






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       __lowercase      (	lowercase_			,      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 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         return 32




     @property
     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         return 32




     @property
     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         return self.time_input_dim




     @property
     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         return self.time_input_dim * 4




     @property
     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         return 100




     @property
     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""				)
         return tokenizer




     @property
     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         torch.manual_seed(0				)
         __A         =	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=1_005 ,	)

         __A         =	MultilingualCLIP(UpperCamelCase_				)
         __A         =	text_encoder.eval()

         return text_encoder




     @property
     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         torch.manual_seed(0				)

         __A         =	{
             """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,
         }

         __A         =	UNetaDConditionModel(**UpperCamelCase_				)
         return model




     @property
     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """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 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         torch.manual_seed(0				)
         __A         =	VQModel(**self.dummy_movq_kwargs				)
         return model




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	self.dummy_text_encoder
         __A         =	self.dummy_tokenizer
         __A         =	self.dummy_unet
         __A         =	self.dummy_movq

         __A         =	DDIMScheduler(
             num_train_timesteps=1_000 ,	beta_schedule="""linear""" ,	beta_start=0.00085 ,	beta_end=0.012 ,	clip_sample=UpperCamelCase_ ,	set_alpha_to_one=UpperCamelCase_ ,	steps_offset=1 ,	prediction_type="""epsilon""" ,	thresholding=UpperCamelCase_ ,	)

         __A         =	{
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """unet""": unet,
             """scheduler""": scheduler,
             """movq""": movq,
         }

         return components




     def 		lowerCAmelCase_     (				self       :			str ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			str=0				):


         """simple docstring"""



         __A         =	floats_tensor((1, self.cross_attention_dim) ,	rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
         __A         =	floats_tensor((1, self.cross_attention_dim) ,	rng=random.Random(seed + 1				)				).to(UpperCamelCase_				)
         # create init_image
         __A         =	floats_tensor((1, 3, 64, 64) ,	rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
         __A         =	image.cpu().permute(0 ,	2 ,	3 ,	1				)[0]
         __A         =	Image.fromarray(np.uinta(UpperCamelCase_				)				).convert("""RGB"""				).resize((256, 256)				)
         # create mask
         __A         =	np.ones((64, 64) ,	dtype=np.floataa				)
         __A         =	0

         if str(UpperCamelCase_				).startswith("""mps"""				):
             __A         =	torch.manual_seed(UpperCamelCase_				)
         else:
             __A         =	torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
         __A         =	{
             """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 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         __A         =	"""cpu"""

         __A         =	self.get_dummy_components()

         __A         =	self.pipeline_class(**UpperCamelCase_				)
         __A         =	pipe.to(UpperCamelCase_				)

         pipe.set_progress_bar_config(disable=UpperCamelCase_				)

         __A         =	pipe(**self.get_dummy_inputs(UpperCamelCase_				)				)
         __A         =	output.images

         __A         =	pipe(
             **self.get_dummy_inputs(UpperCamelCase_				) ,	return_dict=UpperCamelCase_ ,	)[0]

         __A         =	image[0, -3:, -3:, -1]
         __A         =	image_from_tuple[0, -3:, -3:, -1]

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

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

         __A         =	np.array(
             [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]				)

         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 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



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


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







     '''simple docstring'''




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



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





     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         __A         =	load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""				)

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

         __A         =	"""a hat"""

         __A         =	KandinskyPriorPipeline.from_pretrained(
             """kandinsky-community/kandinsky-2-1-prior""" ,	torch_dtype=torch.floataa				)
         pipe_prior.to(UpperCamelCase_				)

         __A         =	KandinskyInpaintPipeline.from_pretrained(
             """kandinsky-community/kandinsky-2-1-inpaint""" ,	torch_dtype=torch.floataa				)
         __A         =	pipeline.to(UpperCamelCase_				)
         pipeline.set_progress_bar_config(disable=UpperCamelCase_				)

         __A         =	torch.Generator(device="""cpu"""				).manual_seed(0				)
         __A    ,	__A         =	pipe_prior(
             UpperCamelCase_ ,	generator=UpperCamelCase_ ,	num_inference_steps=5 ,	negative_prompt="""""" ,	).to_tuple()

         __A         =	pipeline(
             UpperCamelCase_ ,	image=UpperCamelCase_ ,	mask_image=UpperCamelCase_ ,	image_embeds=UpperCamelCase_ ,	negative_image_embeds=UpperCamelCase_ ,	generator=UpperCamelCase_ ,	num_inference_steps=100 ,	height=768 ,	width=768 ,	output_type="""np""" ,	)

         __A         =	output.images[0]

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

         assert_mean_pixel_difference(UpperCamelCase_ ,	UpperCamelCase_				)












from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				42


class       __lowercase      (	lowercase_			,      lowercase_  ):







     '''simple docstring'''




     @register_to_config
     def __init__(				self       :			List[Any] ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			Tuple[str] = ("DownEncoderBlock2D",) ,	UpperCamelCase_       :			Tuple[str] = ("UpDecoderBlock2D",) ,	UpperCamelCase_       :			Tuple[int] = (64,) ,	UpperCamelCase_       :			int = 1 ,	UpperCamelCase_       :			str = "silu" ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			int = 256 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			float = 0.18215 ,	UpperCamelCase_       :			str = "group" ,	):


         """simple docstring"""



         super().__init__()

         # pass init params to Encoder
         __A         =	Encoder(
             in_channels=UpperCamelCase_ ,	out_channels=UpperCamelCase_ ,	down_block_types=UpperCamelCase_ ,	block_out_channels=UpperCamelCase_ ,	layers_per_block=UpperCamelCase_ ,	act_fn=UpperCamelCase_ ,	norm_num_groups=UpperCamelCase_ ,	double_z=UpperCamelCase_ ,	)

         __A         =	vq_embed_dim if vq_embed_dim is not None else latent_channels

         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)
         __A         =	VectorQuantizer(UpperCamelCase_ ,	UpperCamelCase_ ,	beta=0.25 ,	remap=UpperCamelCase_ ,	sane_index_shape=UpperCamelCase_				)
         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)

         # pass init params to Decoder
         __A         =	Decoder(
             in_channels=UpperCamelCase_ ,	out_channels=UpperCamelCase_ ,	up_block_types=UpperCamelCase_ ,	block_out_channels=UpperCamelCase_ ,	layers_per_block=UpperCamelCase_ ,	act_fn=UpperCamelCase_ ,	norm_num_groups=UpperCamelCase_ ,	norm_type=UpperCamelCase_ ,	)




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	self.encoder(UpperCamelCase_				)
         __A         =	self.quant_conv(UpperCamelCase_				)

         if not return_dict:
             return (h,)

         return VQEncoderOutput(latents=UpperCamelCase_				)




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         if not force_not_quantize:
             __A    ,	__A    ,	__A         =	self.quantize(UpperCamelCase_				)
         else:
             __A         =	h
         __A         =	self.post_quant_conv(UpperCamelCase_				)
         __A         =	self.decoder(UpperCamelCase_ ,	quant if self.config.norm_type == """spatial""" else None				)

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	sample
         __A         =	self.encode(UpperCamelCase_				).latents
         __A         =	self.decode(UpperCamelCase_				).sample

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)




