

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(	__a    ,  __a    ,  unittest.TestCase	):
							_UpperCAmelCase     =       StableDiffusionXLImgaImgPipeline
							_UpperCAmelCase     =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
							_UpperCAmelCase     =       PipelineTesterMixin.required_optional_params - {"latents"}
							_UpperCAmelCase     =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_UpperCAmelCase     =       IMAGE_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       IMAGE_TO_IMAGE_IMAGE_PARAMS
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		List[Any]:
											torch.manual_seed(0						)
											snake_case__									=     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       ,	)
											snake_case__									=     EulerDiscreteScheduler(
											    beta_start=0.00_085       ,	beta_end=0.012       ,	steps_offset=1       ,	beta_schedule='scaled_linear'       ,	timestep_spacing='leading'       ,	)
											torch.manual_seed(0						)
											snake_case__									=     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						)
											snake_case__									=     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       ,	)
											snake_case__									=     CLIPTextModel(a_						)
											snake_case__									=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       ,	local_files_only=a_						)

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

											snake_case__									=     {
											    """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 lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Dict=0						)		->		Union[str, Any]:
											snake_case__									=     floats_tensor((1, 3, 32, 32)       ,	rng=random.Random(a_						)						).to(a_						)
											snake_case__									=     image / 2 + 0.5
											if str(a_						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(a_						)
											else:
															snake_case__									=     torch.Generator(device=a_						).manual_seed(a_						)
											snake_case__									=     {
											    """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 lowerCAmelCase_ (     self:      int						)		->		Optional[int]:
											snake_case__									=     """cpu"""  # ensure determinism for the device-dependent torch.Generator
											snake_case__									=     self.get_dummy_components()
											snake_case__									=     StableDiffusionXLImgaImgPipeline(**a_						)
											snake_case__									=     sd_pipe.to(a_						)
											sd_pipe.set_progress_bar_config(disable=a_						)

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

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

											snake_case__									=     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 lowerCAmelCase_ (     self:      List[str]						)		->		int:
											super().test_attention_slicing_forward_pass(expected_max_diff=3e-3						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											super().test_inference_batch_single_identical(expected_max_diff=3e-3						)
							def lowerCAmelCase_ (     self:      str						)		->		int:
											pass


							def lowerCAmelCase_ (     self:      Dict						)		->		Union[str, Any]:
											snake_case__									=     self.get_dummy_components()
											snake_case__									=     StableDiffusionXLImgaImgPipeline(**a_						)
											snake_case__									=     sd_pipe.to(a_						)
											snake_case__									=     sd_pipe.to(a_						)
											sd_pipe.set_progress_bar_config(disable=a_						)

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

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

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

											(
											    snake_case__      
											)									=     sd_pipe.encode_prompt(a_       ,	negative_prompt=a_						)

											snake_case__									=     sd_pipe(
											    **a_       ,	prompt_embeds=a_       ,	negative_prompt_embeds=a_       ,	pooled_prompt_embeds=a_       ,	negative_pooled_prompt_embeds=a_       ,	)
											snake_case__									=     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 lowerCAmelCase_ (     self:      List[str]						)		->		str:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_ (     self:      List[str]       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Dict="cpu"       ,	UpperCamelCase:      str=torch.floataa       ,	UpperCamelCase:      str=0						)		->		Tuple:
											snake_case__									=     torch.Generator(device=a_						).manual_seed(a_						)
											snake_case__									=     np.random.RandomState(a_						).standard_normal((1, 4, 64, 64)						)
											snake_case__									=     torch.from_numpy(a_						).to(device=a_       ,	dtype=a_						)
											snake_case__									=     {
											    """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 lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[Any]:
											snake_case__									=     DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'						)
											pipe.to(a_						)
											pipe.set_progress_bar_config(disable=a_						)

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

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     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
def      a_ (      _A  )			->			bool:







				"""simple docstring"""
				return len(set(_A  )  ) == len(_A  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




