
'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class  lowerCAmelCase__   (  lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     KandinskyInpaintPipeline
					lowerCAmelCase_        =     ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
					lowerCAmelCase_        =     [
					    '''prompt''',
					    '''negative_prompt''',
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					    '''mask_image''',
					]
					lowerCAmelCase_        =     [
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''latents''',
					    '''guidance_scale''',
					    '''negative_prompt''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					lowerCAmelCase_        =     False






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return 32






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return 32






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.time_input_dim






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.time_input_dim * 4






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return 1_00






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''		)
											return tokenizer






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											torch.manual_seed(0		)
											lowercase_	:     Optional[Any]													=       MCLIPConfig(
											    numDims=self.cross_attention_dim				,							transformerDimensions=self.text_embedder_hidden_size				,							hidden_size=self.text_embedder_hidden_size				,							intermediate_size=37				,							num_attention_heads=4				,							num_hidden_layers=5				,							vocab_size=10_05				,							)

											lowercase_	:     int													=       MultilingualCLIP(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       text_encoder.eval()

											return text_encoder






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											torch.manual_seed(0		)

											lowercase_	:     Any													=       {
											    '''in_channels''': 9,
											    # Out channels is double in channels because predicts mean and variance
											    '''out_channels''': 8,
											    '''addition_embed_type''': '''text_image''',
											    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
											    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
											    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
											    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
											    '''layers_per_block''': 1,
											    '''encoder_hid_dim''': self.text_embedder_hidden_size,
											    '''encoder_hid_dim_type''': '''text_image_proj''',
											    '''cross_attention_dim''': self.cross_attention_dim,
											    '''attention_head_dim''': 4,
											    '''resnet_time_scale_shift''': '''scale_shift''',
											    '''class_embed_type''': None,
											}

											lowercase_	:     int													=       UNetaDConditionModel(**__SCREAMING_SNAKE_CASE		)
											return model






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return {
											    "block_out_channels": [32, 64],
											    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
											    "in_channels": 3,
											    "latent_channels": 4,
											    "layers_per_block": 1,
											    "norm_num_groups": 8,
											    "norm_type": "spatial",
											    "num_vq_embeddings": 12,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											torch.manual_seed(0		)
											lowercase_	:     Tuple													=       VQModel(**self.dummy_movq_kwargs		)
											return model






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       self.dummy_text_encoder
											lowercase_	:     int													=       self.dummy_tokenizer
											lowercase_	:     Tuple													=       self.dummy_unet
											lowercase_	:     List[Any]													=       self.dummy_movq

											lowercase_	:     Dict													=       DDIMScheduler(
											    num_train_timesteps=10_00				,							beta_schedule='''linear'''				,							beta_start=0.00_085				,							beta_end=0.012				,							clip_sample=__SCREAMING_SNAKE_CASE				,							set_alpha_to_one=__SCREAMING_SNAKE_CASE				,							steps_offset=1				,							prediction_type='''epsilon'''				,							thresholding=__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     List[Any]													=       {
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''movq''': movq,
											}

											return components






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=0		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       floats_tensor((1, self.cross_attention_dim)				,							rng=random.Random(__SCREAMING_SNAKE_CASE		)		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       floats_tensor((1, self.cross_attention_dim)				,							rng=random.Random(seed + 1		)		).to(__SCREAMING_SNAKE_CASE		)
											# create init_image
											lowercase_	:     Optional[int]													=       floats_tensor((1, 3, 64, 64)				,							rng=random.Random(__SCREAMING_SNAKE_CASE		)		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       image.cpu().permute(0				,							2				,							3				,							1		)[0]
											lowercase_	:     List[Any]													=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE		)		).convert('''RGB'''		).resize((2_56, 2_56)		)
											# create mask
											lowercase_	:     Tuple													=       np.ones((64, 64)				,							dtype=np.floataa		)
											lowercase_	:     Optional[int]													=       0

											if str(__SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
																	lowercase_	:     str													=       torch.manual_seed(__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_	:     str													=       torch.Generator(device=__SCREAMING_SNAKE_CASE		).manual_seed(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       {
											    '''prompt''': '''horse''',
											    '''image''': init_image,
											    '''mask_image''': mask,
											    '''image_embeds''': image_embeds,
											    '''negative_image_embeds''': negative_image_embeds,
											    '''generator''': generator,
											    '''height''': 64,
											    '''width''': 64,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 4.0,
											    '''output_type''': '''np''',
											}
											return inputs






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       '''cpu'''

											lowercase_	:     Dict													=       self.get_dummy_components()

											lowercase_	:     List[str]													=       self.pipeline_class(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       pipe.to(__SCREAMING_SNAKE_CASE		)

											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_	:     List[Any]													=       output.images

											lowercase_	:     str													=       pipe(
											    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)				,							return_dict=__SCREAMING_SNAKE_CASE				,							)[0]

											lowercase_	:     Optional[Any]													=       image[0, -3:, -3:, -1]
											lowercase_	:     Tuple													=       image_from_tuple[0, -3:, -3:, -1]

											print(F'''image.shape {image.shape}'''		)

											assert image.shape == (1, 64, 64, 3)

											lowercase_	:     Optional[int]													=       np.array(
											    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]		)

											assert (
											    np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
											), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
											assert (
											    np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2
											), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






					def 	_snake_case    (   self		):


											"""simple docstring"""





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







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






					def 	_snake_case    (   self		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''		)

											lowercase_	:     Dict													=       load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
											lowercase_	:     str													=       np.ones((7_68, 7_68)				,							dtype=np.floataa		)
											lowercase_	:     List[Any]													=       0

											lowercase_	:     int													=       '''a hat'''

											lowercase_	:     int													=       KandinskyPriorPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-1-prior'''				,							torch_dtype=torch.floataa		)
											pipe_prior.to(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       KandinskyInpaintPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-1-inpaint'''				,							torch_dtype=torch.floataa		)
											lowercase_	:     Dict													=       pipeline.to(__SCREAMING_SNAKE_CASE		)
											pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       torch.Generator(device='''cpu'''		).manual_seed(0		)
											lowercase_						, lowercase_	:     Dict													=       pipe_prior(
											    __SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							num_inference_steps=5				,							negative_prompt=''''''				,							).to_tuple()

											lowercase_	:     Dict													=       pipeline(
											    __SCREAMING_SNAKE_CASE				,							image=__SCREAMING_SNAKE_CASE				,							mask_image=__SCREAMING_SNAKE_CASE				,							image_embeds=__SCREAMING_SNAKE_CASE				,							negative_image_embeds=__SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							num_inference_steps=1_00				,							height=7_68				,							width=7_68				,							output_type='''np'''				,							)

											lowercase_	:     Tuple													=       output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)





# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)