



"""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 			A__   (  __SCREAMING_SNAKE_CASE  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	KandinskyInpaintPipeline
	SCREAMING_SNAKE_CASE          =	['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
	SCREAMING_SNAKE_CASE          =	[
	    'prompt',
	    'negative_prompt',
	    'image_embeds',
	    'negative_image_embeds',
	    'image',
	    'mask_image',
	]
	SCREAMING_SNAKE_CASE          =	[
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'negative_prompt',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	SCREAMING_SNAKE_CASE          =	False

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Optional[Any]:

				"""simple docstring"""

				return 32

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Optional[int]:

				"""simple docstring"""

				return 32

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Dict:

				"""simple docstring"""

				return self.time_input_dim

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Union[str, Any]:

				"""simple docstring"""

				return self.time_input_dim * 4

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       str:

				"""simple docstring"""

				return 100

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
				return tokenizer

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Optional[Any]:

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase      :  str												=       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=1005       ,			)

				__lowerCAmelCase      :  int												=       MultilingualCLIP(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Any												=       text_encoder.eval()

				return text_encoder

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       List[str]:

				"""simple docstring"""

				torch.manual_seed(0)

				__lowerCAmelCase      :  str												=       {
				    "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,
				}

				__lowerCAmelCase      :  Any												=       UNetaDConditionModel(**_SCREAMING_SNAKE_CASE)
				return model

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Optional[Any]:

				"""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 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Optional[Any]:

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase      :  List[str]												=       VQModel(**self.dummy_movq_kwargs)
				return model

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       self.dummy_text_encoder
				__lowerCAmelCase      :  Optional[Any]												=       self.dummy_tokenizer
				__lowerCAmelCase      :  Union[str, Any]												=       self.dummy_unet
				__lowerCAmelCase      :  Any												=       self.dummy_movq

				__lowerCAmelCase      :  int												=       DDIMScheduler(
				    num_train_timesteps=1000       ,			beta_schedule="linear"       ,			beta_start=0.0_0085       ,			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       ,			)

				__lowerCAmelCase      :  List[str]												=       {
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "unet": unet,
				    "scheduler": scheduler,
				    "movq": movq,
				}

				return components

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		str=0)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       floats_tensor((1, self.cross_attention_dim)       ,			rng=random.Random(_SCREAMING_SNAKE_CASE)).to(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Union[str, Any]												=       floats_tensor((1, self.cross_attention_dim)       ,			rng=random.Random(seed + 1)).to(_SCREAMING_SNAKE_CASE)
				# create init_image
				__lowerCAmelCase      :  Optional[Any]												=       floats_tensor((1, 3, 64, 64)       ,			rng=random.Random(_SCREAMING_SNAKE_CASE)).to(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Dict												=       image.cpu().permute(0       ,			2       ,			3       ,			1)[0]
				__lowerCAmelCase      :  List[str]												=       Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE)).convert("RGB").resize((256, 256))
				# create mask
				__lowerCAmelCase      :  List[str]												=       np.ones((64, 64)       ,			dtype=np.floataa)
				__lowerCAmelCase      :  Optional[Any]												=       0

				if str(_SCREAMING_SNAKE_CASE).startswith("mps"):
							__lowerCAmelCase      :  Tuple												=       torch.manual_seed(_SCREAMING_SNAKE_CASE)
				else:
							__lowerCAmelCase      :  str												=       torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       {
				    "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 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       "cpu"

				__lowerCAmelCase      :  int												=       self.get_dummy_components()

				__lowerCAmelCase      :  Optional[int]												=       self.pipeline_class(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       pipe.to(_SCREAMING_SNAKE_CASE)

				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[Any]												=       pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase      :  List[Any]												=       output.images

				__lowerCAmelCase      :  List[Any]												=       pipe(
				    **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE)       ,			return_dict=_SCREAMING_SNAKE_CASE       ,			)[0]

				__lowerCAmelCase      :  str												=       image[0, -3:, -3:, -1]
				__lowerCAmelCase      :  int												=       image_from_tuple[0, -3:, -3:, -1]

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

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

				__lowerCAmelCase      :  Optional[Any]												=       np.array(
				    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786])

				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 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Union[str, Any]:

				"""simple docstring"""

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






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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       List[str]:

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy")

				__lowerCAmelCase      :  Any												=       load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png")
				__lowerCAmelCase      :  List[Any]												=       np.ones((768, 768)       ,			dtype=np.floataa)
				__lowerCAmelCase      :  List[str]												=       0

				__lowerCAmelCase      :  Dict												=       "a hat"

				__lowerCAmelCase      :  Optional[Any]												=       KandinskyPriorPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-1-prior"       ,			torch_dtype=torch.floataa)
				pipe_prior.to(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Dict												=       KandinskyInpaintPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-1-inpaint"       ,			torch_dtype=torch.floataa)
				__lowerCAmelCase      :  Optional[int]												=       pipeline.to(_SCREAMING_SNAKE_CASE)
				pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[Any]												=       torch.Generator(device="cpu").manual_seed(0)
				__lowerCAmelCase						,	__lowerCAmelCase      :  Union[str, Any]												=       pipe_prior(
				    _SCREAMING_SNAKE_CASE       ,			generator=_SCREAMING_SNAKE_CASE       ,			num_inference_steps=5       ,			negative_prompt=""       ,			).to_tuple()

				__lowerCAmelCase      :  Optional[int]												=       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=100       ,			height=768       ,			width=768       ,			output_type="np"       ,			)

				__lowerCAmelCase      :  Union[str, Any]												=       output.images[0]

				assert image.shape == (768, 768, 3)

				assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)




"""simple docstring"""






from typing import TYPE_CHECKING

from ..utils import _LazyModule


__snake_case							:   Optional[int]      =    {
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
	from .config import (
	    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
	    OnnxConfig,
	    OnnxConfigWithPast,
	    OnnxSeqaSeqConfigWithPast,
	    PatchingSpec,
	)
	from .convert import export, validate_model_outputs
	from .features import FeaturesManager
	from .utils import ParameterFormat, compute_serialized_parameters_size

else:
	import sys

	__snake_case							:   Any      =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)