





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      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase							,					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 		lowerCAmelCase     (						self					)						->							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=lowerCAmelCase_			,  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=0.0_00_85			,  beta_end=0.0_12			,  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=128			,  )
										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=1000			,  hidden_act='gelu'			,  projection_dim=32			,  )
										_snake_case													=			CLIPTextModel(lowerCAmelCase_					)
										_snake_case													=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			,  local_files_only=lowerCAmelCase_					)

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

										_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			,  lowerCAmelCase_			,  lowerCAmelCase_=0					)						->							List[str]:
										_snake_case													=			floats_tensor((1, 3, 32, 32)			,  rng=random.Random(lowerCAmelCase_					)					).to(lowerCAmelCase_					)
										_snake_case													=			image / 2 + 0.5
										if str(lowerCAmelCase_					).startswith('mps'					):
															_snake_case													=			torch.manual_seed(lowerCAmelCase_					)
										else:
															_snake_case													=			torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
										_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:
										_snake_case													=			'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case													=			self.get_dummy_components()
										_snake_case													=			StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_					)
										_snake_case													=			sd_pipe.to(lowerCAmelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

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

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

										_snake_case													=			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 		lowerCAmelCase     (						self					)						->							Dict:
										super().test_attention_slicing_forward_pass(expected_max_diff=3E-3					)




					def 		lowerCAmelCase     (						self					)						->							int:
										super().test_inference_batch_single_identical(expected_max_diff=3E-3					)




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										pass






					def 		lowerCAmelCase     (						self					)						->							Any:
										_snake_case													=			self.get_dummy_components()
										_snake_case													=			StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_					)
										_snake_case													=			sd_pipe.to(lowerCAmelCase_					)
										_snake_case													=			sd_pipe.to(lowerCAmelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

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

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

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

										(
										    (
										    _snake_case
										)							,       (
										    _snake_case
										)							,       (
										    _snake_case
										)							,       (
										    _snake_case
										)							,       
										)													=			sd_pipe.encode_prompt(lowerCAmelCase_			,  negative_prompt=lowerCAmelCase_					)

										_snake_case													=			sd_pipe(
										    **lowerCAmelCase_			,  prompt_embeds=lowerCAmelCase_			,  negative_prompt_embeds=lowerCAmelCase_			,  pooled_prompt_embeds=lowerCAmelCase_			,  negative_pooled_prompt_embeds=lowerCAmelCase_			,  )
										_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      UpperCamelCase_						(					unittest.TestCase   ):




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_="cpu"			,  lowerCAmelCase_=torch.floataa			,  lowerCAmelCase_=0					)						->							List[str]:
										_snake_case													=			torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
										_snake_case													=			np.random.RandomState(lowerCAmelCase_					).standard_normal((1, 4, 64, 64)					)
										_snake_case													=			torch.from_numpy(lowerCAmelCase_					).to(device=lowerCAmelCase_			,  dtype=lowerCAmelCase_					)
										_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					)						->							str:
										_snake_case													=			DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'					)
										pipe.to(lowerCAmelCase_					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

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

										assert image.shape == (1, 512, 512, 3)
										_snake_case													=			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










import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


UpperCAmelCase_ 							=       logging.get_logger(__name__)
class      UpperCamelCase_						(					_lowerCamelCase   ):




					def __init__(						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							None:
										warnings.warn(
										    'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
										    ' use FlavaImageProcessor instead.'			,  lowerCAmelCase_			,  )
										super().__init__(*lowerCAmelCase_			,  **lowerCAmelCase_					)



