


'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCAmelCase				:Tuple        =      logging.get_logger(__name__)
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Any  ,					*_A		:						List[Any]  ,					**_A		:						str      )  ->			None:
			warnings.warn(
			    'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
			    ' use BeitImageProcessor instead.'  ,					_A  ,					)
			super().__init__(*_A  ,					**_A      )






'''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	(						lowercase__		,					lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      int				   =    StableDiffusionXLImgaImgPipeline
		A_     :      str				   =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
		A_     :      Optional[Any]				   =    PipelineTesterMixin.required_optional_params - {"""latents"""}
		A_     :      Any				   =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		A_     :      Any				   =    IMAGE_TO_IMAGE_IMAGE_PARAMS
		A_     :      Optional[int]				   =    IMAGE_TO_IMAGE_IMAGE_PARAMS







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Optional[int]:
			torch.manual_seed(0      )
			__magic_name__	:   int					     =					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  ,					)
			__magic_name__	:   str					     =					EulerDiscreteScheduler(
			    beta_start=0.0_0085  ,					beta_end=0.012  ,					steps_offset=1  ,					beta_schedule='scaled_linear'  ,					timestep_spacing='leading'  ,					)
			torch.manual_seed(0      )
			__magic_name__	:   str					     =					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      )
			__magic_name__	:   Any					     =					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  ,					)
			__magic_name__	:   Dict					     =					CLIPTextModel(_A      )
			__magic_name__	:   List[str]					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  ,					local_files_only=_A      )

			__magic_name__	:   Optional[Any]					     =					CLIPTextModelWithProjection(_A      )
			__magic_name__	:   Dict					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  ,					local_files_only=_A      )

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

			__magic_name__	:   Any					     =					self.get_dummy_inputs(_A      )
			__magic_name__	:   Optional[int]					     =					sd_pipe(**_A      ).images
			__magic_name__	:   str					     =					image[0, -3:, -3:, -1]

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

			__magic_name__	:   Any					     =					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        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Optional[Any]:
			super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			int:
			super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







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







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Optional[int]:
			__magic_name__	:   Dict					     =					self.get_dummy_components()
			__magic_name__	:   Optional[Any]					     =					StableDiffusionXLImgaImgPipeline(**_A      )
			__magic_name__	:   List[Any]					     =					sd_pipe.to(_A      )
			__magic_name__	:   str					     =					sd_pipe.to(_A      )
			sd_pipe.set_progress_bar_config(disable=_A      )

			# forward without prompt embeds
			__magic_name__	:   Union[str, Any]					     =					self.get_dummy_inputs(_A      )
			__magic_name__	:   Union[str, Any]					     =					3 * ['this is a negative prompt']
			__magic_name__	:   List[str]					     =					negative_prompt
			__magic_name__	:   int					     =					3 * [inputs['prompt']]

			__magic_name__	:   Tuple					     =					sd_pipe(**_A      )
			__magic_name__	:   str					     =					output.images[0, -3:, -3:, -1]

			# forward with prompt embeds
			__magic_name__	:   Optional[Any]					     =					self.get_dummy_inputs(_A      )
			__magic_name__	:   Tuple					     =					3 * ['this is a negative prompt']
			__magic_name__	:   List[str]					     =					3 * [inputs.pop('prompt'      )]

			(
			    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    
			)	:   List[Any]					     =					sd_pipe.encode_prompt(_A  ,					negative_prompt=_A      )

			__magic_name__	:   Tuple					     =					sd_pipe(
			    **_A  ,					prompt_embeds=_A  ,					negative_prompt_embeds=_A  ,					pooled_prompt_embeds=_A  ,					negative_pooled_prompt_embeds=_A  ,					)
			__magic_name__	:   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							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Tuple:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Optional[int]  ,					_A		:						Optional[Any]="cpu"  ,					_A		:						List[str]=torch.floataa  ,					_A		:						Any=0      )  ->			str:
			__magic_name__	:   List[str]					     =					torch.Generator(device=_A      ).manual_seed(_A      )
			__magic_name__	:   Optional[Any]					     =					np.random.RandomState(_A      ).standard_normal((1, 4, 64, 64)      )
			__magic_name__	:   Union[str, Any]					     =					torch.from_numpy(_A      ).to(device=_A  ,					dtype=_A      )
			__magic_name__	:   Optional[int]					     =					{
			    '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]      )  ->			Dict:
			__magic_name__	:   str					     =					DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

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

			assert image.shape == (1, 512, 512, 3)
			__magic_name__	:   List[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