCAmelCase__			(    unittest.TestCase   ):
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   def       __a							(		self      :	str							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	Any="cpu"							,			snake_case__      :	Optional[Any]=torch.floataa							,			snake_case__      :	Optional[int]=0							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							torch.Generator(device=snake_case__							).manual_seed(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							np.random.RandomState(snake_case__							).standard_normal((1, 4, 6_4, 6_4)							)
        UpperCAmelCase__						:    Optional[int]							=							torch.from_numpy(snake_case__							).to(device=snake_case__							,			dtype=snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							{
            "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      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"							)
        pipe.to(snake_case__							)
        pipe.set_progress_bar_config(disable=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							self.get_inputs(snake_case__							)
        UpperCAmelCase__						:    Union[str, Any]							=							pipe(**snake_case__							).images
        UpperCAmelCase__						:    Union[str, Any]							=							image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        UpperCAmelCase__						:    Any							=							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










"""simple docstring"""



from __future__ import annotations

from math import pow, sqrt


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : float			,					snake_case       : float			,					snake_case       : float				)->		dict[str, float]:






     '''simple docstring'''



     if (resistance, reactance, impedance).count(0				) != 1:
          raise ValueError("One and only one argument must be 0"				)
     if resistance == 0:
          return {"resistance": sqrt(pow(snake_case			,					2				) - pow(snake_case			,					2				)				)}
     elif reactance == 0:
          return {"reactance": sqrt(pow(snake_case			,					2				) - pow(snake_case			,					2				)				)}
     elif impedance == 0:
          return {"impedance": sqrt(pow(snake_case			,					2				) + pow(snake_case			,					2				)				)}
     else:
          raise ValueError("Exactly one argument must be 0"				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


