
'''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 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							StableDiffusionXLImgaImgPipeline
				lowerCAmelCase_     			=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
				lowerCAmelCase_     			=							PipelineTesterMixin.required_optional_params - {"""latents"""}
				lowerCAmelCase_     			=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowerCAmelCase_     			=							IMAGE_TO_IMAGE_IMAGE_PARAMS
				lowerCAmelCase_     			=							IMAGE_TO_IMAGE_IMAGE_PARAMS







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''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=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						,				)
											UpperCamelCase   			=     EulerDiscreteScheduler(
											    beta_start=0.00_085						,				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(A_     )
											UpperCamelCase   			=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						,				local_files_only=A_     )

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

											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 						UpperCAmelCase_    (       self						,				A_						,				A_=0     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     floats_tensor((1, 3, 32, 32)						,				rng=random.Random(A_     )     ).to(A_     )
											UpperCamelCase   			=     image / 2 + 0.5
											if str(A_     ).startswith('mps'     ):
																		UpperCamelCase   			=     torch.manual_seed(A_     )
											else:
																		UpperCamelCase   			=     torch.Generator(device=A_     ).manual_seed(A_     )
											UpperCamelCase   			=     {
											    '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 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     'cpu'  # ensure determinism for the device-dependent torch.Generator
											UpperCamelCase   			=     self.get_dummy_components()
											UpperCamelCase   			=     StableDiffusionXLImgaImgPipeline(**A_     )
											UpperCamelCase   			=     sd_pipe.to(A_     )
											sd_pipe.set_progress_bar_config(disable=A_     )

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

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

											UpperCamelCase   			=     np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]     )

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







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






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







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







											'''simple docstring'''






											pass







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.get_dummy_components()
											UpperCamelCase   			=     StableDiffusionXLImgaImgPipeline(**A_     )
											UpperCamelCase   			=     sd_pipe.to(A_     )
											UpperCamelCase   			=     sd_pipe.to(A_     )
											sd_pipe.set_progress_bar_config(disable=A_     )

											# forward without prompt embeds
											UpperCamelCase   			=     self.get_dummy_inputs(A_     )
											UpperCamelCase   			=     3 * ['this is a negative prompt']
											UpperCamelCase   			=     negative_prompt
											UpperCamelCase   			=     3 * [inputs['prompt']]

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

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

											(
											    (
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	
											)   			=     sd_pipe.encode_prompt(A_						,				negative_prompt=A_     )

											UpperCamelCase   			=     sd_pipe(
											    **A_						,				prompt_embeds=A_						,				negative_prompt_embeds=A_						,				pooled_prompt_embeds=A_						,				negative_pooled_prompt_embeds=A_						,				)
											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


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







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_="cpu"						,				A_=torch.floataa						,				A_=0     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     torch.Generator(device=A_     ).manual_seed(A_     )
											UpperCamelCase   			=     np.random.RandomState(A_     ).standard_normal((1, 4, 64, 64)     )
											UpperCamelCase   			=     torch.from_numpy(A_     ).to(device=A_						,				dtype=A_     )
											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 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






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

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

											assert image.shape == (1, 512, 512, 3)
											UpperCamelCase   			=     np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]     )
											assert np.abs(image_slice - expected_slice     ).max() < 7e-3









from __future__ import annotations

import numpy as np

def        __a			(   __lowerCAmelCase   )     ->     Optional[Any]:
					return np.maximum(0      ,	__lowerCAmelCase   )


if __name__ == "__main__":
			print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]