


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


							def 			_A       (self					):
									torch.manual_seed(0					)
									__lowercase=			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=lowerCAmelCase      ,				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      ,				)
									__lowercase=			EulerDiscreteScheduler(
									    beta_start=0.0_00_85      ,				beta_end=0.0_12      ,				steps_offset=1      ,				beta_schedule='scaled_linear'      ,				timestep_spacing='leading'      ,				)
									torch.manual_seed(0					)
									__lowercase=			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					)
									__lowercase=			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      ,				)
									__lowercase=			CLIPTextModel(lowerCAmelCase					)
									__lowercase=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      ,				local_files_only=lowerCAmelCase					)

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

									__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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase=0					):
									__lowercase=			floats_tensor((1, 3, 3_2, 3_2)      ,				rng=random.Random(lowerCAmelCase					)					).to(lowerCAmelCase					)
									__lowercase=			image / 2 + 0.5
									if str(lowerCAmelCase					).startswith('mps'					):
											__lowercase=			torch.manual_seed(lowerCAmelCase					)
									else:
											__lowercase=			torch.Generator(device=lowerCAmelCase					).manual_seed(lowerCAmelCase					)
									__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': 0.75,
									}
									return inputs


							def 			_A       (self					):
									__lowercase=			'cpu'  # ensure determinism for the device-dependent torch.Generator
									__lowercase=			self.get_dummy_components()
									__lowercase=			StableDiffusionXLImgaImgPipeline(**lowerCAmelCase					)
									__lowercase=			sd_pipe.to(lowerCAmelCase					)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

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

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

									__lowercase=			np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]					)

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


							def 			_A       (self					):
									super().test_attention_slicing_forward_pass(expected_max_diff=3E-3					)


							def 			_A       (self					):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3					)


							def 			_A       (self					):
									pass







							def 			_A       (self					):
									__lowercase=			self.get_dummy_components()
									__lowercase=			StableDiffusionXLImgaImgPipeline(**lowerCAmelCase					)
									__lowercase=			sd_pipe.to(lowerCAmelCase					)
									__lowercase=			sd_pipe.to(lowerCAmelCase					)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

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

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

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

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

									__lowercase=			sd_pipe(
									    **lowerCAmelCase      ,				prompt_embeds=lowerCAmelCase      ,				negative_prompt_embeds=lowerCAmelCase      ,				pooled_prompt_embeds=lowerCAmelCase      ,				negative_pooled_prompt_embeds=lowerCAmelCase      ,				)
									__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 ):


							def 			_A       (self					):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase="cpu"      ,				lowerCAmelCase=torch.floataa      ,				lowerCAmelCase=0					):
									__lowercase=			torch.Generator(device=lowerCAmelCase					).manual_seed(lowerCAmelCase					)
									__lowercase=			np.random.RandomState(lowerCAmelCase					).standard_normal((1, 4, 6_4, 6_4)					)
									__lowercase=			torch.from_numpy(lowerCAmelCase					).to(device=lowerCAmelCase      ,				dtype=lowerCAmelCase					)
									__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 			_A       (self					):
									__lowercase=			DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'					)
									pipe.to(lowerCAmelCase					)
									pipe.set_progress_bar_config(disable=lowerCAmelCase					)

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

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									__lowercase=			np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]					)
									assert np.abs(image_slice - expected_slice					).max() < 7E-3










def        _lowerCamelCase(	lowercase__   , lowercase__ = " "	)						->	list:

		'''simple docstring'''







		__lowercase=			[]

		__lowercase=			0
		for index, char in enumerate(lowercase__	):
				if char == separator:
						split_words.append(string[last_index:index]	)
						__lowercase=			index + 1
				elif index + 1 == len(lowercase__	):
						split_words.append(string[last_index : index + 1]	)
		return split_words


if __name__ == "__main__":
	from doctest import testmod

	testmod()






